Awesome Large Language Model Tools for Cybersecurity Research
The "Awesome Large Language Model Tools for Cybersecurity Research" project is an exciting exploration into how large language models (LLMs) can be applied to various domains of cybersecurity. This project compiles a series of innovative tools that leverage AI models to enhance cybersecurity research, spanning areas from reverse engineering to cloud security. Below is a detailed introduction to some of the standout tools included in this project.
Reverse Engineering
Reverse engineering in cybersecurity involves analyzing software to understand its components and functionality, often without access to the original source code. Several tools have been developed to aid this practice using large language models:
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G-3PO: A Protocol Droid for Ghidra: Created by Olivia Lucca Fraser at Tenable, this AI assistant works alongside Ghidra, a popular software reverse engineering suite. G-3PO helps users analyze and annotate decompiled code by interacting with OpenAI or Anthropic's language models. More information is available in a detailed write-up on the Tenable tech blog.
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AI for Pwndbg and AI for GEF: Both tools serve as AI-powered debugging assistants. Developed by Olivia Lucca Fraser at Tenable, they are integrated with Pwndbg and GEF, two powerful debugging frameworks, offering automated analysis and suggestions during the debugging process.
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Gepetto: This tool is an IDA Pro plugin developed by Ivan Kwiatkowski. Gepetto uses GPT models to provide explanatory comments and meaningful variable names, akin to G-3PO but designed for IDA Pro users.
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GPT-WPRE: Short for Whole-program Reverse Engineering with GPT-3, this prototype developed by Brendan Dolan-Gavitt aims to summarize entire binaries using the GPT-3 model. It relies on decompiled code from Ghidra to generate these summaries.
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IATelligence: Thomas Roccia designed this Python script to extract the Import Address Table (IAT) from PE files. It utilizes OpenAI's GPT-3 for offering insights into Windows API calls and correlates them with potential MITRE ATT&CK techniques.
Network Analysis
Network traffic analysis is crucial for identifying malicious activities and safeguarding data. One tool in this domain is:
- Burp Extension for GPT: Developed by Yossi Nisani at Tenable, this plugin for BurpSuite employs GPT to analyze HTTP requests and responses, providing deeper insights into web traffic.
Cloud Security
With the popularity of cloud computing, ensuring secure cloud environments is paramount. The tool in this area addresses privilege escalation vulnerabilities:
- EscalateGPT: Another innovation by Yossi Nisani at Tenable, this tool leverages GPT to identify vulnerabilities in AWS Identity Access Management (IAM) policies, helping users discover and mitigate weaknesses in their cloud configuration.
Proofs of Concept
Two interesting proofs of concept demonstrate the potential risks and creative applications of LLMs in cybersecurity:
Hacking LLMs
- Indirect Prompt Injections: Developed by Kai Greshake, this proof of concept showcases how indirect prompt injection attacks can manipulate LLMs, highlighting a potential vector for compromising AI systems.
LLM-Driven Malware
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LLMorphism: Created by Second Part to Hell, this is a self-replicating agent that acts as a metamorphic engine using GPT-3.5, illustrating a novel approach to malware that adapts and evolves.
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Darwin-GPT: Developed by Bernhard Mueller, this minimal self-replicating agent uses GPT-3.5/4 models, showcasing experimental applications of LLMs in creating adaptive malware.
These tools and concepts collectively demonstrate the expansive potential of integrating large language models within the realm of cybersecurity, offering new methodologies for defense and providing insights into both security measures and potential threats.