Pentest AI

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Pentest-AI is an open-source AI penetration testing and bug-bounty tool that proves every finding. It connects an AI client or model to a real pentest workflow — investigating web applications, discovering and reproducing vulnerabilities, and using machine oracles to verify whether an exploit actually works. No proof, no finding: verified results carry evidence you can replay yourself.

Use it through MCP (Claude Code, Codex, Cursor, VS Code), from the CLI, or in CI/CD. The model drives the investigation; an independent oracle decides whether a finding earns a verified verdict — so the output is signal, not AI slop.

Built for authorized bug bounty: import a program's scope (HackerOne, Bugcrowd, Intigriti, YesWeHack), run a scope-enforced engagement, and turn each verified finding into a platform-ready submission. Jump to the bug-bounty workflow →

Test only systems you own or have explicit authorization to assess. Read the acceptable-use policy and terms before running an engagement.

Quick start

Recommended: Codex or Claude Code

Use Python 3.10–3.14 and install pipx first. Run pipx ensurepath and reopen your terminal if needed. pipx isolates Pentest-AI's dependencies and makes its commands available across folders.

pipx install ptai
ptai setup --mcp

Install your AI client's CLI first. Setup asks before connecting all detected supported clients, including both Codex and Claude Code when installed. It preserves existing registrations. Restart your client afterward.

The wizard uses user-wide settings. It does not install clients, sign you in, download models or configure Ollama. Your AI client supplies the model; no separate model API key is required by the MCP server. Use ptai setup --mcp --dry-run to preview changes. See single-client and per-repository setup for Codex .codex/config.toml and Claude Code .mcp.json instructions.

For upgrades, use pipx upgrade ptai. In an existing activated virtual environment, python -m pip install --upgrade ptai is also supported; keep that environment available after registering it with your AI client.

Alternative: Ollama and local models

Install Pentest-AI as above, but skip MCP setup for this route. Install and start Ollama, then pull a model with tool-calling support. Use ollama list to find its exact installed name.

export PENTEST_AI_LLM_PROVIDER=ollama
export PENTEST_AI_MODEL="your-installed-model"
ptai start https://your-authorized-target.example --no-sync

In PowerShell:

$env:PENTEST_AI_LLM_PROVIDER = "ollama"
$env:PENTEST_AI_MODEL = "your-installed-model"
ptai start https://your-authorized-target.example --no-sync

Replace the placeholders before running. A local model needs no cloud API key. Model quality and available memory affect results. See the model guide for endpoint configuration.

Try the bundled demo

No API key or external target required.