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AI/TLDR

PentestGPT

AI-powered autonomous penetration testing agent, published at USENIX Security 2024

Security AgentsOpen source
Language
Python
License
MIT
$git clone https://github.com/GreyDGL/PentestGPT.git

Overview

PentestGPT is an open-source agent that uses large language models to perform penetration testing and capture-the-flag (CTF) challenges. It was published at USENIX Security 2024 and has since grown into an agentic tool that can work through a target on its own.

The current v1.0 release runs an autonomous iteration loop: the agent works continuously, keeps a context file with its progress, and restarts with that prior context when it hits a limit. The loop stops when a flag is captured or the maximum number of iterations is reached.

PentestGPT also keeps the classic human-in-the-loop experience from the original paper as a modernized legacy mode. That mode uses three cooperating LLM sessions for reasoning, generation, and parsing, and lets you drive the session interactively while it maintains a Pentesting Task Tree.

What it does

  • Autonomous agent pipeline that runs penetration tests and CTFs with little or no human input
  • Iteration loop with a saved context file, so the agent can restart with prior progress and stop on flag capture or a max-iteration limit
  • Live walkthrough and real-time feedback that show each step as the agent works through a challenge
  • Multi-category coverage for web, crypto, reversing, forensics, PWN, and privilege escalation tasks
  • Interactive multi-LLM legacy mode supporting OpenAI, Anthropic, Google Gemini, DeepSeek, xAI Grok, Qwen, Moonshot Kimi, and local Ollama models
  • Session persistence to save and resume penetration testing runs

Getting started

PentestGPT needs Python 3.12+, the uv package manager, and an installed and authenticated Claude Code CLI for the autonomous agent. Clone the repository, install dependencies, then run the agent against a target.

Clone and install

Clone the repository and install dependencies with the provided Make target, which runs uv sync under the hood.

bashbash
git clone https://github.com/GreyDGL/PentestGPT.git
cd PentestGPT
make install

Run the autonomous agent against a target

Point PentestGPT at a target host. You can add challenge context with an instruction and cap the number of iterations.

bashbash
pentestgpt --target 10.10.11.234

pentestgpt --target 10.10.11.50 --instruction "WordPress site, focus on plugin vulnerabilities"

pentestgpt --target 10.10.11.234 --max-iterations 5

Use the interactive multi-LLM legacy mode

Set an API key for any provider you want, then run the modernized legacy mode. It can auto-pick the best available models or let you choose a model per session, and it can list or smoke-test every supported model.

bashbash
pentestgpt-legacy

pentestgpt-legacy --reasoning-model claude-opus-4-8 --parsing-model gemini-3.5-flash

pentestgpt-legacy --list-models

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

When to use it

  • Running autonomous penetration tests against an authorized target to find and exploit vulnerabilities
  • Solving capture-the-flag challenges across categories such as web, crypto, reversing, forensics, PWN, and privilege escalation
  • Driving a human-in-the-loop pentesting session that maintains a task tree and suggests next steps interactively
  • Comparing how different LLM providers and models perform on security reasoning tasks via the legacy mode's model registry and smoke test

How PentestGPT compares

PentestGPT alongside other open-source security agents tools AI/TLDR tracks, ranked by GitHub stars.

ToolStarsWhat it does
PentAGI★ 25.2kPentAGI is a self-hosted AI security platform that plans and runs penetration tests autonomously using a team of agents and 20+ built-in pentesting tools.
PentestGPT★ 15.7kAI-powered autonomous penetration testing agent, published at USENIX Security 2024
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CAI★ 9.8kCAI (Cybersecurity AI) is an open-source Python framework for building AI agents that automate offensive and defensive security tasks like recon, vulnerability discovery, and exploitation.
AI-Infra-Guard★ 6.7kTencent Zhuque Lab's AI red teaming platform: scans agents, Agent Skills and MCP servers, checks AI infra against a CVE library, fingerprints API relays and runs jailbreak evaluations.
T3MP3ST★ 6.3kA multi-agent offensive-security harness for authorised testing that drives an already-installed coding agent, or a local OpenAI-compatible model, through recon, exploitation and reporting from a localhost War Room or the CLI.
RedAmon★ 2.9kA Docker-deployed offensive-security platform for authorised testing that chains parallel recon, exploitation and post-exploitation into a Neo4j attack graph, then triages the findings and opens remediation pull requests on your repository.