🚀 Pentest Ai

Official Open-Source Release & Maintained Codebase by @kush-coder


💡 About This Project

Open-source AI pentester that proves every finding. Machine oracles re-run each exploit; verified bugs ship a proof capsule you can replay yourself.

This repository is actively maintained and distributed by @kush-coder as an official open-source release.

✨ Key Highlights

• 🛡️ Verified Health Checks: Passed automated sandbox builds, dependency resolution, and health test runs.
• 🚀 Production Ready: Lightweight footprint optimized for high efficiency.
• 📦 Permissive Open Source: Available for developer integration, custom modifications, and community enhancements.

🚀 Quick Start & Installation

# Clone your official repository
git clone https://github.com/kush-coder/autopilot-pentest-ai.git
cd autopilot-pentest-ai

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📖 Comprehensive Documentation

Website · Quick start · Bug bounty · Verification · Documentation · Issues

Pentest-AI is an open-source AI penetration testing and bug-bounty tool that keeps proven findings apart from guesses. 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. A finding no oracle can prove stays labelled a candidate, and a verified one carries the requests and responses that proved it.

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 model's guesses never arrive dressed as results.

Built for authorized bug bounty: one profile per program carries its scope, its stated rate and your researcher handle, a dry run shows the plan before anything is sent, and report drafts come from verified findings only. 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 to 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.

pipx install ptai
ptai demo

The demo tests a bundled vulnerable application, replays a finding from its proof capsule, and checks the hardened routes. Inspect the evidence workflow before configuring a real engagement.

Other model providers

For standalone cloud models and optional scanner dependencies, see the installation guide and provider guide. Keep API keys out of source control.

Bug-bounty automation

Bug bounty mode is for programs that allow automated testing. You write one profile per program with its scope, its stated rate and your researcher handle. --program then applies all of it to every request, and --dry-run prints the plan without sending anything.

# ~/.pentest-ai/programs/acme.toml
platform = "hackerone"
handle = "acme"
researcher = "your-h1-username"   # sent on every request in X-Bug-Bounty
rps = 5.0                         # the rate the program states
scope = ["*.acme.com", "api.acme.com"]
excluded = ["status.acme.com"]
scanners_allowed = true
allow_race = false

ptai start https://api.acme.com --program acme --dry-run    # sends nothing
ptai start https://api.acme.com --program acme --auto-auth
ptai bounty-report <engagement-id> --program acme --output-dir drafts/

Under --program:

• Every request is held to the program's rate, external tools included. A tool that cannot be held to its share of the rate is skipped and the scan says so.
• Scope comes from the profile and nowhere else. Requests outside it are refused, and exclusions win over wildcards.
• Each request carries your researcher handle in X-Bug-Bounty, or in the header the program names (PTAI_BOUNTY_HEADER_NAME).
• Wordlist tools such as gobuster and ffuf are declined. Two built-in discovery probes still try a list of likely paths, about 500 requests.
• Payloads built to exhaust or crash the target are not sent. Probes that change other users' accounts or shared state do not run. Race-condition bursts only run when the profile sets allow_race = true.
• 429 and Retry-After are honoured. A target that slows down gets a lower rate, and one that stays degraded stops the scan with the reason recorded.
• A program that bans scanners needs a typed yes, because scanning it can void safe harbour.