PentestGPT: Open-source automated penetration testing agentic framework
Open-source PentestGPT runs autonomous LLM-driven penetration tests via Claude Code and Codex, with legacy human-in-the-loop mode supporting many providers.
PentestGPT, originally published at USENIX Security 2024 by Gelei Deng and colleagues, is an open-source framework that lets a large language model autonomously run penetration testing stages (recon, exploit, walkthrough) with no human in the loop, driving Claude Code or Codex CLIs. A legacy interactive mode uses three cooperating LLM sessions maintaining a Pentesting Task Tree and supports OpenAI, Anthropic, Google Gemini, DeepSeek, xAI, Qwen, Moonshot, and local models via Ollama. The tool sends anonymous telemetry to Langfuse by default, excluding command outputs, credentials, and flags, and is available free on GitHub.
- Autonomous mode chains recon, exploit, and walkthrough stages without human interaction.
- Requires Python 3.12+, uv, and authenticated Claude Code and Codex CLIs bundled in Docker.
- Legacy mode supports nine providers including OpenAI, Anthropic, Gemini, DeepSeek, Qwen, and Ollama.
- Default Langfuse telemetry is metadata-only; credentials and outputs are never transmitted.
- Vendors
- Victims
- Android users
- Countries
- US
From the original article
2 min read at helpnetsecurity.com
PentestGPT is an open-source penetration testing agent that points a large language model at a target and lets it work. In its default mode it runs recon, then exploit, then walkthrough, each stage feeding the next. Switch it to pentest mode and the stages become asset discovery, vulnerability identification, report.
By @helpnetsecurity · helpnetsecurity.com
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