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DLL hijacking is a common technique in which attackers replace a library called by a legitimate process with a malicious one. It is used by both creators of mass-impact malware, like stealers and banking Trojans, and by APT and cybercrime groups behind targeted attacks. In recent years, the number of DLL hijacking attacks has grown significantly.

Trend in the number of DLL hijacking attacks. 2023 data is taken as 100% (download)

We have observed this technique and its variations, like DLL sideloading, in targeted attacks on organizations in Russia, Africa, South Korea, and other countries and regions. Lumma, one of 2025’s most active stealers, uses this method for distribution. Threat actors trying to profit from popular applications, such as DeepSeek, also resort to DLL hijacking.

Detecting a DLL substitution attack is not easy because the library executes within the trusted address space of a legitimate process. So, to a security solution, this activity may look like a trusted process. Directing excessive attention to trusted processes can compromise overall system performance, so you have to strike a delicate balance between a sufficient level of security and sufficient convenience.

Detecting DLL hijacking with a machine-learning model

Artificial intelligence can help where simple detection algorithms fall short. Kaspersky has been using machine learning for 20 years to identify malicious activity at various stages. The AI expertise center researches the capabilities of different models in threat detection, then trains and implements them. Our colleagues at the threat intelligence center approached us with a question of whether machine learning could be used to detect DLL hijacking, and more importantly, whether it would help improve detection accuracy.

Preparation

To determine if we could train a model to distinguish between malicious and legitimate library loads, we first needed to define a set of features highly indicative of DLL hijacking. We identified the following key features:

• Wrong library location. Many standard libraries reside in standard directories, while a malicious DLL is often found in an unusual location, such as the same folder as the executable that calls it.
• Wrong executable location. Attackers often save executables in non-standard paths, like temporary directories or user folders, instead of %Program Files%.
• Renamed executable. To avoid detection, attackers frequently save legitimate applications under arbitrary names.
• Library size has changed, and it is no longer signed.
• Modified library structure.

Training sample and labeling

For the training sample, we used dynamic library load data provided by our internal automatic processing systems, which handle millions of files every day, and anonymized telemetry, such as that voluntarily provided by Kaspersky users through Kaspersky Security Network.

The training sample was labeled in three iterations. Initially, we could not automatically pull event labeling from our analysts that indicated whether an event was a DLL hijacking attack. So, we used data from our databases containing only file reputation, and labeled the rest of the data manually. We labeled as DLL hijacking those library-call events where the process was definitively legitimate but the DLL was definitively malicious. However, this labeling was not enough because some processes, like “svchost”, are designed mainly to load various libraries. As a result, the model we trained on this data had a high rate of false positives and was not practical for real-world use.

In the next iteration, we additionally filtered malicious libraries by family, keeping only those which were known to exhibit DLL-hijacking behavior. The model trained on this refined data showed significantly better accuracy and essentially confirmed our hypothesis that we could use machine learning to detect this type of attacks.

At this stage, our training dataset had tens of millions of objects. This included about 20 million clean files and around 50,000 definitively malicious ones.

Status	Total	Unique files
Unknown	~ 18M	~ 6M
Malicious	~ 50K	~ 1,000
Clean	~ 20M	~ 250K

We then trained subsequent models on the results of their predecessors, which had been verified and further labeled by analysts. This process significantly increased the efficiency of our training.

Loading DLLs: what does normal look like?

So, we had a labeled sample with a large number of library loading events from various processes. How can we describe a “clean” library? Using a process name + library name combination does not account for renamed processes. Besides, a legitimate user, not just an attacker, can rename a process. If we used the process hash instead of the name, we would solve the renaming problem, but then every version of the same library would be treated as a separate library. We ultimately settled on using a library name + process signature combination. While this approach considers all identically named libraries from a single vendor as one, it generally produces a more or less realistic picture.

To describe safe library loading events, we used a set of counters that included information about the processes (the frequency of a specific process name for a file with a given hash, the frequency of a specific file path for a file with that hash, and so on), information about the libraries (the frequency of a specific path for that library, the percentage of legitimate launches, and so on), and event properties (that is, whether the library is in the same directory as the file that calls it).

The result was a system with multiple aggregates (sets of counters and keys) that could describe an input event. These aggregates can contain a single key (e.g., a DLL’s hash sum) or multiple keys (e.g., a process’s hash sum + process signature). Based on these aggregates, we can derive a set of features that describe the library loading event. The diagram below provides examples of how these features are derived:

Loading DLLs: how to describe hijacking

Certain feature combinations (dependencies) strongly indicate DLL hijacking. These can be simple dependencies. For some processes, the clean library they call always resides in a separate folder, while the malicious one is most often placed in the process folder.

Other dependencies can be more complex and require several conditions to be met. For example, a process renaming itself does not, on its own, indicate DLL hijacking. However, if the new name appears in the data stream for the first time, and the library is located on a non-standard path, it is highly likely to be malicious.

Model evolution

Within this project, we trained several generations of models. The primary goal of the first generation was to show that machine learning could at all be applied to detecting DLL hijacking. When training this model, we used the broadest possible interpretation of the term.

The model’s workflow was as simple as possible:

1. We took a data stream and extracted a frequency description for selected sets of keys.
2. We took the same data stream from a different time period and obtained a set of features.
3. We used type 1 labeling, where events in which a legitimate process loaded a malicious library from a specified set of families were marked as DLL hijacking.
4. We trained the model on the resulting data.

The second-generation model was trained on data that had been processed by the first-generation model and verified by analysts (labeling type 2). Consequently, the labeling was more precise than during the training of the first model. Additionally, we added more features to describe the library structure and slightly complicated the workflow for describing library loads.

Based on the results from this second-generation model, we were able to identify several common types of false positives. For example, the training sample included potentially unwanted applications. These can, in certain contexts, exhibit behavior similar to DLL hijacking, but they are not malicious and rarely belong to this attack type.

We fixed these errors in the third-generation model. First, with the help of analysts, we flagged the potentially unwanted applications in the training sample so the model would not detect them. Second, in this new version, we used an expanded labeling that included useful detections from both the first and second generations. Additionally, we expanded the feature description through one-hot encoding — a technique for converting categorical features into a binary format — for certain fields. Also, since the volume of events processed by the model increased over time, this version added normalization of all features based on the data flow size.

Comparison of the models

To evaluate the evolution of our models, we applied them to a test data set none of them had worked with before. The graph below shows the ratio of true positive to false positive verdicts for each model.

As the models evolved, the percentage of true positives grew. While the first-generation model achieved a relatively good result (0.6 or higher) only with a very high false positive rate (10^⁻³ or more), the second-generation model reached this at 10^⁻⁵. The third-generation model, at the same low false positive rate, produced 0.8 true positives, which is considered a good result.

Evaluating the models on the data stream at a fixed score shows that the absolute number of new events labeled as DLL Hijacking increased from one generation to the next. That said, evaluating the models by their false verdict rate also helps track progress: the first model has a fairly high error rate, while the second and third generations have significantly lower ones.

False positives rate among model outputs, July 2024 – August 2025 (download)

Practical application of the models

All three model generations are used in our internal systems to detect likely cases of DLL hijacking within telemetry data streams. We receive 6.5 million security events daily, linked to 800,000 unique files. Aggregates are built from this sample at a specified interval, enriched, and then fed into the models. The output data is then ranked by model and by the probability of DLL hijacking assigned to the event, and then sent to our analysts. For instance, if the third-generation model flags an event as DLL hijacking with high confidence, it should be investigated first, whereas a less definitive verdict from the first-generation model can be checked last.

Simultaneously, the models are tested on a separate data stream they have not seen before. This is done to assess their effectiveness over time, as a model’s detection performance can degrade. The graph below shows that the percentage of correct detections varies slightly over time, but on average, the models detect 70–80% of DLL hijacking cases.

DLL hijacking detection trends for all three models, October 2024 – September 2025 (download)

Additionally, we recently deployed a DLL hijacking detection model into the Kaspersky SIEM, but first we tested the model in the Kaspersky MDR service. During the pilot phase, the model helped to detect and prevent a number of DLL hijacking incidents in our clients’ systems. We have written a separate article about how the machine learning model for detecting targeted attacks involving DLL hijacking works in Kaspersky SIEM and the incidents it has identified.

Conclusion

Based on the training and application of the three generations of models, the experiment to detect DLL hijacking using machine learning was a success. We were able to develop a model that distinguishes events resembling DLL hijacking from other events, and refined it to a state suitable for practical use, not only in our internal systems but also in commercial products. Currently, the models operate in the cloud, scanning hundreds of thousands of unique files per month and detecting thousands of files used in DLL hijacking attacks each month. They regularly identify previously unknown variations of these attacks. The results from the models are sent to analysts who verify them and create new detection rules based on their findings.

Introduction

Our colleagues from the AI expertise center recently developed a machine-learning model that detects DLL-hijacking attacks. We then integrated this model into the Kaspersky Unified Monitoring and Analysis Platform SIEM system. In a separate article, our colleagues shared how the model had been created and what success they had achieved in lab environments. Here, we focus on how it operates within Kaspersky SIEM, the preparation steps taken before its release, and some real-world incidents it has already helped us uncover.

How the model works in Kaspersky SIEM

The model’s operation generally boils down to a step-by-step check of all DLL libraries loaded by processes in the system, followed by validation in the Kaspersky Security Network (KSN) cloud. This approach allows local attributes (path, process name, and file hashes) to be combined with a global knowledge base and behavioral indicators, which significantly improves detection quality and reduces the probability of false positives.

The model can run in one of two modes: on a correlator or on a collector. A correlator is a SIEM component that performs event analysis and correlation based on predefined rules or algorithms. If detection is configured on a correlator, the model checks events that have already triggered a rule. This reduces the volume of KSN queries and the model’s response time.

This is how it looks:

A collector is a software or hardware component of a SIEM platform that collects and normalizes events from various sources, and then delivers these events to the platform’s core. If detection is configured on a collector, the model processes all events associated with various processes loading libraries, provided these events meet the following conditions:

• The path to the process file is known.
• The path to the library is known.
• The hashes of the file and the library are available.

This method consumes more resources, and the model’s response takes longer than it does on a correlator. However, it can be useful for retrospective threat hunting because it allows you to check all events logged by Kaspersky SIEM. The model’s workflow on a collector looks like this:

It is important to note that the model is not limited to a binary “malicious/non-malicious” assessment; it ranks its responses by confidence level. This allows it to be used as a flexible tool in SOC practice. Examples of possible verdicts:

• 0: data is being processed.
• 1: maliciousness not confirmed. This means the model currently does not consider the library malicious.
• 2: suspicious library.
• 3: maliciousness confirmed.

A Kaspersky SIEM rule for detecting DLL hijacking would look like this:

N.KL_AI_DLLHijackingCheckResult > 1

Embedding the model into the Kaspersky SIEM correlator automates the process of finding DLL-hijacking attacks, making it possible to detect them at scale without having to manually analyze hundreds or thousands of loaded libraries. Furthermore, when combined with correlation rules and telemetry sources, the model can be used not just as a standalone module but as part of a comprehensive defense against infrastructure attacks.

Incidents detected during the pilot testing of the model in the MDR service

Before being released, the model (as part of the Kaspersky SIEM platform) was tested in the MDR service, where it was trained to identify attacks on large datasets supplied by our telemetry. This step was necessary to ensure that detection works not only in lab settings but also in real client infrastructures.

During the pilot testing, we verified the model’s resilience to false positives and its ability to correctly classify behavior even in non-typical DLL-loading scenarios. As a result, several real-world incidents were successfully detected where attackers used one type of DLL hijacking — the DLL Sideloading technique — to gain persistence and execute their code in the system.

Let us take a closer look at the three most interesting of these.

Incident 1. ToddyCat trying to launch Cobalt Strike disguised as a system library

In one incident, the attackers successfully leveraged the vulnerability CVE-2021-27076 to exploit a SharePoint service that used IIS as a web server. They ran the following command:

c:\windows\system32\inetsrv\w3wp.exe -ap "SharePoint - 80" -v "v4.0" -l "webengine4.dll" -a \\.\pipe\iisipmd32ded38-e45b-423f-804d-34471928538b -h "C:\inetpub\temp\apppools\SharePoint - 80\SharePoint - 80.config" -w "" -m 0

After the exploitation, the IIS process created files that were later used to run malicious code via the DLL sideloading technique (T1574.001 Hijack Execution Flow: DLL):

C:\ProgramData\SystemSettings.exe
C:\ProgramData\SystemSettings.dll

SystemSettings.dll is the name of a library associated with the Windows Settings application (SystemSettings.exe). The original library contains code and data that the Settings application uses to manage and configure various system parameters. However, the library created by the attackers has malicious functionality and is only pretending to be a system library.

Later, to establish persistence in the system and launch a DLL sideloading attack, a scheduled task was created, disguised as a Microsoft Edge browser update. It launches a SystemSettings.exe file, which is located in the same directory as the malicious library:

Schtasks  /create  /ru "SYSTEM" /tn "\Microsoft\Windows\Edge\Edgeupdates" /sc DAILY /tr "C:\ProgramData\SystemSettings.exe" /F

The task is set to run daily.

When the SystemSettings.exe process is launched, it loads the malicious DLL. As this happened, the process and library data were sent to our model for analysis and detection of a potential attack.

The resulting data helped our analysts highlight a suspicious DLL and analyze it in detail. The library was found to be a Cobalt Strike implant. After loading it, the SystemSettings.exe process attempted to connect to the attackers’ command-and-control server.

DNS query: connect-microsoft[.]com
DNS query type: AAAA
DNS response: ::ffff:8.219.1[.]155;
8.219.1[.]155:8443

After establishing a connection, the attackers began host reconnaissance to gather various data to develop their attack.

C:\ProgramData\SystemSettings.exe
whoami /priv
hostname
reg query HKLM\SOFTWARE\Microsoft\Cryptography /v MachineGuid
powershell -c $psversiontable
dotnet --version
systeminfo
reg query "HKEY_LOCAL_MACHINE\SOFTWARE\VMware, Inc.\VMware Drivers"
cmdkey /list
REG query "HKLM\SYSTEM\CurrentControlSet\Control\Terminal Server\WinStations\RDP-Tcp" /v PortNumber
reg query "HKEY_CURRENT_USER\Software\Microsoft\Terminal Server Client\Servers
netsh wlan show profiles
netsh wlan show interfaces
set
net localgroup administrators
net user
net user administrator
ipconfig /all
net config workstation
net view
arp -a
route print
netstat -ano
tasklist
schtasks /query /fo LIST /v
net start
net share
net use
netsh firewall show config
netsh firewall show state
net view /domain
net time /domain
net group "domain admins" /domain
net localgroup administrators /domain
net group "domain controllers" /domain
net accounts /domain
nltest / domain_trusts
reg query HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows\CurrentVersion\Run
reg query HKEY_CURRENT_USER\Software\Microsoft\Windows\CurrentVersion\RunOnce
reg query HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Policies\Explorer\Run
reg query HKEY_LOCAL_MACHINE\Software\Wow6432Node\Microsoft\Windows\CurrentVersion\Run
reg query HKEY_CURRENT_USER\Software\Wow6432Node\Microsoft\Windows\CurrentVersion\RunOnce

Based on the attackers’ TTPs, such as loading Cobalt Strike as a DLL, using the DLL sideloading technique (1, 2), and exploiting SharePoint, we can say with a high degree of confidence that the ToddyCat APT group was behind the attack. Thanks to the prompt response of our model, we were able to respond in time and block this activity, preventing the attackers from causing damage to the organization.

Incident 2. Infostealer masquerading as a policy manager

Another example was discovered by the model after a client was connected to MDR monitoring: a legitimate system file located in an application folder attempted to load a suspicious library that was stored next to it.

C:\Program Files\Chiniks\SettingSyncHost.exe
C:\Program Files\Chiniks\policymanager.dll E83F331BD1EC115524EBFF7043795BBE

The SettingSyncHost.exe file is a system host process for synchronizing settings between one user’s different devices. Its 32-bit and 64-bit versions are usually located in C:\Windows\System32\ and C:\Windows\SysWOW64\, respectively. In this incident, the file location differed from the normal one.

Analysis of the library file loaded by this process showed that it was malware designed to steal information from browsers.

The file directly accesses browser files that contain user data.

C:\Users\<user>\AppData\Local\Google\Chrome\User Data\Local State

The library file is on the list of files used for DLL hijacking, as published in the HijackLibs project. The project contains a list of common processes and libraries employed in DLL-hijacking attacks, which can be used to detect these attacks.

Incident 3. Malicious loader posing as a security solution

Another incident discovered by our model occurred when a user connected a removable USB drive:

The connected drive’s directory contained hidden folders with an identically named shortcut for each of them. The shortcuts had icons typically used for folders. Since file extensions were not shown by default on the drive, the user might have mistaken the shortcut for a folder and launched it. In turn, the shortcut opened the corresponding hidden folder and ran an executable file using the following command:

"%comspec%" /q /c "RECYCLER.BIN\1\CEFHelper.exe [$DIGITS] [$DIGITS]"

CEFHelper.exe is a legitimate Avast Antivirus executable that, through DLL sideloading, loaded the wsc.dll library, which is a malicious loader.

The loader opens a file named AvastAuth.dat, which contains an encrypted backdoor. The library reads the data from the file into memory, decrypts it, and executes it. After this, the backdoor attempts to connect to a remote command-and-control server.

The library file, which contains the malicious loader, is on the list of known libraries used for DLL sideloading, as presented on the HijackLibs project website.

Conclusion

Integrating the model into the product provided the means of early and accurate detection of DLL-hijacking attempts which previously might have gone unnoticed. Even during the pilot testing, the model proved its effectiveness by identifying several incidents using this technique. Going forward, its accuracy will only increase as data accumulates and algorithms are updated in KSN, making this mechanism a reliable element of proactive protection for corporate systems.

IoC

Legitimate files used for DLL hijacking
E0E092D4EFC15F25FD9C0923C52C33D6 loads SystemSettings.dll
09CD396C8F4B4989A83ED7A1F33F5503 loads policymanager.dll
A72036F635CECF0DCB1E9C6F49A8FA5B loads wsc.dll

Malicious files
EA2882B05F8C11A285426F90859F23C6   SystemSettings.dll
E83F331BD1EC115524EBFF7043795BBE   policymanager.dll
831252E7FA9BD6FA174715647EBCE516   wsc.dll

Paths
C:\ProgramData\SystemSettings.exe
C:\ProgramData\SystemSettings.dll
C:\Program Files\Chiniks\SettingSyncHost.exe
C:\Program Files\Chiniks\policymanager.dll
D:\RECYCLER.BIN\1\CEFHelper.exe
D:\RECYCLER.BIN\1\wsc.dll

If a system is popular with users, you can bet it’s 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

Let’s start by outlining the set of security mechanisms in macOS with a brief description of each:

1. Keychain – default password manager
2. TCC – application access control
3. SIP – ensures the integrity of information in directories and processes vulnerable to attacks
4. File Quarantine – protection against launching suspicious files downloaded from the internet
5. Gatekeeper – ensures only trusted applications are allowed to run
6. XProtect – signature-based anti-malware protection in macOS
7. XProtect Remediator – 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 victim’s device. What’s 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:

python -m chainbreaker -pa test_keychain.keychain -o output

As mentioned above, the security utility can be used for command line management, specifically the following commands:

• security list-keychains – displays all available Keychain files

• security dump-keychain -a -d – dumps all Keychain files

• security dump-keychain ~/Library/Keychains/login.keychain-db – 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.

Sigma:

title: Keychain access
description: This rule detects dumping of keychain
tags:
    - attack.credential-access
    - attack.t1555.001
logsource:
    category: process_creation
    product: macos
detection:
    selection:
		cmdline: security
		cmdline: 
			-list-keychains
			-dump-keychain
    condition: selection
falsepositives:
    - Unknow
level: medium

SIP

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 – 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.

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.

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).

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.

A potential vector for bypassing this mechanism is TCC Clickjacking – 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.

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.

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 – flag for further Gatekeeper actions
• 689cb865 – timestamp in hexadecimal format (Mac Absolute Time)
• Safari – browser used to download the file
• 66EA7FA5-1F9E-4779-A5B5-9CCA2A4A98F5 – UUID attached to this file. This is needed to database a record of the file

The information returned by this command is stored in a database located at ~/Library/Preferences/com.apple.LaunchServices.QuarantineEventsV2, where it can be audited.

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.

It is also possible to remove the attribute manually using the xattr -d com.apple.quarantine <filename> command.

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 user’s knowledge.

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.

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>

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 → 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.

Let’s 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.

If we run the program from the context menu (right-click → Open), we see the following.

Attackers with local access and administrator rights can disable Gatekeeper using the spctl –master disable or --global-disable command.

To detect this activity, you need to monitor execution of the spctl command with parameters –master 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.

Sigma:

title: Gatekeeper disable
description: This rule detects disabling of Gatekeeper 
tags:
    - attack.defense-evasion
    - attack.t1562.001
logsource:
    category: process_creation
    product: macos
detection:
    selection:
	cmdline: spctl 
	cmdline: 
		- '--master-disable'
		- '--global-disable'
    condition: selection

Takeaways

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.