PentestGPT

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• Multi-Stage Pipeline - The agent works through staged phases (recon → exploit → walkthrough for CTF; asset discovery → vulnerability identification → report for pentests), feeding each stage's findings into the next.
• Autonomous Agent - Drives Claude Code or Codex to run tools and reason without human intervention.
• Session Persistence - Save and resume penetration testing sessions.

The autonomous CTF pipeline is backend-pluggable for Claude Code and Codex. The interactive modernized legacy mode (pentestgpt-legacy) supports a wider provider set: OpenAI, Anthropic, Google Gemini, DeepSeek, xAI, Qwen, Moonshot, and local Ollama. See Interactive Multi-LLM Mode.

• AI-Powered Challenge Solver - Leverages LLM advanced reasoning to perform penetration testing and CTFs
• Live Walkthrough - Tracks steps in real-time as the agent works through challenges
• Multi-Category Support - Web, Crypto, Reversing, Forensics, PWN, Privilege Escalation
• Real-Time Feedback - Watch the AI work with live activity updates
• Extensible Architecture - Clean, modular design ready for future enhancements

• Python 3.12+
• uv - Python package manager
• Claude Code CLI (claude) - installed and authenticated for local Claude runs. See Claude Code docs
• Codex CLI (codex) - installed and authenticated for local Codex runs. The Docker flow below bundles both CLIs.

git clone https://github.com/GreyDGL/PentestGPT.git
cd PentestGPT
make install    # runs uv sync

# Run against a target (CTF mode by default)
pentestgpt --target 10.10.11.234

# With challenge context
pentestgpt --target 10.10.11.50 --instruction "WordPress site, focus on plugin vulnerabilities"

# Penetration-test mode (asset discovery → vulnerabilities → report)
pentestgpt --target 10.10.11.234 --mode pentest

# List previously saved sessions
pentestgpt --list-sessions

The agent works through a multi-stage pipeline, feeding each stage's findings into the next — recon → exploit → walkthrough for CTF, asset discovery → vulnerability identification → report for pentest.

A self-contained image bundles the tool + the Claude Code and Codex CLIs. You log in once and the sessions persist in named volumes — no re-login on later runs.

make docker-build        # build the tool image
make docker-login        # ONE-TIME, idempotent: checks logins, logs in only what's missing
make docker-auth-status  # check both are logged in (ROUNDTRIP=1 for a live 1-token check)

# Run the pipeline against a target (any backend / model / mode):
make docker-run TARGET=http://127.0.0.1:8000 BACKEND=codex MODEL=gpt-5.5 MODE=ctf
make docker-run TARGET=10.10.11.234         BACKEND=claude MODEL=opus    MODE=pentest

make docker-login logs in Claude (setup-token → token stored in the volume) and Codex (its own in-container codex login, OAuth callback forwarded via socat — not seeded, since ChatGPT refresh tokens are single-use). It is idempotent: re-running skips whatever is still valid. Logins persist across container recreation; make docker-down keeps them, make docker-nuke removes the login volumes (to force a fresh login / rotate a token). Design + details: docs/docker-dev-plan.md.

The classic, human-in-the-loop PentestGPT from the USENIX 2024 paper is preserved and modernized as pentestgpt-legacy. It runs three cooperating LLM sessions — reasoning / generation / parsing — that maintain a Pentesting Task Tree (PTT) while you drive the session interactively (next, more, todo, discuss). The autonomous fixed-stage pipeline supports Claude and Codex backends; this legacy mode talks natively to many providers via their official SDKs.

Set an API key for any provider you want to use (in your environment or .env — see .env.example). Only the providers you configure are enabled.

OPENAI_API_KEY=...        ANTHROPIC_API_KEY=...     GEMINI_API_KEY=...   # or GOOGLE_API_KEY
DEEPSEEK_API_KEY=...      GROK_API_KEY=...          QWEN_API_KEY=...     KIMI_API_KEY=...

# Auto-pick the best available models for each session
pentestgpt-legacy

# Choose models per session
pentestgpt-legacy --reasoning-model claude-opus-4-8 --parsing-model gemini-3.5-flash

# Local model via Ollama (OpenAI-compatible)
pentestgpt-legacy --reasoning-model ollama:qwen3 --base-url http://localhost:11434/v1

# List every supported model (shows which providers are configured)
pentestgpt-legacy --list-models

# Live round-trip every configured model and print a pass/fail matrix
pentestgpt-legacy --smoke-test

pentestgpt-legacy --list-models always renders the live registry. Re-run --smoke-test after model IDs change. Current snapshot:

The registry lives in pentestgpt_legacy/llm/registry.py (the single source of truth). Adding a model is one ModelSpec entry; OpenAI-compatible providers reuse one connector.

PentestGPT collects anonymous usage data to help improve the tool. This data is sent to our Langfuse project and includes:

• Session metadata (target type, duration, completion status)
• Tool execution patterns (which tools are used, not the actual commands)
• Flag detection events (that a flag was found, not the flag content)

No sensitive data is collected - command outputs, credentials, or actual flag values are never transmitted.

# Via command line flag
pentestgpt --target 10.10.11.234 --no-telemetry

# Via environment variable
export LANGFUSE_ENABLED=false

PentestGPT achieved an 86.5% success rate (90/104 benchmarks) on an XBOW validation-suite experiment in December 2025. That number is a historical research result, not a current pentestgpt-agent regression guarantee.

XBOW harnesses and result archives are maintained outside this product repository as reference-only research artifacts. The supported PentestGPT CLI, Makefile, CI, and Docker runtime do not expose an XBOW runner. A future evaluation may reuse that corpus through a separately owned adapter without making it a product dependency.

If you use PentestGPT in your research, please cite our paper:

@inproceedings{299699,
  author = {Gelei Deng and Yi Liu and Víctor Mayoral-Vilches and Peng Liu and Yuekang Li and Yuan Xu and Tianwei Zhang and Yang Liu and Martin Pinzger and Stefan Rass},
  title = {{PentestGPT}: Evaluating and Harnessing Large Language Models for Automated Penetration Testing},
  booktitle = {33rd USENIX Security Symposium (USENIX Security 24)},
  year = {2024},
  isbn = {978-1-939133-44-1},
  address = {Philadelphia, PA},
  pages = {847--864},
  url = {https://www.usenix.org/conference/usenixsecurity24/presentation/deng},
  publisher = {USENIX Association},
  month = aug
}

Distributed under the MIT License. See LICENSE.md for more information.

Disclaimer: This tool is for educational purposes and authorized security testing only. The authors do not condone any illegal use. Use at your own risk.

• Research supported by Quantstamp and NTU Singapore

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