Ai Code Security Auditor

    1

    Audits code and LLM integrations for security vulnerabilities, exploit paths, and agent-specific risks.

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    2 installsSecurity scanned

    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    Ai Code Security Auditor

    Example session with this skill installed

    Audit the new authentication middleware and the OpenAI tool-calling logic I just wrote for security vulnerabilities. Is this safe to deploy?

    • Read your context and instructions
    • Compiled the ai code security
    • Generated the document

    I have completed a security audit of the new authentication middleware and the LLM tool-calling logic. While the basic JWT validation is sound, there are two high-severity issues related to tool permissions and error logging.

    Security Audit — Auth & LLM Integration

    Critical

    [src/auth/middleware.ts:24] Sensitive data leak in logs — The middleware logs the entire req.headers object when a token fails validation, which includes the raw Authorization header. Concretely, an attacker or a system admin with log access could harvest valid tokens from failed attempts due to unrelated malformed headers.
    Fix:

    // Change this:
console.error("Auth failed:", req.headers);
// To this:
console.error("Auth failed for user-agent:", req.headers['user-agent']);

    High

    [src/agents/openai_tools.ts:88] Excessive tool permissions — The execute_shell tool is exposed to the LLM without a restricted allowlist or path validation. An attacker could use prompt injection to trick the model into executing rm -rf / or exfiltrating .env files.
    Fix:

    const ALLOWED_COMMANDS = ['ls', 'git status', 'npm test'];
if (!ALLOWED_COMMANDS.includes(input.command)) {
  throw new Error("Unauthorized command execution");
}

    Medium / Low

    [src/auth/middleware.ts:12] Weak JWT secret — The code uses a hardcoded default string for development. Ensure this is pulled from a secure environment variable in production.

    Next steps

    1. Apply the log masking fix to middleware.ts immediately.
    2. Implement the command allowlist in the tool-calling logic.
    3. Verify that your production environment variables are properly configured for the JWT secret.

    ai-code-security-auditor.pdf

    PDF · document

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Scan for hardcoded secrets and weak crypto before deployment.Identify prompt injection risks in agentic tool-calling logic.Verify authentication and authorization integrity in new features.Generate actionable security reports with specific code fixes.

    About this skill

    The problem

    AI coding agents often prioritize functional code over secure code, frequently introducing predictable vulnerabilities like hardcoded secrets, injection points, and unsafe defaults. Standard linters catch syntax errors but miss the logic-based exploit paths that lead to production breaches.

    What it does

    • Executes a deterministic pattern scan for hardcoded secrets, weak cryptography, and unsafe deserialization.
    • Audits authentication flows, authorization checks, and IDOR vulnerabilities against a specialized security checklist.
    • Identifies agent-specific risks including prompt injection surfaces and excessive tool permissions in LLM integrations.
    • Generates structured reports with concrete exploit paths and copy-pasteable code fixes.
    • Classifies findings by severity to help developers prioritize critical patches over best-practice deviations.

    Frameworks & tools

    Works across all languages and frameworks, with specialized focus on LLM integrations, MCP servers, and common web backends (Node.js, Python, Go).

    Why this beats prompting it yourself

    Generic security prompts often produce "hallucinated" vulnerabilities or vague advice. This skill follows a rigorous multi-step workflow—scanning first, then verifying exploitability against the specific codebase context—to ensure every reported issue is a real threat, not a false positive.

    Use cases

    • Reviewing generated code from Claude Code or Cursor before merging a PR.
    • Auditing LLM-powered applications for prompt injection and insecure tool-calling.
    • Hardening authentication and payment processing logic in new features.
    • Rapidly scanning a legacy repo for low-hanging security fruit like debug flags or wildcard CORS.

    Known limitations

    Requires access to the local filesystem to run the initial pattern scanner. Best results come from auditing scoped diffs rather than massive, multi-million line monorepos at once.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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    Security scanned

    Passed our 8-point scan before listing

    2 installs

    Downloaded by developers to date

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    Trust & safety

    Security scanned

    Verified clean 24 days ago

    • Passed all security checks, Safe to install

    Listed24 days ago
    Updated12 days ago

    What's inside

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