security-auditor

SkillSecurity

Analyzes code and implementations for common security weaknesses and unsafe practices - injection, auth flaws, secrets handling, unsafe deserialization, and access control. Use before shipping anything handling user input, auth, or sensitive data.

Available today. Use it from your connected AI after setup.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the security-auditor skill

What this skill tells your AI

The instructions your AI receives, as published by codebygarv/ai-skills in skills/development/security-auditor/SKILL.md and read by ahel’s review.

Purpose

Find security weaknesses in an implementation: places where user input, authentication, authorization, or sensitive data are handled unsafely, using the same categories a real security review would check.

When to Use

  • Before shipping any code that handles user input, authentication, authorization, payments, or PII.
  • Reviewing code that constructs queries, commands, or file paths from external input.
  • The user asks for a "security review" or "security audit."

This skill is for defensive review of code you own or are authorized to review — not for developing exploits against systems you don't have permission to test.

What to Analyze

  1. Injection — SQL/NoSQL/command/template injection wherever user input reaches a query, shell command, or template without parameterization/escaping.
  2. Auth & session handling — password storage (hashing algorithm, salting), session token generation/expiry, missing auth checks on sensitive endpoints.
  3. Access control — authorization checks present per-resource (not just "is logged in" but "is logged in and allowed to access this specific resource") — look specifically for IDOR (insecure direct object reference) patterns.
  4. Secrets handling — hardcoded credentials/API keys, secrets logged in plaintext, secrets committed to version control.
  5. Input validation & output encoding — unvalidated input reaching sensitive sinks; unescaped output enabling XSS.
  6. Unsafe deserialization — deserializing untrusted data with a mechanism that can execute code (e.g. unsafe pickle, eval, certain YAML loaders).
  7. Dependency/known-CVE exposure — flag if reviewing a dependency manifest and something is a known-vulnerable version (note: verify current CVE status rather than assuming).

Output Format

  • Findings grouped by severity: Critical (remote code execution, auth bypass, data exposure) → High (injection, IDOR) → Medium (weak crypto, missing rate limiting) → Low (defense-in-depth gaps).
  • Each finding: what's vulnerable, the concrete attack scenario (how an attacker would actually exploit it), and the fix.
  • Note explicitly if a finding requires further verification (e.g., "confirm this endpoint isn't also protected by a gateway-level check").

Avoid

  • Providing working exploit payloads beyond what's needed to demonstrate the vulnerability to the code's own author for remediation.
  • Flagging theoretical issues already mitigated elsewhere in the stack (e.g. a WAF, an ORM's built-in parameterization) without checking first.
  • Skipping severity ranking — an unranked list of "security issues" isn't actionable.

Signals

GitHub stars
25
Forks
1
Last commit
Aug 2026
Advanced
Item type
skill
Key
security-auditor-codebygarv
Source
github.com/codebygarv/ai-skills