An autonomous security testing workflow that runs end-to-end without manual intervention becomes possible with the hunt target.com command. This framework executes 20 coordinated AI agents to probe web applications, collect findings, and deliver structured vulnerability reports. For security teams evaluating continuous assessment pipelines, it offers a single-command entry point into automated reconnaissance.

Installing

The project is written in TypeScript and requires Node.js runtime. A credential vault stores authentication tokens, while Burp Suite MCP integrates existing proxy workflows.

git clone https://github.com/h4ckologic/bughunter-ai.git
cd bughunter-ai
npm install

The framework depends on Claude Code SDK for agent orchestration. Environment variables configure the LLM backend and proxy integrations.

Basic usage

The interface reduces to a single command pattern. After installation, targeting happens through the built-in CLI:

hunt target.com

This triggers state-machine orchestration across all 20 agents. Each agent handles a distinct phase: subdomain enumeration, endpoint discovery, parameter analysis, and exploitation pathways. Results aggregate into a unified report format.

Advanced bits

State-machine orchestration manages agent coordination through defined phases. Agents transition between discovery, scanning, and exploitation states based on intermediate findings. This prevents redundant work while maintaining coverage breadth.

Burp Suite MCP bridges the framework with existing proxy infrastructure. HTTP traffic logs feed into agent decision trees, allowing targeted attacks against authenticated flows. Session tokens and headers persist through the credential vault.

The LLM security track tunes agent behavior toward real-world bug bounty patterns. Prompts emphasize actual vulnerability classes: XSS, SQL injection, SSRF, and authentication bypasses. This differs from generic scanning by prioritizing exploitability over signature matching.

Trade-offs

The framework suits continuous assessment workflows where manual testing cadence is too slow. Small teams benefit from the 20-agent coverage without hiring additional testers. Integration with Burp Suite preserves existing toolchains while adding autonomous capability.

Resource requirements exceed lightweight scanners. Twenty concurrent agents consume significant compute during active hunts. Rate limiting and target politeness controls help manage infrastructure load.

Credential vault security demands careful key management. Teams storing production tokens should audit access permissions regularly. The vault encrypts stored credentials but requires secure initial configuration.

The approach works best against established web applications with clear input surfaces. Single-page applications or heavily JavaScript-driven sites may reduce agent effectiveness. Traditional reconnaissance tools still complement the autonomous discovery phase.

Check out the project at bughunter-ai.