Agentic-Bug-Hunter is a Python CLI and Claude Code plugin that automates the recon-to-report pipeline for authorized bug bounty hunters, with a strict validation gate and free local LLM support.
| Tool | Awarexone/Agentic-Bug-Hunter — AI-assisted bug bounty toolkit that chains recon, hunting, validation, and reporting in a terminal workflow |
| Category | AI-augmented offensive security automation / recon orchestration |
| Primary Use | Running bughunter recon, hunt, validate, and report against in-scope bug bounty targets on platforms like HackerOne, Bugcrowd, Intigriti, and Immunefi |
| Safe Use | Intended solely for authorized engagements: programs with defined scope on sanctioned bounty platforms, penetration tests with written permission, and lab environments |
| Telemetry Note | Full recon shells out to subfinder, httpx, nuclei, katana, ffuf, and nmap, generating traffic and scan artifacts that defenders will see as standard tool signatures; all output persists under ~/.bughunter/ |
Agentic-Bug-Hunter sits in a growing class of tools that wrap large language models around the mechanical parts of bug bounty work. Written in Python (3.10+) and MIT-licensed, the project has accumulated roughly 4,800 stars and positions itself as an end-to-end pipeline: point it at a target and it performs reconnaissance, tests for vulnerabilities, validates findings against what the README calls a strict gate, and drafts a submission-ready report. The framing is explicitly aimed at bounty platforms — HackerOne, Bugcrowd, Intigriti, and Immunefi are all named — which tells you the intended operator is someone working within authorized program scope, not someone scanning arbitrary infrastructure.
Architecturally, the tool is interesting because it is agent-agnostic. It ships in two modes: as a Claude Code plugin, where you drive it with slash commands like /hunt target.com, and as a fully standalone CLI exposed as the bughunter command with no subscription required. The standalone path is clearly the project's strategic bet — the README leads with the fact that you no longer need Claude Code, Claude Pro, or any paid AI subscription. That decision pushed the design toward a pluggable provider layer where bughunter setup configures whichever LLM backend you prefer, and bughunter providers enumerates the options.
The provider matrix is where the privacy story lives. Ollama is listed first with full local execution — the README suggests ollama pull qwen2.5:14b — which means an operator can run the entire hunting loop without target data ever leaving the machine. Cloud options include Groq (free tier), DeepSeek, the Claude API, OpenAI, Grok via xAI, OpenRouter, and OrcaRouter. For consultants under NDA or handling sensitive scope definitions, the Ollama path is the defensible choice; every cloud provider in that list receives at least some context about the target being tested, which is a data-handling consideration worth raising in any engagement's rules of engagement.
Under the hood, the CLI does not reinvent reconnaissance tooling — it orchestrates it. Full recon delegates to the standard ProjectDiscovery-and-friends stack: subfinder for subdomain enumeration, httpx for probing, nuclei for template-based vulnerability scanning, katana for crawling, plus ffuf for content discovery and nmap for port mapping. These are pulled in via an install_tools.sh script from the repository. This is a sensible design: the LLM layer adds reasoning, prioritization, and report writing on top of tooling that already has mature signatures, rather than reimplementing scanners poorly.
The command surface is compact and readable. bughunter recon target.com maps the attack surface, bughunter hunt target.com runs the full find loop, bughunter validate "finding" applies what the README calls a 7-Question Gate to a candidate finding, and bughunter report produces the submission document. There are short aliases (h, r, v), a bughunter chat interactive AI shell, and introspection commands (providers, models, status). Everything the tool writes lands under ~/.bughunter/, and provider configuration persists in ~/.bughunter/config.json — a detail that matters for both cleanup after an engagement and for incident responders who might find the directory as an artifact.
The 7-Question Gate is the most conceptually important feature, even though the README doesn't enumerate the questions themselves. The core problem with LLM-driven security tooling is false positives: language models are excellent at producing plausible-sounding vulnerability narratives and terrible at knowing whether a finding is real. By forcing every candidate through a validation stage before it reaches the report — and by claiming to find 'real, reportable bugs, not theoretical ones' — the tool attempts to separate evidence-backed findings from model hallucination. Any operator adopting this class of tool should treat that gate as the load-bearing wall and manually verify anything that passes it before submission.
Memory is the other differentiator. The README states that patterns found on one target inform the next, and that sessions resume where they left off. This implies persistent state under ~/.bughunter/ that accumulates target-specific knowledge — useful for long-running bounty programs where you return to a scope repeatedly, but also a concentration of sensitive reconnaissance data on the operator's workstation. Encrypt or carefully scope that directory if the machine is shared, and be aware that resuming a session assumes the target is still in scope since your last run.
Installation is straightforward and the lifecycle scripts are unusually well thought out. Beyond uv tool install agentic-bug-hunter or pipx, the git-based path uses ./install.sh --agent standalone, which the README notes can refresh older managed installs in /usr/local/bin or ~/.local/bin while preserving provider configuration. The matching ./uninstall.sh supports --purge-config and targets claude, opencode, pi, codex, agents, and all — the range of agent names is itself a small map of the current agentic-tooling ecosystem. A tests.yml GitHub Actions workflow runs on main, which suggests at least baseline CI hygiene.
From a defensive perspective, this tool is mostly a visibility amplification layer over known quantities. The heavy lifting produces recognizable telemetry: nuclei scan patterns, ffuf brute-force request rates, subfinder passive DNS lookups, and katana crawl behavior. Security teams watching internet-facing assets should already have detections for those; what changes is cadence and consistency, since an AI agent will run these more persistently and more patiently than a human clicking through a checklist. The ~/.bughunter/ directory, config.json, and the bughunter binary are host-side indicators relevant to insider-threat or authorized-assessment verification contexts.
A few caveats deserve honest mention. The README is heavy on promotion — a 'Trusted By' logo wall compiled from stargazers' public employer profiles (which the README itself carefully disclaims as non-endorsement), cross-promotional banners for a social media account recovery service, and a referral link to an AI gateway partner with a promo code. None of this affects functionality, but a senior operator should read it as marketing surface and evaluate the code on its merits. The MIT license and visible test workflow are the more substantive trust signals.
For the right operator, though, the value proposition is real. Junior bounty hunters get a structured pipeline that enforces validation discipline instead of skipping it; experienced hunters get an automation scaffold that handles the tedious recon-to-report glue and lets local Ollama models do the reasoning without recurring API costs. Red team leads can use it as a scoped recon orchestrator on authorized engagements, and blue teams benefit from understanding what its output and traffic look like. As with every LLM-driven security tool, the professional posture is simple: run it only against targets you are contractually permitted to test, verify every gated finding with your own eyes, and keep the human in the submission loop.
Awarexone/Agentic-Bug-Hunter.Educational analysis for authorized security professionals. Use only in controlled, authorized environments.
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