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Introduction:
The traditional bug bounty workflow is a grind: hours of passive reconnaissance, manual vulnerability testing across dozens of classes, tedious validation of false positives, and even more time crafting reports that might get rejected. BugHunter disrupts this paradigm by introducing a terminal-first, AI-powered toolkit that automates the entire pipeline—from reconnaissance to submission-ready reports—without requiring any paid AI subscription. By integrating with local models like Ollama or free-tier cloud providers such as Groq, this open-source framework democratizes elite-level hunting capabilities for security professionals at any level.
Learning Objectives:
- Master the configuration and deployment of BugHunter in standalone mode using local LLMs for fully offline, zero-cost bug bounty operations.
- Execute automated vulnerability assessments and apply the “7-Question Gate” validation process to drastically reduce false positives.
- Generate platform-compliant reports for HackerOne, Bugcrowd, and Immunefi directly from terminal findings.
You Should Know:
- Zero-Cost Arsenal: Deploying Your Autonomous AI Hunting Stack
BugHunter’s core innovation is its ability to operate without ongoing subscriptions, prioritizing local and free AI providers in a strict order: Ollama → Groq → DeepSeek → Claude → OpenAI. This section provides a verified, step-by-step guide to deploying a fully functional, offline-capable installation on a Linux or macOS system (Windows via WSL).
Step‑by‑step guide explaining what this does and how to use it.
This process establishes a complete, self-contained AI penetration testing environment. First, you’ll deploy a local LLM using Ollama, eliminating any external API dependencies. Then, you’ll install BugHunter and its essential scanning toolchain—including subfinder, httpx, nuclei, ffuf, katana, and dalfox—which are automatically detected and utilized by the AI agents. Finally, you’ll configure the standalone `bughunter` CLI, ready to execute autonomous reconnaissance and exploitation.
Step 1: Install Ollama and Pull a Local Model
Install Ollama (local LLM runner) curl -fsSL https://ollama.ai/install.sh | sh Pull a capable model (~9 GB download, runs fully offline afterward) ollama pull qwen2.5:14b
Step 2: Install BugHunter and Its Toolchain
Clone the repository git clone https://github.com/shuvonsec/claude-bug-bounty.git cd claude-bug-bounty Install the scanning tools (subfinder, nuclei, ffuf, etc.) chmod +x install_tools.sh && ./install_tools.sh
What this does: The `install_tools.sh` script installs a curated suite of open-source security scanners. BugHunter’s AI agents will call these tools based on the target context; if a tool is missing, the agent intelligently skips it and adapts its strategy rather than failing.
Step 3: Install the Standalone CLI & Configure AI
Install the 'bughunter' command system-wide ./install.sh --agent standalone Configure your AI provider (select Ollama from the interactive menu) bughunter setup Verify the configuration and see which provider is active bughunter status
What this does: The `–agent standalone` flag creates the `bughunter` binary, completely decoupled from Claude Code. The `setup` command walks you through AI provider selection, and `status` confirms which model is powering your hunt.
- From Recon to Report: Executing the Autonomous Workflow
With the stack deployed, the next phase is operational. BugHunter encodes the methodology of a senior penetration tester into a series of slash commands and autonomous agents. This guide walks through a live hunt using the core commands that map to the phases of a professional engagement: attack surface mapping, vulnerability discovery, rigorous validation, and report generation.
Step‑by‑step guide explaining what this does and how to use it.
This workflow mirrors a professional bug bounty engagement. You begin by discovering the attack surface, then move to targeted exploitation, followed by a strict validation gate to filter out non-issues, and finally generate a compliant report. The `/autopilot` command can automate all four phases in sequence, with checkpoints for researcher review.
Step 1: Reconnaissance – Map the Attack Surface
Full reconnaissance: subdomain enumeration, live probing, URL crawling, and nuclei sweep bughunter recon target.com Alternative using the slash command if running within Claude Code /recon target.com
What this does: This command executes a multi-layered discovery process. It performs subdomain enumeration, probes for live hosts, crawls URLs, and runs a `nuclei` template sweep, consolidating all findings into a structured knowledge base for subsequent hunting phases.
Step 2: Hunting – Vulnerability Discovery
Launch the hunting agent against the target bughunter hunt target.com Short alias for quick execution bughunter h target.com
What this does: The hunting agent tests for over 20 vulnerability classes, including IDOR, authentication bypass, SSRF, XSS, SQLi, and business logic flaws. It leverages the reconnaissance data to prioritize high-value attack surfaces and dynamically selects appropriate tools (e.g., `ffuf` for fuzzing, `dalfox` for XSS) for each test.
Step 3: Validation – Applying the 7-Question Gate
After finding a candidate vulnerability, run the validation gate bughunter validate "my finding description" Short alias bughunter v "IDOR on /api/v1/user/123"
What this does: This is the critical quality control step. The validator agent asks seven structured questions to assess exploitability, impact, and required privileges. Findings that fail this gate are killed immediately, saving researchers from wasting time reporting false positives or low-impact issues.
Step 4: Report Generation
Generate a submission-ready report for the validated finding bughunter report
What this does: The report-writer agent consumes the validated finding and the hunt memory to produce a professional, impact-first report formatted for HackerOne, Bugcrowd, Intigriti, or Immunefi. The report includes technical details, proof-of-concept, and remediation advice, ready for direct submission.
3. Advanced AI Agent Orchestration and Customization
Beyond the core workflow, BugHunter features a multi-agent architecture that allows for highly specialized and autonomous operations. Understanding how to leverage these agents unlocks the tool’s full potential for both bug bounty and advanced penetration testing.
Understanding the Agent Architecture:
recon-agent: Autonomous subdomain enumeration and live host discovery.validator: Runs the 7-Question Gate, a critical quality control step.chain-builder: Takes a found bug and attempts to identify chained vulnerabilities that increase overall impact.autopilot: The full autonomous hunting loop with safety checkpoints.web3-auditor: Specialized agent for smart contract audit across 10 bug classes.
Customizing with External API Keys:
For enhanced reconnaissance, add optional API keys that the agents will automatically leverage:
Better subdomain coverage via Chaos API export CHAOS_API_KEY="your-key-here" VirusTotal integration for enriched data export VT_API_KEY="your-key-here"
4. Linux and Windows Commands for Manual Validation
While BugHunter automates much of the process, manual validation remains a critical skill. Below are verified commands for common tasks, usable standalone or to verify AI-generated findings.
Linux/macOS Commands:
HTTP parameter discovery with Arjun (installed by BugHunter) arjun -u https://target.com/api/endpoint -o param_output.txt Subdomain takeover check with subjack subjack -w subdomains.txt -t 100 -timeout 30 -ssl -o results.txt 403/401 bypass techniques curl -X OPTIONS https://target.com/admin -v curl -H "X-Forwarded-For: 127.0.0.1" https://target.com/admin curl -H "X-Original-URL: /admin" https://target.com/ SQL injection testing with sqlmap (manual override) sqlmap -u "https://target.com/page?id=1" --batch --level=3 --risk=2 XSS detection with dalfox dalfox url https://target.com/search?q=test --custom-cookie "session=value"
Windows Commands (via WSL2 recommended):
Inside WSL2 Ubuntu terminal Install prerequisites sudo apt update && sudo apt install golang python3 jq git Clone and install BugHunter (same as Linux steps above) git clone https://github.com/shuvonsec/claude-bug-bounty.git cd claude-bug-bounty && ./install_tools.sh && ./install.sh --agent standalone Run recon from WSL2 bughunter recon windows-target.internal
5. API Security and Cloud Hardening Automation
BugHunter includes specialized modules for API security and cloud asset discovery, addressing two of the most critical and payout-rich areas in modern bug bounty.
API Security Commands:
Hidden parameter discovery in API endpoints /param-discover https://api.target.com/v2/ GraphQL introspection and attack /graphql-scan https://api.target.com/graphql JWT vulnerability testing /jwt-attack "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..."
Cloud Reconnaissance Commands:
Discover public S3, Azure, and GCP buckets /cloud-recon --keyword targetcompany Find origin IPs behind CloudFlare (for SSRF and direct attacks) /cloud-recon --cloudflare-bypass
What this does: The `/cloud-recon` command performs targeted searches for misconfigured cloud storage and attempts to bypass CDNs to discover origin servers, opening avenues for SSRF and direct infrastructure attacks.
6. Vulnerability Exploitation and Mitigation Table
Understanding both exploitation and mitigation is essential for effective reporting. Below is a quick reference table for high-value vulnerability classes targeted by BugHunter.
| Vulnerability Class | Exploitation Technique (BugHunter Agent) | Mitigation Strategy |
| : | : | : |
| IDOR | Enumerate sequential object IDs; chain with race conditions | Implement object-level access controls; use unpredictable, non-sequential identifiers |
| SSRF | Inject internal IPs (169.254.169.254) and port scan internal services | Strict allowlist of allowed URLs; disable HTTP redirects; run URL allowlist validation |
| XSS | Context-aware payload injection; automated DOM testing via Dalfox | Output encode properly; deploy strict CSP headers; use `HttpOnly` and `Secure` flags |
| SQLi | Error-based and time-based inference via `sqlmap` | Parameterized queries/prepared statements; least privilege DB accounts |
| Auth Bypass | Parameter pollution; JWT algorithm confusion (e.g., `none` algorithm) | Enforce strong JWT validation; implement multi-factor authentication |
| Subdomain Takeover | DNS enumeration; identify dangling CNAME records | Regular DNS hygiene audits; remove orphaned DNS records |
| Cloud Misconfiguration | Public bucket enumeration; IAM privilege escalation | Enforce bucket policies; use AWS Config rules; mandatory MFA for IAM users |
What Undercode Say:
- Key Takeaway 1: The democratization of elite bug bounty capabilities through local LLMs (Ollama) removes financial barriers to entry, leveling the playing field for independent researchers and students.
- Key Takeaway 2: The “7-Question Gate” validation agent represents a significant advancement in AI application for security, directly addressing the critical problem of false positives that has long plagued automated scanners.
Analysis (10 lines): BugHunter is more than a collection of scripts; it is a philosophical shift in how we approach offensive security automation. By embedding an adversarial testing methodology directly into an agentic workflow, it forces a discipline that is often missing in both novice and automated hunting. The ability to run a full reconnaissance, hunting, and reporting loop within a fully offline environment using Ollama addresses significant privacy and compliance concerns for enterprise red teams. The project’s modular skill architecture allows for continuous evolution, as new vulnerability classes can be added without rewriting core logic. For the individual researcher, the `/autopilot` command offers a competitive edge, systematically covering attack surfaces while the human focuses on complex, chained vulnerabilities. However, the tool’s effectiveness is directly tied to the quality of its underlying AI model; local models like Qwen2.5, while functional, may lag behind frontier models in nuanced reasoning. The reliance on command-line interfaces, while powerful, presents a steeper learning curve for those accustomed to GUI-based tools. Ultimately, BugHunter sets a powerful precedent: the future of bug bounty will not be human versus machine, but human–machine teams leveraging autonomous AI agents like these.
Prediction:
- +1 The integration of local, resource-efficient LLMs into tools like BugHunter will drive a surge in “blue-collar” offensive AI agents that are privately hosted, auditable, and specifically tuned for compliance-heavy environments.
- -1 The widespread availability of such powerful automation lowers the barrier to entry not just for ethical hackers, but also for less-scrupulous actors, potentially leading to a temporary increase in low-effort, automated attacks against unprepared targets.
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