ai-threat-testing
Offensive AI security testing and exploitation framework. Systematically tests LLM…
HackerOne bug bounty automation - parses scope CSVs, deploys parallel pentesting agents per asset, validates PoCs, and generates platform-ready submission reports.
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HackerOne bug bounty automation - parses scope CSVs, deploys parallel pentesting agents per asset, validates PoCs, and generates platform-ready submission reports.
name: hackerone description: HackerOne bug bounty automation - parses scope CSVs, deploys parallel pentesting agents per asset, validates PoCs, and generates platform-ready submission reports.
Automates: scope parsing → parallel testing per asset → **authoritative finding validation** → submission reports.
1. Input: HackerOne program URL or scope CSV. 2. Parse scope and program guidelines. 3. Spawn one coordinator per eligible asset (parallel). 4. Each coordinator runs the standard engagement flow (see `skills/coordination/SKILL.md`). 5. Run the **`validate-findings` workflow per asset** (authoritative submission gate). Generate HackerOne reports from the validated set ONLY — never submit a finding that is not `VALID`/`REPAIRED`.
Expected columns:
Parse with `skills/hackerone/tools/csv_parser.py`. Filter for `eligible_for_submission=true`.
One coordinator per asset, spawned in parallel:
coordinator_role = Read("skills/coordination/SKILL.md")
Agent(prompt=f"{coordinator_role}\n\nTARGET: {asset_url}\nSCOPE: {program_guidelines}\nOUTPUT_DIR: ...",
run_in_background=True)10 assets → 10 parallel coordinators (~2-4 h vs 20-40 h sequential). Each coordinator follows `skills/coordination/SKILL.md` and `reference/role-matrix.md`.
Every finding requires `poc.py` (executable exploit), `poc_output.txt` (timestamped execution proof), manual repro steps, and evidence (screenshots / HTTP captures / video). This is the input the validator consumes.
**After each asset's coordinator returns, the orchestrator runs the `validate-findings` workflow for that asset and submits ONLY findings it marks `VALID` or `REPAIRED`:**
v = Workflow(name="validate-findings", args={
"output_dir": asset_output_dir, # the coordinator's OUTPUT_DIR
"target": asset_url,
"business_tier": "revenue", # production/external bug-bounty scope; else "unknown"
"votes": 3, # adversarial refuters per finding (bounty = stricter)
"repair": True, # regenerate broken/absent PoCs so each finding has a runnable evidence script
"strict": True, # one failed gate => REJECTED
})
# v.counts -> {total, valid, repaired, rejected}; v.validated[] / v.rejected[]
# Verdicts at {asset_output_dir}/artifacts/validated/{id}.json (+ false-positives/).
# Asset report at {asset_output_dir}/reports/validation-report.md.`validate-findings` is the authoritative gate because it directly preempts the most common HackerOne rejections: it verifies each CVE against **NVD + a recomputed CVSS base score (from the vector) + CISA KEV + the vendor advisory**, **runs (and repairs) every PoC** until it emits the evidence that proves the issue, **recomputes the risk/severity**, corroborates every claim against raw evidence, and kills false positives via **adversarial refutation**.
The orchestrator is the only layer that runs this workflow (it is top-level; the workflow is one level below — legal nesting). To avoid double work, coordinators may treat their **interleaved per-finding validators** (run the instant each candidate is materialized) as a provisional self-check; the standalone `validate-findings` verdict is authoritative for submission. A `REJECTED` finding is a false positive — it never enters a submission, an appendix, or a count; its `artifacts/false-positives/{id}.json` is the sole record. Likewise a coordinator-dropped candidate (uncured DEMOTED, in `artifacts/dropped/`) is never submitted.
The HackerOne PoC contract is a superset of the standard finding contract (`skills/coordination/reference/validator-role.md`).
Build each report from a validated finding only. Required sections per HackerOne standard: 1. Summary (2-3 sentences). 2. Severity (CVSS + business impact; v4.0 primary, v3.1 → v3.0 → v2.0 fallback) — use the score `validate-findings` recomputed from the vector and the risk bucket it assigned, not a hand-typed number. 3. Steps to Reproduce (numbered, clear) — mirror the validated `verification-script.py`. 4. Visual Evidence (from `evidence/validation/`). 5. Impact (realistic attack scenario). 6. Remediation (actionable fixes).
Validate report *format* with `skills/hackerone/tools/report_validator.py` (complements the finding validation).
Standard `OUTPUT_DIR` (`skills/coordination/reference/output-discipline.md`) plus a per-asset `reports/submissions/` containing the platform-ready markdown. `validate-findings` writes the verdicts under `artifacts/validated|false-positives/` and the asset `reports/validation-report.md`.
{OUTPUT_DIR}/
├── findings/
│ └── finding-NNN/evidence/validation/ # validate-findings proof packages
├── reports/
│ ├── submissions/ # built ONLY from artifacts/validated/
│ │ ├── H1_CRITICAL_001.md
│ │ └── H1_HIGH_001.md
│ ├── validation-report.md # validate-findings asset report
│ └── SUBMISSION_GUIDE.md
├── recon/
├── logs/
└── artifacts/
├── validated/ # {id}.json — submit these
└── false-positives/ # {id}.json — never submit these**High-value:** new programs (< 30 days), fast response (< 24 h), high bounties, large attack surface. **Avoid:** slow response (> 1 week), low bounties, restrictive scope.
Open-source Claude Code skills and agents for AI-powered penetration testing, bug bounty hunting, AI threat testing, and security reconnaissance — from the team at Transilience.ai
Repo: transilienceai/communitytools
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