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---
## Demo
### Installation
[](https://asciinema.org/a/761661)
[Watch on YouTube](https://www.youtube.com/watch?v=RUNmoXqBwVg)
### PentestGPT in Action
[](https://asciinema.org/a/761663)
[Watch on YouTube](https://www.youtube.com/watch?v=cWi3Yb7RmZA)
---
## What's New in v1.0 (Agentic Upgrade)
- **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](#interactive-multi-llm-mode-modernized-legacy).
---
## Features
- **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
---
## Quick Start
### Prerequisites
- **Python 3.12+**
- **[uv](https://docs.astral.sh/uv/)** - Python package manager
- **Claude Code CLI** (`claude`) - installed and authenticated for local Claude runs. See [Claude Code docs](https://docs.anthropic.com/en/docs/claude-code)
- **Codex CLI** (`codex`) - installed and authenticated for local Codex runs. The Docker flow below bundles both CLIs.
### Installation
```bash
git clone https://github.com/GreyDGL/PentestGPT.git
cd PentestGPT
make install # runs uv sync
```
### Commands Reference
| Command | Description |
|---------|-------------|
| `make install` | Install dependencies |
| `make test` | Run all tests |
| `make check` | Run lint + typecheck |
| `make build` | Build distributable package |
---
## Usage
```bash
# 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.
### Run in Docker (install once, log in once)
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.
```bash
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`](https://github.com/GreyDGL/PentestGPT/blob/HEAD/docs/docker-dev-plan.md).
---
## Interactive Multi-LLM Mode (modernized legacy)
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.
### Configure providers
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.
```bash
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=...
```
### Run
```bash
# 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
```
### Supported models (web-verified June 2026)
`pentestgpt-legacy --list-models` always renders the live registry. Re-run `--smoke-test`
after model IDs change. Current snapshot:
| Provider | Current models | Legacy (kept) | Env key |
|----------|----------------|---------------|---------|
| **OpenAI** | `gpt-5.5`, `gpt-5.5-pro`, `gpt-5.4-mini`, `gpt-5.4-nano`, `gpt-5.2`, `gpt-5.3-codex` | `gpt-4o`, `gpt-4o-mini`, `o3`, `o4-mini` | `OPENAI_API_KEY` |
| **Anthropic** | `claude-opus-4-8`, `claude-sonnet-4-6`, `claude-haiku-4-5-20251001` | — | `ANTHROPIC_API_KEY` |
| **Google Gemini** | `gemini-3.1-pro`, `gemini-3.5-flash`, `gemini-3-pro`, `gemini-3.1-flash-lite` | `gemini-2.5-pro`, `gemini-2.5-flash` | `GEMINI_API_KEY` / `GOOGLE_API_KEY` |
| **DeepSeek** | `deepseek-v4-flash`, `deepseek-v4-pro` | `deepseek-chat`, `deepseek-reasoner` | `DEEPSEEK_API_KEY` |
| **xAI Grok** | `grok-4.3` | — | `GROK_API_KEY` / `XAI_API_KEY` |
| **Alibaba Qwen** | `qwen3.7-max`, `qwen3.5-flash` | `qwen3-max` | `QWEN_API_KEY` / `DASHSCOPE_API_KEY` |
| **Moonshot Kimi** | `kimi-k2.6` | — | `KIMI_API_KEY` (`.cn` default; set `MOONSHOT_BASE_URL` for `.ai`) |
| **Local (Ollama)** | `ollama:` (e.g. `ollama:qwen3`) | — | none (`OLLAMA_BASE_URL`) |
> 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.
---
## Telemetry
PentestGPT collects anonymous usage data to help improve the tool. This data is sent to our [Langfuse](https://langfuse.com) 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.
### Opting Out
```bash
# Via command line flag
pentestgpt --target 10.10.11.234 --no-telemetry
# Via environment variable
export LANGFUSE_ENABLED=false
```
---
## Benchmark history
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.
---
## Citation
If you use PentestGPT in your research, please cite our paper:
```bibtex
@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
}
```
---
## License
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.
---
## Acknowledgments
- Research supported by [Quantstamp](https://www.quantstamp.com/) and [NTU Singapore](https://www.ntu.edu.sg/)
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