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.
git clone --depth 1 https://github.com/adonai-labs/agent-runwayWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/adonai-labs/agent-runway/owasp-ai-security-privacy)<a href="https://agentmods.dev/rules/adonai-labs/agent-runway/owasp-ai-security-privacy"><img src="https://agentmods.dev/badge/rules/adonai-labs/agent-runway/owasp-ai-security-privacy/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/adonai-labs/agent-runway/owasp-ai-security-privacy"><img src="https://agentmods.dev/badge/rules/adonai-labs/agent-runway/owasp-ai-security-privacy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00515 | $0.00515 |
| Opus 5 | $0.00258 | $0.00258 |
| Sonnet 5 | $0.00103 | $0.00103 |
| Haiku 4.5 | $0.00052 | $0.00052 |
Grade A, and why
owasp-ai-security-privacy 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 10d 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OWASP AI Security & Privacy – PR Review Rules
Data Governance
- Classify inputs/outputs; prohibit uncontrolled ingestion of sensitive categories (PII, PHI, credentials).
- Minimize retention; delete intermediates; avoid storing raw prompts/outputs unless justified.
- Document data sources and consent; prefer data references over raw payloads.
Privacy by Design
- Redact data before prompts; support field-level masking and hashing.
- Provide opt-out and data deletion pathways where applicable.
- Disable training/fine-tuning on user data by default in production.
Access Control and Secrets
- Secrets in secure variables/key vault; rotate regularly; scope by environment.
- Principle of least privilege for service principals and tokens used by tools.
Monitoring & Incident Response
- Capture minimal operational telemetry (latency, tokens, errors) without sensitive content.
- Threshold alerts for spikes, cost anomalies, and failure patterns.
- Provide runbooks for key incident types (leak, poisoning, abuse, drift).
Safety & Abuse Prevention
- Add content/abuse filters for inputs/outputs appropriate to domain.
- Restrict high-risk actions (code exec, network, file writes) behind approvals.
- Sandbox tool execution; apply egress allowlists and filesystem quotas.
Model & Supply Chain Risk
- Track model/SDK versions; maintain SBOM; verify signatures/hashes when available.
- Evaluate model updates in a staging environment; document behavior changes.
Compliance & Auditability
- Log decision rationale for automated actions (without sensitive content).
- Keep configuration-as-code for prompts, tools, and safety policies.
PR Reviewer Checklist
- Sensitive data is redacted/masked; no raw secrets in code or logs.
- No long-term storage of prompts/outputs without a clear purpose.
- Access tokens scoped and stored securely; least privilege enforced.
- Telemetry avoids sensitive content; alerts configured for anomalies.
- Risky capabilities gated; sandboxing and allowlists in place.
- Dependencies/models pinned; update process documented.
- Policies and prompts are versioned and reviewable.
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.
- 10d ago First seen · 52 lines · 515 tokens per session scan A e24596877ddd
owasp-ai-security-privacy is a cursor rule published in the GitHub repository adonai-labs/agent-runway (2 stars, last pushed 20d ago), licensed MIT. It adds 515 tokens to every session, about $0.0026 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 2026-08-31.
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