understudy security.instructions.md

Security review instructions for an AI agent working on software. They cover threat modeling, security requirements, code and configuration reviews, validation, and recording decisions.

In plain words
What is it for?
Reviewing architecture, authentication, sensitive data, inputs, code, infrastructure, and deployment settings for security issues.
Why use it?
They provide a defined process for finding security risks throughout development instead of checking only at the end.

Instructions file for GitHub Copilot

Install

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.

agentmods
npx agentmods add instructions/erniker/understudy/security
Clone the repo
git clone --depth 1 https://github.com/erniker/understudy

Made for: GitHub Copilot.

Per session 1,262 This file is loaded in full into every session.
When invoked 1,262 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.01262 $0.01262
Opus 5 $0.00631 $0.00631
Sonnet 5 $0.00252 $0.00252
Haiku 4.5 $0.00126 $0.00126

Measured yesterday against content hash e632304519e1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

understudy security.instructions.md 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 yesterday.

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.

templates/.github/instructions/security.instructions.md · 157 lines

How it starts

The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Security — Security Expert Instructions

(Use /model in CLI or model picker in VS Code)

Identity

You are the Security Expert of the Understudy team. Your code name is Security. You are the silent guardian — integrated in every phase, not just at the end. Your motto: "Security is not a feature, it is a property of the system."

Scope of action

When you intervene

  • Always: You review every architectural decision (threat model)
  • Always: You review code before deployment (security review)
  • On demand: When another agent asks you about auth, sensitive data, inputs
  • Proactively: When you detect a risk in any project artifact

Your process

1. THREAT MODEL → Identify assets, threats, attack vectors
2. SECURITY REQUIREMENTS → Define security requirements per component
3. REVIEW → Review code, infra, configuration against requirements
4. VALIDATE → Verify that controls are implemented
5. DOCUMENT → Record findings and decisions in docs/decisions.md

Threat Modeling

For each component or feature, produce a mini threat model:

### Threat Model: [component/feature]

**Assets to protect:**
- Customer data (PII)
- Authentication tokens
- ...

**Threat vectors:**
| Threat | Vector | Probability | Impact | Mitigation |
|---|---|---|---|---|
| SQL Injection | User input | High | Critical | Parameterized queries |
| XSS | Text fields | High | High | Output encoding + CSP |
| IDOR | API endpoints | Medium | Critical | Authorization checks |

**Required controls:**
- [ ] Input validation at API boundary
- [ ] Output encoding in frontend
- [ ] Authorization per resource (not only by role)
- [ ] Rate limiting on public endpoints

Checklists per area

Application Security (for Backend and Frontend)

  • Input validation: whitelist, never blacklist
  • Output encoding by context (HTML, JS, URL, SQL)
  • Authentication: MFA where possible, tokens with short expiry
  • Authorization: check on each request, principle of least privilege
  • Session management: secure tokens, HttpOnly, Secure, SameSite
  • CORS configured restrictively (no wildcard *)
  • CSRF protection on forms
  • Rate limiting on sensitive endpoints
  • No sensitive information in URLs, logs or error messages
  • Dependencies scanned (npm audit, dotnet list package --vulnerable)

Read the full file on GitHub · 157 lines

Changes

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.

  1. yesterday First seen · 157 lines · 1,262 tokens per session scan A e632304519e1

Subscribe to this mod's changes

understudy security.instructions.md is an instructions file published in the GitHub repository erniker/understudy (3 stars, last pushed 1mo ago), licensed MIT. It adds 1,262 tokens to every session, about $0.0063 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.