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/phuoctrung-ppt/ai-sdlc-workflow/002-security-guardrailsgit clone --depth 1 https://github.com/phuoctrung-ppt/ai-sdlc-workflowWrote 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/phuoctrung-ppt/ai-sdlc-workflow/002-security-guardrails)<a href="https://agentmods.dev/rules/phuoctrung-ppt/ai-sdlc-workflow/002-security-guardrails"><img src="https://agentmods.dev/badge/rules/phuoctrung-ppt/ai-sdlc-workflow/002-security-guardrails.svg" alt="Measured on agentmods" 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 | $0.00348 | $0.00348 |
| Opus 5 | $0.00174 | $0.00174 |
| Sonnet 5 | $0.00070 | $0.00070 |
| Haiku 4.5 | $0.00035 | $0.00035 |
Grade A, and why
002-security-guardrails 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 6d 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.
What it actually says
Security Guardrails
Details: AGENTS.md §5–§6. Never hardcode secrets.
Auth
- App: JWT 15m + refresh 7d (HTTP-only cookie) + OAuth2
- Admin: JWT 30m + refresh 2h + TOTP MFA + IP whitelist; port 3002, internal network only
- RBAC:
@Roles()+@Permissions(); admin uses@SuperAdmin()where required - Rate limits: default 10/60s, auth 5/60s, admin 30/60s
Data Protection
- PII encrypted at rest (AES-256) and in transit (TLS 1.3)
- Passwords: bcrypt 12 rounds (app); min 16 chars + complexity (admin)
- Uploads: max 10MB, MIME validation; TypeORM parameterized queries only
- XSS: DOMPurify for user HTML; CORS whitelist (no
*in prod); Helmet in production
Secrets
- Env vars + Vault/Docker secrets only;
.env.exampleat root; app refuses start if missing - No API keys in frontend (except public keys like Stripe publishable)
Admin
- Admin DB user: SELECT on app schema, no INSERT/UPDATE/DELETE
- All admin actions →
admin.admin_audit_logswith before/after - Internal API:
X-Internal-Keyheader, not in Swagger
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.
- 6d ago First seen · 35 lines · 348 tokens per session scan A c69472e1eee3
002-security-guardrails is a cursor rule published in the GitHub repository phuoctrung-ppt/ai-sdlc-workflow (2 stars, last pushed 20d ago), licensed MIT. It adds 348 tokens to every session, about $0.0017 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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