HackSynth An Autonomous Penetration Testing Framework For Simulating Cyber-Attacks

HackSynth is an autonomous penetration testing agent that leverages Large Language Models (LLMs) to solve Capture The Flag (CTF) challenges without human intervention. 

It utilizes a two-module architecture: a planner to create commands and a summarizer to understand the hacking process’s current state by employing contextual information from past commands to make future decisions and adapt strategies. 

For the purpose of ensuring security, HackSynth operates within a containerized environment that is protected by a firewall, which prevents unauthorized interactions and safeguards systems, respectively. 

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The use of Large Language Models (LLMs) for Capture The Flag (CTF) challenges, which are gamified security exercises where participants find vulnerabilities to uncover flags. 

Traditional tools for CTFs rely on heuristics and lack human-like reasoning, where LLMs offer more adaptable solutions. LLM agents, powered by LLMs, can perceive their environment, make decisions, and take actions.

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Existing LLM agents have shown success in areas like privilege escalation and vulnerability identification. However, these agents often require human intervention and lack the full autonomy of human experts. 

HackSynth is an autonomous LLM-based system designed to solve cybersecurity challenges, consists of a Planner module that generates commands within a secure containerized environment and a Summarizer module that maintains a comprehensive history of actions and observations. 

The system utilizes a feedback loop to continuously refine its actions and achieve its objectives.

Two benchmarks, PicoCTF and OverTheWire, are proposed to evaluate the effectiveness of HackSynth, which cover a wide range of cybersecurity challenges, from basic Linux commands to complex binary exploitation and cryptography techniques.

The study optimizes HackSynth’s parameters, improving its performance on CTF benchmarks. A larger observation window enhances performance up to a point, while higher temperatures and top-p values can increase variability but decrease reliability. 

GPT-4o and Llama-3.1-70B excel on both benchmarks, with GPT-4o showing faster response times. Iterative planning and summarizing significantly impact performance, with higher-performing models benefiting more from additional cycles.

Command usage varies across models, with Qwen2-72B exhibiting a tendency for elevated privilege commands, highlighting potential security risks.

HackSynth demonstrates unique problem-solving strategies, often leveraging command-line tools for tasks typically requiring interactive interfaces, while its reliance on initial problem-solving steps can lead to fixation on ineffective strategies. 

Unexpected behaviors like hallucinating targets, searching within the execution environment, and resource exhaustion highlight the need for robust safety measures when deploying such autonomous agents.

It is a promising automated penetration testing framework that can be further enhanced by incorporating specialized modules for visual data analysis, internet searches, and interactive terminal handling. 

Fine-tuning techniques like RAG and RLHF can optimize its performance. Expanding benchmarks to complex platforms and real-world scenarios, including live CTF events, will provide rigorous evaluation.

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