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/aiagentwithdhruv/ai-dev-stackWrote 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/aiagentwithdhruv/ai-dev-stack/70-security)<a href="https://agentmods.dev/rules/aiagentwithdhruv/ai-dev-stack/70-security"><img src="https://agentmods.dev/badge/rules/aiagentwithdhruv/ai-dev-stack/70-security/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/aiagentwithdhruv/ai-dev-stack/70-security"><img src="https://agentmods.dev/badge/rules/aiagentwithdhruv/ai-dev-stack/70-security.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 | $0.00236 | $0.00236 |
| Opus 5.5 | $0.00094 | $0.00094 |
| Sonnet 5.5 | $0.00047 | $0.00047 |
| Haiku 4.5 | $0.00024 | $0.00024 |
Grade A, and why
70-security 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 1mo 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 rules:
- Never hardcode secrets, API keys, tokens, credentials, or private URLs.
- Never log passwords, tokens, raw secrets, or sensitive user content.
- Validate and sanitize all user inputs.
- Treat uploads, URLs, prompts, and external content as untrusted.
- Enforce authentication and authorization checks on protected resources.
- Use least-privilege access patterns.
- Add rate limiting to sensitive or expensive endpoints.
- Use secure defaults.
AI-specific security:
- Add prompt injection resistance where relevant.
- Validate tool inputs and outputs.
- Restrict tool access by policy.
- Protect system instructions and internal configuration.
- Handle unsafe model output before returning it to the user.
Infra/security hygiene:
- Prefer environment variables and secret managers.
- Avoid broad wildcard permissions in cloud configs.
- Do not expose internal services publicly unless required.
Do not:
- Assume client-side validation is enough.
- Return internal error details to end users in production.
- Store plaintext passwords or tokens in databases.
- Use default credentials or weak secrets in any environment.
- Disable HTTPS or certificate validation.
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.
- 1mo ago First seen · 34 lines · 236 tokens per session scan A afa1d6d1280e
70-security is a cursor rule published in the GitHub repository aiagentwithdhruv/ai-dev-stack (11 stars, last pushed 3mo ago), licensed MIT. It adds 236 tokens to every session, about $0.0009 per session on Opus 5.5. 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.
Other cursor rules, from other repositories
cursorrules
You are building an AI/ML project with Python. The project uses PyTorch for model training, handles data pipelines with proper validation, tracks experiments systematically, and follows production ML engineering practices. Code is type-hinted, tested, and reproducible.
refactoring
Refactoring: systematic approach, extract/inline, guard clauses, early returns.
cross-tool-config
Cross-tool AI config: what transfers between Cursor, Claude Code, Copilot, Windsurf, Gemini, and Codex.
angular
Angular: signals, standalone components, RxJS patterns.
django
Django: models, views, ORM best practices.
fastapi
FastAPI: Pydantic v2, dependency injection, async.