mlsanityData to 14 Oct 2026

PentestMCP: A Toolkit for Agentic Penetration Testing

arXiv 2510.03610 ↗First posted 4 Oct 2025cs.CR cs.AIcited by 2

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

Abstract (arXiv)

Agentic AI is transforming security by automating many tasks being performed manually. While initial agentic approaches employed a monolithic architecture, the Model-Context-Protocol has now enabled a remote-procedure call (RPC) paradigm to agentic applications, allowing for the flexible construction and composition of multi-function agents. This paper describes PentestMCP, a library of MCP server implementations that support agentic penetration testing. By supporting common penetration testing tasks such as network scanning, resource enumeration, service fingerprinting, vulnerability scanning, exploitation, and post-exploitation, PentestMCP allows a developer to customize multi-agent workflows for performing penetration tests.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 0 of its 12 references that are in the map. First posted on arXiv 4 Oct 2025.

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Citing papers per year: 2026: 2.

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  1. 2026From Controlled to the Wild: Evaluation of Pentesting Agents for the Real-World
  2. 2025PentestJudge: Judging Agent Behavior Against Operational Requirements
  3. 2026Environment-Grounded Multi-Agent Workflow for Autonomous Penetration Testing
  4. 2025RedTeamLLM: an Agentic AI framework for offensive security
  5. 2026APT-Agent: Automated Penetration Testing using Large Language Models
  6. 2025AutoPentester: An LLM Agent-based Framework for Automated Pentesting
  7. 2025VulnBot: Autonomous Penetration Testing for A Multi-Agent Collaborative Framework
  8. 2026PenForge: On-the-Fly Expert Agent Construction for Automated Penetration Testing