Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/stackhawk/agent-skills/stackhawk-hawkscan-false-positivesgit clone --depth 1 https://github.com/stackhawk/agent-skillsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00031 | $0.02262 |
| Opus 5 | $0.00015 | $0.01131 |
| Sonnet 5 | $0.00006 | $0.00452 |
| Haiku 4.5 | $0.00003 | $0.00226 |
Grade A, and why
stackhawk-hawkscan-false-positives scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
False Positives and Accepted Risk
Contents
- Identifying False Positives
- How to Decide: Fix or Suppress?
- Suppression via Config
- Triaging via the API
- Reporting Accepted Risk
- When in Doubt
Identifying False Positives
Not every finding from a DAST scan is a real vulnerability. Some common false positive scenarios:
- Health check or status endpoints that intentionally return server info (e.g.,
/health,/actuator/info) may trigger "Information Disclosure" findings - CORS headers set intentionally permissive for public APIs
- Deliberately open endpoints (public API docs, login pages) flagged for missing authentication
- Security headers on non-HTML responses — CSP, X-Frame-Options findings on JSON API endpoints that never serve HTML
- Rate limiting findings on endpoints that are already behind an API gateway enforcing rate limits
How to Decide: Fix or Suppress?
| Signal | Action |
|---|---|
| The finding describes real user-input handling with no sanitization | Fix it |
| The finding is on a test/mock endpoint not present in production | Suppress — exclude the path |
| The finding is on an intentionally open endpoint (health, docs) | Suppress — exclude the path |
| The finding is a header issue on a non-HTML API response | Suppress — exclude the path or accept the risk |
| You're unsure | Fix it — false negatives are worse than false positives |
Suppression via Config
Exclude specific paths from scanning
The scanner is pinned to the host: value in stackhawk.yml and will not
traverse to other hosts. You do not need to add external domains or CDN
URLs to excludePaths — the scanner won't follow them.
Use excludePaths for same-host paths that generate noise without security
value: static assets (images, CSS, JavaScript bundles), health endpoints,
API docs, and similar paths that are either not user-controllable or not
relevant to security testing.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 251 lines · 31 tokens per session scan A e568843e3f29
stackhawk-hawkscan-false-positives is a cursor rule published in the GitHub repository stackhawk/agent-skills (16 stars, last pushed 12d ago), licensed MIT. It adds 31 tokens to every session and 2,262 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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