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The AI Bug Hunter

October 1, 2026

The New Era of Vulnerability Hunting: Agentic AI Meets Bug Bounties | by Samadhan Shimple

From reconnaissance to submission-ready report — all from your command line, with or without a paid AI subscription.

By Samadhan shimple

8 min read

The Problem with Traditional Bug Bounty Hunting

If you've ever participated in a bug bounty program, you know the drill. You spend hours enumerating subdomains, crawling URLs, finding parameters, testing endpoints, checking technologies, validating vulnerabilities, and finally — writing the report. By the time you're done, you're exhausted, and half your findings turn out to be false positives anyway.

The repetitive parts of bug bounty hunting are exactly the parts that drain your energy and eat into the time you could spend on the creative, high-impact bugs. That's where Agentic Bug Hunter comes in.

What Is Agentic Bug Hunter?

Agentic Bug Hunter is an open-source, AI-powered bug bounty hunting toolkit built by AwareXone that takes researchers from reconnaissance to report directly from the terminal. It finds real, reportable bugs not theoretical ones by running a complete.

pipeline: recon → find → validate → report.

The project has gained significant traction, with over 4,600 GitHub stars and 25+ contributors, and was featured on Product Hunt in September 2026. It reached #10 on GitHub Trending in June 2026 and currently sits at over 5,100 stars.

Unlike traditional vulnerability scanners that just spit out alerts, Agentic Bug Hunter is designed as an orchestrated workflow that combines security tooling, AI agents, persistent hunt memory, validation logic, and reporting workflows into one environment

The Core Workflow:

The project's methodology follows a clear, four-stage pipeline:

Recon → Hunt → Validate → Report

In the standalone CLI, this translates to:

bughunter recon example.com    # Map the attack surface
bughunter hunt example.com     # Hunt for vulnerabilities
bughunter validate "finding"  # Run the 7-Question Gate
bughunter report              # Write a submission-ready report

There's also an /autopilot command that runs the entire loop autonomously with safety checkpoints, stopping for your review at critical stages.

This is a fundamental shift from treating every security tool as an isolated command. Agentic Bug Hunter connects the stages together, letting the AI reason about what deserves your attention next.

Core Capabilities:

  • Reconnaissance: Subdomain enumeration, live host probing, URL crawling, nuclei sweeps
  • Vulnerability Hunting: Tests IDOR, auth bypass, SSRF, XSS, SQLi, business logic flaws, and more
  • Validation: A 7-Question Gate that kills weak findings before you waste time reporting
  • Reporting: Generates submission-ready reports for HackerOne, Bugcrowd, Intigriti, and Immunefi in 60 seconds
  • Autopilot: Full autonomous loop — scope → recon → hunt → validate → report
  • Cross-Session Memory: Patterns found on one target inform the next; sessions can be resumed.

Supported Vulnerability Classes:

26 Web2 Vulnerability Classes

IDOR/BOLA, Auth Bypass, XSS (Stored/Reflected/DOM), SSRF, Business Logic, Race Conditions, SQL Injection, OAuth/OIDC, File Upload → RCE, GraphQL Auth Bypass, LLM/Prompt Injection, API Misconfiguration (mass assignment, JWT, CORS), Account Takeover, SSTI, Subdomain Takeover, Cloud/Infra Exposure, HTTP Request Smuggling, Cache Poisoning, MFA/2FA Bypass, SAML/SSO Attack, Error Disclosure/Debug Endpoints, CSS Injection, LFI → RCE, Insecure Deserialization, Dependency Confusion/Supply Chain, Padding Oracle/Crypto Misuse.

10 Web3 / Smart Contract Bug Classes

Accounting Desync, Access Control, Incomplete Code Path, Off-By-One, Oracle Manipulation, ERC4626 Share Inflation, Reentrancy, Flash Loan Attack, Signature Replay, Proxy/Upgrade.

AI Agents:

recon-agent: Subdomain enum·live host discovery URL crawl

report-writer: Impact-first reports that get paid

validator: Runs the 7-Question Gate — kills weak findings

web3-auditor: Smart contract audit across 10 bug classes

chain-builder: Bug A → finds bugs B and C that chain with it

autopilot: Full hunt loop with safety checkpoints

recon-ranker: Ranks attack surface by highest-value targets first

token-auditor: Meme coin / token rug pull and security scan

credential-hunter: Wordlist gen → OSINT → breach-check → spray

Installation on Linux:

Link: https://github.com/Awarexone/Agentic-Bug-Hunter

git clone https://github.com/Awarexone/Agentic-Bug-Hunter.git
cd Agentic-Bug-Hunter
./install.sh --agent standalone
bughunter setup         # pick a free AI provider (Ollama is free + offline)
bughunter recon target.com
bughunter hunt target.com
bughunter validate "my finding"
bughunter report

Quick Reference — One-Liner Setup

# Full install + first hunt in one sequence
sudo apt install golang python3 jq git -y && \
git clone https://github.com/Awarexone/Agentic-Bug-Hunter.git && \
cd Agentic-Bug-Hunter && \
chmod +x install_tools.sh && ./install_tools.sh && \
./install.sh --agent standalone && \
bughunter setup && \
bughunter recon target.com && \
bughunter hunt target.com && \
bughunter validate "finding" && \
bughunter report

Complete Linux CLI Commands (bughunter):

Core Workflow

bughunter help                  # show every command
bughunter setup                 # choose your AI provider (Ollama is free + offline)
bughunter recon target.com      # map the attack surface
bughunter hunt target.com       # hunt for vulnerabilities
bughunter validate "finding"    # 7-Question Gate on your finding
bughunter report                # write a submission-ready report
bughunter chat                  # interactive AI hunting shell
bughunter autopilot target.com  # full autonomous loop

Provider & Model Management:

bughunter providers             # list all available AI providers
bughunter models                # list models and show the selected one
bughunter status                # check which provider is active
bughunter setup --provider fluxion --model openai/gpt-4o

Short Aliases

bughunter h target.com          # short alias for hunt
bughunter r target.com          # short alias for recon
bughunter v "finding"           # short alias for validate

Engine CLI (Direct)

./engine.py recon <target>        # recon + AI surface analysis
./engine.py hunt <target>         # full hunt pipeline
./engine.py validate "<finding>"  # 7-Question Gate on a finding

MCP Server

pip install 'mcp>=2.2.0'
./install.sh --agent mcp
bughunter mcp doctor            # verify MCP setup
bughunter mcp serve             # start MCP server

Scope & Intel Commands

bughunter scope target.com              # check if a domain/URL is in scope
bughunter intel target.com              # CVEs + disclosed reports
bughunter surface target.com            # ranked attack surface
bughunter pickup target.com             # resume from last session
bughunter param-discover https://target.com  # hidden HTTP parameters

Uninstall

./uninstall.sh --agent standalone          # keep configuration
./uninstall.sh --agent standalone --purge-config  # also delete ~/.bughunter/config.json
./uninstall.sh --agent claude              # remove Claude Code plugin
./uninstall.sh --agent all                 # remove every supported target

Slash Commands (Claude Code / AI Agent Mode)

When used inside Claude Code or a supported AI agent, BugHunter exposes slash commands:

Core Workflow

/recon target.com: Subdomain enum · live host probing · URL crawl · nuclei sweep

/hunt target.com: Tests IDOR · auth bypass · SSRF · XSS · SQLi · logic flaws

/validate: 7-Question Gate — kills weak findings

/report: Generates H1 · Bugcrowd · Intigriti · Immunefi submission in 60s

/autopilot target.com: Full loop — scope → recon → hunt → validate → report

Recon & Enumeration

/surface target.com: Ranked attack surface from recon data + memory

/scope-aggregate: cloud-recon — keyword

Public S3 · Azure · GCP buckets + CloudFlare-bypass origin IPs

/param-discover: Hidden HTTP parameters via Arjun · x8

/secrets-hunt — js-bundle: Leaked credentials in source, JS bundles, or GitHub org

/takeover — recon: Subdomain takeover candidates via dnsReaper · subjack

/scan-cves: Focused nuclei high/critical sweep + log4j-scan

/bypass-403: Header · method · encoding tricks against 403/401

/portscan: Open ports + non-web services via naabu/smap

/screenshot -l urls.txt: Screenshot live hosts into HTML gallery

Scanners (Web + LLM)

/cors: CORS misconfig — origin reflection · null · credentialed

/crlf: CRLF / response-splitting + host-header injection

/nosqli: NoSQL injection (operator bypass · $where timing)

/jwt-scan: Offline JWT toolkit — alg:none · RS256→HS256 · secret crack

/oob: Out-of-band listener (interactsh) for blind SSRF/XXE/SQLi

/sast: Semgrep security packs over fetched JS/source

/domxss: Confirms DOM XSS in headless Chromium

/llm-redteam: LLM red-team corpus — prompt injection · jailbreak · exfil

Smart Contract (Web3)

/web3-audit: 10-class smart contract audit with Foundry PoC template

/token-scan: Rug pull scanner — mint authority · LP lock · honeypot · bonding curve

Session & Utility

/pickup target.com: Resume from last session — untested endpoints first

/intel target.com: CVEs + disclosed reports relevant to this target

/chain: Bug A found → finds bugs B and C that chain with it

/scope: Checks if a domain or URL is in scope before you test it

/triage: Quick 2-minute go/no-go check

/remember: Logs the current finding or technique to hunt memory

/memory-gc: Inspect or rotate hunt-memory JSONL files

/arsenal tool: Lists installed external tools or prints an install hint

Project Structure:

Agentic-Bug-Hunter/

├── skills/ # AI knowledge bases (loaded as /skill-name) │ ├── bug-bounty/ # Master workflow — all vuln classes │ ├── bb-methodology/ # Hunting mindset · 5-phase workflow │ ├── web2-recon/ # Subdomain enum · live host · URL crawl │ ├── web2-vuln-classes/ # 26 bug classes with bypass tables │ ├── security-arsenal/ # Payloads · bypass tables · gf patterns │ ├── triage-validation/ # 7-Question Gate · 4 gates │ ├── report-writing/ # Templates for H1 · Bugcrowd · Intigriti · Immunefi │ ├── web3-audit/ # Smart contract bugs · Foundry PoC │ ├── meme-coin-audit/ # Rug pull detection │ ├── credential-attack/ # Password spray methodology │ └── client-reverse/ # Request-signing / anti-bot token reversal ├── commands/ # 33 slash commands ├── agents/ # 9 specialized AI agents ├── tools/ # Python + shell scanner pipeline (~35 tools) │ ├── hunt.py # Master orchestrator │ ├── recon_engine.sh # Subdomain + URL discovery │ ├── vuln_scanner.sh # XSS · SQLi · SSRF · SSTI probe pipeline │ └── validate.py # 4-gate finding validator ├── memory/ # Cross-session hunt memory ├── rules/ # Always-active hunting + reporting rules ├── tests/ # Regression test suite (pytest) ├── web3/ # 13-chapter smart contract audit guide ├── mcp/ # MCP — native BugHunter server + Burp · Caido · HackerOne ├── wordlists/ # Curated wordlists + SecLists / PayloadsAllTheThings ├── scripts/ # Dork runner · full hunt pipeline ├── hooks/ # Claude Code hook configuration ├── demo/ # Local vulnerable target for tutorials ├── docs/ # Extended documentation ├── engine.py # Standalone CLI — 'bughunter' command ├── brain.py # Multi-provider LLM layer ├── agent.py # LangGraph-style ReAct hunting agent ├── install.sh # Install skills + commands ├── install_tools.sh # Install subfinder · httpx · nuclei · katana · ffuf ├── uninstall.sh # Remove skills + commands ├── uninstall_tools.sh # Remove external scanning tools ├── serve.py # Launch local demo target ├── config.example.json # Auth session config template ├── requirements.txt # Python dependencies ├── CLAUDE.md # Claude Code plugin manifest ├── AGENTS.md # Multi-harness plugin guide (OpenCode · Codex · Pi) └── SKILL.md # Master skill shortcut

The 7-Question Gate (Validation)

The validate command runs findings through a strict 7-Question Gate before you report:

  1. Can I reproduce it?
  2. Is it actually a security issue?
  3. Can I demonstrate impact?
  4. Is it within scope?
  5. Can another researcher reproduce my result?
  6. Can an attacker do this RIGHT NOW?
  7. Is the impact worth reporting?

A Safer Way to Learn

If you're new to agentic security workflows, the tool's documentation recommends building a laboratory environment before pointing it at real targets. Intentionally vulnerable applications like OWASP Juice Shop, DVWA, WebGoat, and PortSwigger Web Security Academy labs are excellent for learning how the AI moves through the

recon → discovery → validation → reporting pipeline.

The FAQ also emphasizes that you don't need to be a professional hacker to use this tool. Beginners get AI guidance at each step, mid-level hunters get time-saving automation, and experienced hunters get an autonomous loop that runs while they focus on creative bugs.

Important Limitations and Ethical Considerations

Agentic Bug Hunter is a powerful tool, but it's critical to understand its limitations:

You must have explicit, written authorization to test any target. The tool may only be used on assets listed as in-scope in an active bug bounty program. Testing without permission is illegal under laws like the Computer Fraud and Abuse Act (USA) and Computer Misuse Act (UK).

You are responsible for every HTTP request the tool sends. The terms explicitly state that "The AI did it" is not a legal defense.

The AI can make mistakes. It can make incorrect assumptions, misunderstand application logic, misclassify responses, generate false positives, and miss vulnerabilities. The tool is an assistant, not a replacement for professional judgment.

Scope control becomes more important with automation, not less. A human might accidentally send one request to the wrong system — an automated workflow can send hundreds.

Agentic Bug Hunter is released under the MIT License and is built by AwareXone. For authorized security testing only. Always test within an approved bug bounty program scope.

Have questions or want to share your own recon workflow? Drop a comment below or connect with me on https://www.linkedin.com/in/samadhan-shimple/