mlsanityData to 7 Oct 2026

BreachSeek: A Multi-Agent Automated Penetration Tester

arXiv 2409.03789 ↗First posted 31 Aug 2024cs.CR cs.AIcited by 5

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 2 of its 5 references that are in the map. First posted on arXiv 31 Aug 2024.

Builds on

2 of 5 references.

References · 2

  1. 2023PentestGPT: An LLM-empowered Automatic Penetration Testing Toolcited by 94
  2. 2024Large Language Models in Cybersecurity: State-of-the-Artcited by 34

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025xOffense: An Autonomous Multi-Agent Framework for Penetration Testing with Domain-Adapted Large Language Models
  2. 2024PentestAgent: Incorporating LLM Agents to Automated Penetration Testing
  3. 2026APT-Agent: Automated Penetration Testing using Large Language Models
  4. 2024AutoPenBench: Benchmarking Generative Agents for Penetration Testing
  5. 2024HackSynth: LLM Agent and Evaluation Framework for Autonomous Penetration Testing
  6. 2025AutoPentester: An LLM Agent-based Framework for Automated Pentesting
  7. 2024Artificial Intelligence as the New Hacker: Developing Agents for Offensive Security
  8. 2025AgentSentinel: An End-to-End and Real-Time Security Defense Framework for Computer-Use Agents