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Digital Forensics: Analyzing a USB Flash Drive for Malicious Content

18 September 2025 at 10:58

Welcome back, aspiring forensic investigators!

Today, we continue our exploration of digital forensics with a hands-on case study. So far, we have laid the groundwork for understanding forensic principles, but now it’s time to put theory into practice. Today we will analyze a malicious USB drive, a common vector for delivering payloads, and walk through how forensic analysts dissect its components to uncover potential threats.

usb sticks on the ground

USB drives remain a popular attack vector because they exploit human curiosity and trust. Often, the most challenging stage of the cyber kill chain is delivering the payload to the target. Many users are cautious about downloading unknown files from the internet, but physical media like USB drives can bypass that hesitation. Who wouldn’t be happy with a free USB? As illustrated in Mr. Robot, an attacker may drop USB drives in a public place, hoping someone curious will pick them up and plug them in. Once connected, the payload can execute automatically or rely on the victim opening a document. While this is a simple strategy, curiosity remains a powerful motivator, which hackers exploit consistently.Β 

(Read more: https://hackers-arise.com/mr-robot-hacks-how-elliot-hacked-the-prison/)

Forensic investigation of such incidents is important. When a USB drive is plugged into a system, changes may happen immediately, sometimes leaving traces that are difficult to detect or revert. Understanding the exact mechanics of these changes helps us reconstruct events, assess damage, and develop mitigation strategies. Today, we’ll see how an autorun-enabled USB and a malicious PDF can compromise a system, and how analysts dissect such threats.

Analyzing USB Files

Our investigation begins by extracting the files from the USB drive. While there are multiple methods for acquiring data from a device in digital forensics, this case uses a straightforward approach for demonstration purposes.

unzipping USB files
viewing USB files

After extraction, we identify two key files: a PDF document and an autorun configuration file. Let’s learn something about each.

Autorun

The autorun file represents a legacy technique, often used as a fallback mechanism for older systems. Windows versions prior to Windows 7 frequently executed instructions embedded in autorun files automatically. In this case, the file defines which document to open and even sets an icon to make the file appear legitimate.

analyzing autorun.inf from USB

On modern Windows systems, autorun functionality is disabled by default, but the attacker likely counted on human curiosity to ensure the document would still be opened. Although outdated, this method remains effective in environments where older systems persist, which are common in government and corporate networks with strict financial or operational constraints. Even today, autorun files can serve as a backup plan to increase the likelihood of infection.

PDF Analysis

Next, we analyze the PDF. Before opening the file, it is important to verify that it is indeed a PDF and not a disguised executable. Magic bytes, which are unique identifiers at the beginning of a file, help us confirm its type. Although these bytes can be manipulated, altering them may break the functionality of the file. This technique is often seen in webshell uploads, where attackers attempt to bypass file type filters.

To inspect the magic bytes:

bash$ > xxd README.pdf | head

analyzing a PDF

In this case, the file is a valid PDF. Opening it appears benign initially, allowing us to read its contents without immediate suspicion. However, a forensic investigation cannot stop at surface-level observation. We will proceed with checking the MD5 hash of it against malware databases:

bash$ > md5sum README.pdf

generating a md5 hash of a pdf file
running the hash against malware databases in virus total

VirusTotal and similar services confirm the file contains malware. At this stage, a non-specialist might consider the investigation complete, but forensic analysts need a deeper understanding of the file’s behavior once executed.

Dynamic Behavior Analysis

Forensic laboratories provide tools to safely observe malware behavior. Platforms like AnyRun allow analysts to simulate the malware execution and capture detailed reports, including screenshots, spawned processes, and network activity.

analyzing the behavior of the malware by viewing process and service actions

Key observations in this case include multiple instances of msiexec.exe. While this could indicate an Adobe Acrobat update or repair routine, we need to analyze this more thoroughly. Malicious PDFs often exploit vulnerabilities in Acrobat to execute additional code.

viewing the process tree of the malware

Next we go to AnyRun and get the behavior graph. We can see child processes such as rdrcef.exe spawned immediately upon opening.

viewing command line arguments of the malicious PDF

Hybrid Analysis reveals that the PDF contains an embedded JavaScript stream utilizing this.exportDataObject(...). This function allows the document to silently extract and save embedded files. The file also defines a /Launch action referencing Windows command execution and system paths, including cmd /C and environment variables such as %HOMEDRIVE%%HOMEPATH%.

The script attempts to navigate into multiple user directories in both English and Spanish, such as Desktop, My Documents, Documents, Escritorio, Mis Documentos, before executing the payload README.pdf. Such malware could be designed to operate across North and South American systems. At this stage the malware acts as a dropper duplicating itself.

Summary

In our case study we demonstrated how effective USB drives can be to deliver malware. Despite modern mitigations such as disabled autorun functionality, human behavior, especially curiosity and greed remain a key vulnerability.Β  Attackers adapt by combining old strategies with new mechanisms such as embedded JavaScript and environment-specific paths. Dynamic behavior analysis, supported by platforms like AnyRun, allows us to visualize these threats in action and understand their system-level impact.Β 

To stay safe, be careful with unknown USB drives and view unfamiliar PDF files in a browser or in the cloud with JavaScript blocked in settings. Dynamic behavior analysis from platforms like AnyRun, VirusTotal and Hybrid Analysis helps us to visualize these threats in action and understand their system-level impact.

If you need forensic assistance, we offer professional services to help investigate and mitigate incidents. Additionally, we provide classes on digital forensics for those looking to expand their skills and understanding in this field.

The post Digital Forensics: Analyzing a USB Flash Drive for Malicious Content first appeared on Hackers Arise.

Tai-e - An Easy-To-Learn/Use Static Analysis Framework For Java

By: Unknown
21 January 2023 at 06:30


Tai-e

What is Tai-e?

Tai-e (Chinese: ε€ͺ阿; pronunciation: [ˈtaΙͺΙ™:]) is a new static analysis framework for Java (please see our technical report for details), which features arguably the "best" designs from both the novel ones we proposed and those of classic frameworks such as Soot, WALA, Doop, and SpotBugs. Tai-e is easy-to-learn, easy-to-use, efficient, and highly extensible, allowing you to easily develop new analyses on top of it.

Currently, Tai-e provides the following major analysis components (and more analyses are on the way):

  • Powerful pointer analysis framework
    • On-the-fly call graph construction
    • Various classic and advanced techniques of heap abstraction and context sensitivity for pointer analysis
    • Extensible analysis plugin system (allows to conveniently develop and add new analyses that interact with pointer analysis)
  • Various fundamental/client/utility analyses
    • Fundamental analyses, e.g., reflection analysis and exception analysis
    • Modern language feature analyses, e.g., lambda and method reference analysis, and invokedynamic analysis
    • Clients, e.g., configurable taint analysis (allowing to configure sources, sinks and taint transfers)
    • Utility tools like analysis timer, constraint checker (for debugging), and various graph dumpers
  • Control/Data-flow analysis framework
    • Control-flow graph construction
    • Classic data-flow analyses, e.g., live variable analysis, constant propagation
    • Your data-flow analyses
  • SpotBugs-like bug detection system
    • Bug detectors, e.g., null pointer detector, incorrect clone() detector
    • Your bug detectors

Tai-e is developed in Java, and it can run on major operating systems including Windows, Linux, and macOS.


How to Obtain Runnable Jar of Tai-e?

The simplest way is to download it from GitHub Releases.

Alternatively, you might build the latest Tai-e yourself from the source code. This can be simply done via Gradle (be sure that Java 17 (or higher version) is available on your system). You just need to run command gradlew fatJar, and then the runnable jar will be generated in tai-e/build/, which includes Tai-e and all its dependencies.

Documentation

We are hosting the documentation of Tai-e on the GitHub wiki, where you could find more information about Tai-e such as Setup in IntelliJ IDEA , Command-Line Options , and Development of New Analysis .

Tai-e Assignments

In addition, we have developed an educational version of Tai-e where eight programming assignments are carefully designed for systematically training learners to implement various static analysis techniques to analyze real Java programs. The educational version shares a large amount of code with Tai-e, thus doing the assignments would be a good way to get familiar with Tai-e.



Fuzzable - Framework For Automating Fuzzable Target Discovery With Static Analysis

By: Unknown
14 January 2023 at 06:30


Framework for Automating Fuzzable Target Discovery with Static Analysis.

Introduction

Vulnerability researchers conducting security assessments on software will often harness the capabilities of coverage-guided fuzzing through powerful tools like AFL++ and libFuzzer. This is important as it automates the bughunting process and reveals exploitable conditions in targets quickly. However, when encountering large and complex codebases or closed-source binaries, researchers have to painstakingly dedicate time to manually audit and reverse engineer them to identify functions where fuzzing-based exploration can be useful.

Fuzzable is a framework that integrates both with C/C++ source code and binaries to assist vulnerability researchers in identifying function targets that are viable for fuzzing. This is done by applying several static analysis-based heuristics to pinpoint risky behaviors in the software and the functions that executes them. Researchers can then utilize the framework to generate basic harness templates, which can then be used to hunt for vulnerabilities, or to be integrated as part of a continuous fuzzing pipeline, such as Google's oss-fuzz project.

In addition to running as a standalone tool, Fuzzable is also integrated as a plugin for the Binary Ninja disassembler, with support for other disassembly backends being developed.

Check out the original blog post detailing the tool here, which highlights the technical specifications of the static analysis heuristics and how this tool came about. This tool is also featured at Black Hat Arsenal USA 2022.


Features

  • Supports analyzing binaries (with Angr and Binary Ninja) and source code artifacts (with tree-sitter).
  • Run static analysis both as a standalone CLI tool or a Binary Ninja plugin.
  • Harness generation to ramp up on creating fuzzing campaigns quickly.

Installation

Some binary targets may require some sanitizing (ie. signature matching, or identifying functions from inlining), and therefore fuzzable primarily uses Binary Ninja as a disassembly backend because of it's ability to effectively solve these problems. Therefore, it can be utilized both as a standalone tool and plugin.

Since Binary Ninja isn't accessible to all and there may be a demand to utilize for security assessments and potentially scaling up in the cloud, an angr fallback backend is also supported. I anticipate to incorporate other disassemblers down the road as well (priority: Ghidra).

Command Line (Standalone)

If you have Binary Ninja Commercial, be sure to install the API for standalone headless usage:

$ python3 /Applications/Binary\ Ninja.app/Contents/Resources/scripts/install_api.py

Install with pip:

$ pip install fuzzable

Manual/Development Build

We use poetry for dependency management and building. To do a manual build, clone the repository with the third-party modules:

$ git clone --recursive https://github.com/ex0dus-0x/fuzzable

To install manually:

$ cd fuzzable/

# without poetry
$ pip install .

# with poetry
$ poetry install

# with poetry for a development virtualenv
$ poetry shell

You can now analyze binaries and/or source code with the tool!

# analyzing a single shared object library binary
$ fuzzable analyze examples/binaries/libbasic.so

# analyzing a single C source file
$ fuzzable analyze examples/source/libbasic.c

# analyzing a workspace with multiple C/C++ files and headers
$ fuzzable analyze examples/source/source_bundle/

Binary Ninja Plugin

fuzzable can be easily installed through the Binary Ninja plugin marketplace by going to Binary Ninja > Manage Plugins and searching for it. Here is an example of the fuzzable plugin running, accuracy identifying targets for fuzzing and further vulnerability assessment:

Usage

fuzzable comes with various options to help better tune your analysis. More will be supported in future plans and any feature requests made.

Static Analysis Heuristics

To determine fuzzability, fuzzable utilize several heuristics to determine which targets are the most viable to target for dynamic analysis. These heuristics are all weighted differently using the scikit-criteria library, which utilizes multi-criteria decision analysis to determine the best candidates. These metrics and are there weights can be seen here:

Heuristic Description Weight
Fuzz Friendly Name Symbol name implies behavior that ingests file/buffer input 0.3
Risky Sinks Arguments that flow into risky calls (ie memcpy) 0.3
Natural Loops Number of loops detected with the dominance frontier 0.05
Cyclomatic Complexity Complexity of function target based on edges + nodes 0.05
Coverage Depth Number of callees the target traverses into 0.3

As mentioned, check out the technical blog post for a more in-depth look into why and how these metrics are utilized.

Many metrics were largely inspired by Vincenzo Iozzo's original work in 0-knowledge fuzzing.

Every targets you want to analyze is diverse, and fuzzable will not be able to account for every edge case behavior in the program target. Thus, it may be important during analysis to tune these weights appropriately to see if different results make more sense for your use case. To tune these weights in the CLI, simply specify the --score-weights argument:

$ fuzzable analyze <TARGET> --score-weights=0.2,0.2,0.2,0.2,0.2

Analysis Filtering

By default, fuzzable will filter out function targets based on the following criteria:

  • Top-level entry calls - functions that aren't called by any other calls in the target. These are ideal entry points that have potentially very high coverage.
  • Static calls - (source only) functions that are static and aren't exposed through headers.
  • Imports - (binary only) other library dependencies being used by the target's implementations.

To see calls that got filtered out by fuzzable, set the --list_ignored flag:

$ fuzzable analyze --list-ignored <TARGET>

In Binary Ninja, you can turn this setting in Settings > Fuzzable > List Ignored Calls.

In the case that fuzzable falsely filters out important calls that should be analyzed, it is recommended to use --include-* arguments to include them during the run:

# include ALL non top-level calls that were filtered out
$ fuzzable analyze --include-nontop <TARGET>

# include specific symbols that were filtered out
$ fuzzable analyze --include-sym <SYM> <TARGET>

In Binary Ninja, this is supported through Settings > Fuzzable > Include non-top level calls and Symbols to Exclude.

Harness Generation

Now that you have found your ideal candidates to fuzz, fuzzable will also help you generate fuzzing harnesses that are (almost) ready to instrument and compile for use with either a file-based fuzzer (ie. AFL++, Honggfuzz) or in-memory fuzzer (libFuzzer). To do so in the CLI:

If this target is a source codebase, the generic source template will be used.

If the target is a binary, the generic black-box template will be used, which ideally can be used with a fuzzing emulation mode like AFL-QEMU. A copy of the binary will also be created as a shared object if the symbol isn't exported directly to be dlopened using LIEF.

At the moment, this feature is quite rudimentary, as it simply will create a standalone C++ harness populated with the appropriate parameters, and will not auto-generate code that is needed for any runtime behaviors (ie. instantiating and freeing structures). However, the templates created for fuzzable should get still get you running quickly. Here are some ambitious features I would like to implement down the road:

  • Full harness synthesis - harnesses will work directly with absolutely no manual changes needed.
  • Synthesis from potential unit tests using the DeepState framework (Source only).
  • Immediate deployment to a managed continuous fuzzing fleet.

Exporting Reports

fuzzable supports generating reports in various formats. The current ones that are supported are JSON, CSV and Markdown. This can be useful if you are utilizing this as part of automation where you would like to ingest the output in a serializable format.

In the CLI, simply pass the --export argument with a filename with the appropriate extension:

$ fuzzable analyze --export=report.json <TARGET>

In Binary Ninja, go to Plugins > Fuzzable > Export Fuzzability Report > ... and select the format you want to export to and the path you want to write it to.

Contributing

This tool will be continuously developed, and any help from external mantainers are appreciated!

  • Create an issue for feature requests or bugs that you have come across.
  • Submit a pull request for fixes and enhancements that you would like to see contributed to this tool.

License

Fuzzable is licensed under the MIT License.



Tai-e - An Easy-To-Learn/Use Static Analysis Framework For Java

By: Unknown
21 January 2023 at 06:30


Tai-e

What is Tai-e?

Tai-e (Chinese: ε€ͺ阿; pronunciation: [ˈtaΙͺΙ™:]) is a new static analysis framework for Java (please see our technical report for details), which features arguably the "best" designs from both the novel ones we proposed and those of classic frameworks such as Soot, WALA, Doop, and SpotBugs. Tai-e is easy-to-learn, easy-to-use, efficient, and highly extensible, allowing you to easily develop new analyses on top of it.

Currently, Tai-e provides the following major analysis components (and more analyses are on the way):

  • Powerful pointer analysis framework
    • On-the-fly call graph construction
    • Various classic and advanced techniques of heap abstraction and context sensitivity for pointer analysis
    • Extensible analysis plugin system (allows to conveniently develop and add new analyses that interact with pointer analysis)
  • Various fundamental/client/utility analyses
    • Fundamental analyses, e.g., reflection analysis and exception analysis
    • Modern language feature analyses, e.g., lambda and method reference analysis, and invokedynamic analysis
    • Clients, e.g., configurable taint analysis (allowing to configure sources, sinks and taint transfers)
    • Utility tools like analysis timer, constraint checker (for debugging), and various graph dumpers
  • Control/Data-flow analysis framework
    • Control-flow graph construction
    • Classic data-flow analyses, e.g., live variable analysis, constant propagation
    • Your data-flow analyses
  • SpotBugs-like bug detection system
    • Bug detectors, e.g., null pointer detector, incorrect clone() detector
    • Your bug detectors

Tai-e is developed in Java, and it can run on major operating systems including Windows, Linux, and macOS.


How to Obtain Runnable Jar of Tai-e?

The simplest way is to download it from GitHub Releases.

Alternatively, you might build the latest Tai-e yourself from the source code. This can be simply done via Gradle (be sure that Java 17 (or higher version) is available on your system). You just need to run command gradlew fatJar, and then the runnable jar will be generated in tai-e/build/, which includes Tai-e and all its dependencies.

Documentation

We are hosting the documentation of Tai-e on the GitHub wiki, where you could find more information about Tai-e such as Setup in IntelliJ IDEA , Command-Line Options , and Development of New Analysis .

Tai-e Assignments

In addition, we have developed an educational version of Tai-e where eight programming assignments are carefully designed for systematically training learners to implement various static analysis techniques to analyze real Java programs. The educational version shares a large amount of code with Tai-e, thus doing the assignments would be a good way to get familiar with Tai-e.



Fuzzable - Framework For Automating Fuzzable Target Discovery With Static Analysis

By: Unknown
14 January 2023 at 06:30


Framework for Automating Fuzzable Target Discovery with Static Analysis.

Introduction

Vulnerability researchers conducting security assessments on software will often harness the capabilities of coverage-guided fuzzing through powerful tools like AFL++ and libFuzzer. This is important as it automates the bughunting process and reveals exploitable conditions in targets quickly. However, when encountering large and complex codebases or closed-source binaries, researchers have to painstakingly dedicate time to manually audit and reverse engineer them to identify functions where fuzzing-based exploration can be useful.

Fuzzable is a framework that integrates both with C/C++ source code and binaries to assist vulnerability researchers in identifying function targets that are viable for fuzzing. This is done by applying several static analysis-based heuristics to pinpoint risky behaviors in the software and the functions that executes them. Researchers can then utilize the framework to generate basic harness templates, which can then be used to hunt for vulnerabilities, or to be integrated as part of a continuous fuzzing pipeline, such as Google's oss-fuzz project.

In addition to running as a standalone tool, Fuzzable is also integrated as a plugin for the Binary Ninja disassembler, with support for other disassembly backends being developed.

Check out the original blog post detailing the tool here, which highlights the technical specifications of the static analysis heuristics and how this tool came about. This tool is also featured at Black Hat Arsenal USA 2022.


Features

  • Supports analyzing binaries (with Angr and Binary Ninja) and source code artifacts (with tree-sitter).
  • Run static analysis both as a standalone CLI tool or a Binary Ninja plugin.
  • Harness generation to ramp up on creating fuzzing campaigns quickly.

Installation

Some binary targets may require some sanitizing (ie. signature matching, or identifying functions from inlining), and therefore fuzzable primarily uses Binary Ninja as a disassembly backend because of it's ability to effectively solve these problems. Therefore, it can be utilized both as a standalone tool and plugin.

Since Binary Ninja isn't accessible to all and there may be a demand to utilize for security assessments and potentially scaling up in the cloud, an angr fallback backend is also supported. I anticipate to incorporate other disassemblers down the road as well (priority: Ghidra).

Command Line (Standalone)

If you have Binary Ninja Commercial, be sure to install the API for standalone headless usage:

$ python3 /Applications/Binary\ Ninja.app/Contents/Resources/scripts/install_api.py

Install with pip:

$ pip install fuzzable

Manual/Development Build

We use poetry for dependency management and building. To do a manual build, clone the repository with the third-party modules:

$ git clone --recursive https://github.com/ex0dus-0x/fuzzable

To install manually:

$ cd fuzzable/

# without poetry
$ pip install .

# with poetry
$ poetry install

# with poetry for a development virtualenv
$ poetry shell

You can now analyze binaries and/or source code with the tool!

# analyzing a single shared object library binary
$ fuzzable analyze examples/binaries/libbasic.so

# analyzing a single C source file
$ fuzzable analyze examples/source/libbasic.c

# analyzing a workspace with multiple C/C++ files and headers
$ fuzzable analyze examples/source/source_bundle/

Binary Ninja Plugin

fuzzable can be easily installed through the Binary Ninja plugin marketplace by going to Binary Ninja > Manage Plugins and searching for it. Here is an example of the fuzzable plugin running, accuracy identifying targets for fuzzing and further vulnerability assessment:

Usage

fuzzable comes with various options to help better tune your analysis. More will be supported in future plans and any feature requests made.

Static Analysis Heuristics

To determine fuzzability, fuzzable utilize several heuristics to determine which targets are the most viable to target for dynamic analysis. These heuristics are all weighted differently using the scikit-criteria library, which utilizes multi-criteria decision analysis to determine the best candidates. These metrics and are there weights can be seen here:

Heuristic Description Weight
Fuzz Friendly Name Symbol name implies behavior that ingests file/buffer input 0.3
Risky Sinks Arguments that flow into risky calls (ie memcpy) 0.3
Natural Loops Number of loops detected with the dominance frontier 0.05
Cyclomatic Complexity Complexity of function target based on edges + nodes 0.05
Coverage Depth Number of callees the target traverses into 0.3

As mentioned, check out the technical blog post for a more in-depth look into why and how these metrics are utilized.

Many metrics were largely inspired by Vincenzo Iozzo's original work in 0-knowledge fuzzing.

Every targets you want to analyze is diverse, and fuzzable will not be able to account for every edge case behavior in the program target. Thus, it may be important during analysis to tune these weights appropriately to see if different results make more sense for your use case. To tune these weights in the CLI, simply specify the --score-weights argument:

$ fuzzable analyze <TARGET> --score-weights=0.2,0.2,0.2,0.2,0.2

Analysis Filtering

By default, fuzzable will filter out function targets based on the following criteria:

  • Top-level entry calls - functions that aren't called by any other calls in the target. These are ideal entry points that have potentially very high coverage.
  • Static calls - (source only) functions that are static and aren't exposed through headers.
  • Imports - (binary only) other library dependencies being used by the target's implementations.

To see calls that got filtered out by fuzzable, set the --list_ignored flag:

$ fuzzable analyze --list-ignored <TARGET>

In Binary Ninja, you can turn this setting in Settings > Fuzzable > List Ignored Calls.

In the case that fuzzable falsely filters out important calls that should be analyzed, it is recommended to use --include-* arguments to include them during the run:

# include ALL non top-level calls that were filtered out
$ fuzzable analyze --include-nontop <TARGET>

# include specific symbols that were filtered out
$ fuzzable analyze --include-sym <SYM> <TARGET>

In Binary Ninja, this is supported through Settings > Fuzzable > Include non-top level calls and Symbols to Exclude.

Harness Generation

Now that you have found your ideal candidates to fuzz, fuzzable will also help you generate fuzzing harnesses that are (almost) ready to instrument and compile for use with either a file-based fuzzer (ie. AFL++, Honggfuzz) or in-memory fuzzer (libFuzzer). To do so in the CLI:

If this target is a source codebase, the generic source template will be used.

If the target is a binary, the generic black-box template will be used, which ideally can be used with a fuzzing emulation mode like AFL-QEMU. A copy of the binary will also be created as a shared object if the symbol isn't exported directly to be dlopened using LIEF.

At the moment, this feature is quite rudimentary, as it simply will create a standalone C++ harness populated with the appropriate parameters, and will not auto-generate code that is needed for any runtime behaviors (ie. instantiating and freeing structures). However, the templates created for fuzzable should get still get you running quickly. Here are some ambitious features I would like to implement down the road:

  • Full harness synthesis - harnesses will work directly with absolutely no manual changes needed.
  • Synthesis from potential unit tests using the DeepState framework (Source only).
  • Immediate deployment to a managed continuous fuzzing fleet.

Exporting Reports

fuzzable supports generating reports in various formats. The current ones that are supported are JSON, CSV and Markdown. This can be useful if you are utilizing this as part of automation where you would like to ingest the output in a serializable format.

In the CLI, simply pass the --export argument with a filename with the appropriate extension:

$ fuzzable analyze --export=report.json <TARGET>

In Binary Ninja, go to Plugins > Fuzzable > Export Fuzzability Report > ... and select the format you want to export to and the path you want to write it to.

Contributing

This tool will be continuously developed, and any help from external mantainers are appreciated!

  • Create an issue for feature requests or bugs that you have come across.
  • Submit a pull request for fixes and enhancements that you would like to see contributed to this tool.

License

Fuzzable is licensed under the MIT License.



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