hackerone

HackerOne Bug Bounty Hunting

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Install skill "hackerone" with this command: npx skills add transilienceai/communitytools/transilienceai-communitytools-hackerone

HackerOne Bug Bounty Hunting

Automates HackerOne workflows: scope parsing → parallel testing → PoC validation → submission reports.

Quick Start

  1. Input: HackerOne program URL or CSV file
  2. Parse scope and program guidelines
  3. Deploy Pentester agents in parallel (one per asset)
  4. Validate PoCs (poc.py + poc_output.txt required)
  5. Generate HackerOne-formatted reports

Workflows

Option 1: HackerOne URL

  • Fetch program data and guidelines
  • Download scope CSV
  • Parse eligible assets
  • Deploy agents in parallel
  • Validate PoCs
  • Generate submissions

Option 2: CSV File

  • Parse CSV scope file
  • Extract eligible_for_submission=true assets
  • Collect program guidelines
  • Deploy agents
  • Validate and generate reports

Scope CSV Format

Expected columns:

  • identifier

  • Asset URL/domain

  • asset_type

  • URL, WILDCARD, API, CIDR

  • eligible_for_submission

  • Must be "true"

  • max_severity

  • critical, high, medium, low

  • instruction

  • Asset-specific notes

Use tools/csv_parser.py to parse.

Agent Deployment

Pentester Agent per asset:

  • Passes program-specific guidelines

  • Tests all vulnerability types

  • Returns validated findings with PoCs

Parallel Execution:

  • 10 assets = 10 Pentester agents

  • Each spawns 30+ specialized agents

  • Total: 300+ concurrent tests

  • Time: 2-4 hours vs 20-40 sequential

PoC Validation (CRITICAL)

Every finding MUST have:

  • poc.py

  • Executable exploit script

  • poc_output.txt

  • Timestamped execution proof

  • workflow.md

  • Manual steps (if applicable)

  • Evidence screenshots/videos

Experimentation: Test edge cases, verify impact, document failures.

Report Format

Required sections (HackerOne standard):

  • Summary (2-3 sentences)

  • Severity (CVSS + business impact)

  • Steps to Reproduce (numbered, clear)

  • Visual Evidence (screenshots/video)

  • Impact (realistic attack scenario)

  • Remediation (actionable fixes)

Use tools/report_validator.py to validate.

Output Structure

Per OUTPUT.md - Bug Bounty format:

outputs/<program>/ ├── findings/ │ ├── finding-001/ │ │ ├── report.md # HackerOne report │ │ ├── poc.py # Validated PoC │ │ ├── poc_output.txt # Proof │ │ └── workflow.md # Manual steps ├── reports/ │ ├── submissions/ │ │ ├── H1_CRITICAL_001.md # Ready to submit │ │ └── H1_HIGH_001.md │ └── SUBMISSION_GUIDE.md └── evidence/ ├── screenshots/ └── http-logs/

Program Selection

High-Value:

  • New programs (< 30 days)

  • Fast response (< 24 hours)

  • High bounties (Critical: $5,000+)

  • Large attack surface

Avoid:

  • Slow response (> 1 week)

  • Low bounties (Critical: < $500)

  • Overly restrictive scope

Critical Rules

MUST DO:

  • Validate ALL PoCs before reporting

  • Sanitize sensitive data

  • Test only eligible_for_submission=true assets

  • Follow program-specific guidelines

  • Generate CVSS scores

NEVER:

  • Report without validated PoC

  • Test out-of-scope assets

  • Include real user data

  • Cause service disruption

Quality Checklist

Before submission:

  • Working PoC with poc_output.txt

  • Accurate CVSS score

  • Step-by-step reproduction

  • Visual evidence

  • Impact analysis

  • Remediation guidance

  • Sensitive data sanitized

Tools

  • tools/csv_parser.py

  • Parse HackerOne scope CSVs

  • tools/report_validator.py

  • Validate report completeness

  • /pentest skill - Core testing functionality

  • Pentester agent - Orchestrates testing

Integration

Uses /pentest skill and Pentester agent. Follows OUTPUT.md for submission format.

Common Rejections

Out of Scope: Check eligible_for_submission=true

Cannot Reproduce: Validate PoC, include poc_output.txt Duplicate: Search disclosed reports, submit quickly Insufficient Impact: Show realistic attack scenario

Usage

/hackerone <program_url_or_csv_path>

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

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