BreachSeek: A Multi-Agent Automated Penetration Tester
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
- 2023PentestGPT: An LLM-empowered Automatic Penetration Testing Toolcited by 94
- 2024Large Language Models in Cybersecurity: State-of-the-Artcited by 34
Built on by
Citing papers per year: 2025: 4; 2026: 1.
5 papers.
- 2025VulnBot: Autonomous Penetration Testing for A Multi-Agent Collaborative Frameworkcited by 30
- 2025AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agentscited by 8
- 2025A Unified Modeling Framework for Automated Penetration Testingcited by 1
- 2025Recognition Without Mitigation: Ethical Frameworks in Autonomous Offensive-LLM Agent Researchcited by 0
- 2026Toward Secure AI-Powered Penetration Testing Agents: Security Threats, Guardrails, and Architectural Perspectivescited by 0
- 2026Toward Secure AI-Powered Penetration Testing Agents: Security Threats, Guardrails, and Architectural Perspectives15 Sep 2026
- 2025Recognition Without Mitigation: Ethical Frameworks in Autonomous Offensive-LLM Agent Research10 Jun 2025
- 2025AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agents15 May 2025
- 2025A Unified Modeling Framework for Automated Penetration Testing17 Feb 2025
- 2025VulnBot: Autonomous Penetration Testing for A Multi-Agent Collaborative Framework23 Jan 2025
Similar, not cited
Close in wording, no citation either way
- 2025xOffense: An Autonomous Multi-Agent Framework for Penetration Testing with Domain-Adapted Large Language Models
- 2024PentestAgent: Incorporating LLM Agents to Automated Penetration Testing
- 2026APT-Agent: Automated Penetration Testing using Large Language Models
- 2024AutoPenBench: Benchmarking Generative Agents for Penetration Testing
- 2024HackSynth: LLM Agent and Evaluation Framework for Autonomous Penetration Testing
- 2025AutoPentester: An LLM Agent-based Framework for Automated Pentesting
- 2024Artificial Intelligence as the New Hacker: Developing Agents for Offensive Security
- 2025AgentSentinel: An End-to-End and Real-Time Security Defense Framework for Computer-Use Agents