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BreachSeek: A Multi-Agent Automated Penetration Tester

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arxiv 2409.03789 v1 pith:O7YCBAKE submitted 2024-08-31 cs.CR cs.AI

BreachSeek: A Multi-Agent Automated Penetration Tester

classification cs.CR cs.AI
keywords breachseekpenetrationvulnerabilitiesautomatedcybersecuritymulti-agenttestingacross
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The increasing complexity and scale of modern digital environments have exposed significant gaps in traditional cybersecurity penetration testing methods, which are often time-consuming, labor-intensive, and unable to rapidly adapt to emerging threats. There is a critical need for an automated solution that can efficiently identify and exploit vulnerabilities across diverse systems without extensive human intervention. BreachSeek addresses this challenge by providing an AI-driven multi-agent software platform that leverages Large Language Models (LLMs) integrated through LangChain and LangGraph in Python. This system enables autonomous agents to conduct thorough penetration testing by identifying vulnerabilities, simulating a variety of cyberattacks, executing exploits, and generating comprehensive security reports. In preliminary evaluations, BreachSeek successfully exploited vulnerabilities in exploitable machines within local networks, demonstrating its practical effectiveness. Future developments aim to expand its capabilities, positioning it as an indispensable tool for cybersecurity professionals.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Hackers or Hallucinators? A Comprehensive Analysis of LLM-Based Automated Penetration Testing

    cs.CR 2026-04 unverdicted novelty 8.0

    The first SoK on LLM-based AutoPT frameworks provides a six-dimension taxonomy of agent designs and a unified empirical benchmark evaluating 15 frameworks via over 10 billion tokens and 1,500 manually reviewed logs.

  2. A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges

    cs.SE 2026-07 accept novelty 5.5

    LLM pentest agents co-evolved through four bottleneck-driven phases into RLVR systems, while CTF platforms became dual evaluation/training infrastructure and three linked reliability gaps remain.