Running Security Audits on AI-Generated Code with Remocode

Why AI-Generated Code Needs Security Audits

AI coding agents write functional code quickly, but they don't always prioritize security. An agent focused on implementing a feature might skip input validation, use insecure defaults, or expose sensitive data in error messages. Remocode's security audit feature catches these issues before they reach production.

The Audit Command

Send audit <name> via Telegram, where the name matches a pane running your coding agent. The AI performs a comprehensive security analysis of the code visible in that terminal, checking for common vulnerability categories.

What the Audit Checks

The security audit covers seven major vulnerability categories:

Input Validation

The audit looks for missing or insufficient input validation — form fields that accept any input, API endpoints that don't sanitize parameters, and file uploads without type checking. Input validation gaps are one of the most common vulnerabilities in AI-generated code because agents often focus on the happy path.

Authentication Gaps

Missing auth checks on protected routes, endpoints that should require authentication but don't, and session management issues. The audit flags any route or function that handles sensitive data without verifying the user's identity.

Exposed Secrets

Hardcoded API keys, database passwords in configuration files, tokens in source code, and credentials that should be in environment variables. AI agents sometimes embed secrets directly in code during development, and the audit catches these before they're committed.

SQL Injection

String concatenation in database queries, missing parameterized queries, and ORM misuse that could allow SQL injection. The audit checks for both obvious cases (direct string interpolation) and subtle ones (dynamic query building).

Cross-Site Scripting (XSS)

Unescaped user input rendered in HTML, missing Content-Security-Policy headers, and DOM manipulation with unsanitized data. The audit covers reflected, stored, and DOM-based XSS vectors.

Command Injection

Unsanitized input passed to shell commands, exec() calls with user-controlled data, and path traversal vulnerabilities. Particularly relevant when AI agents write backend code that interacts with the operating system.

Insecure Defaults

Default passwords, disabled HTTPS, permissive CORS configurations, debug mode left enabled, and overly broad file permissions. These are settings that work fine in development but create security holes in production.

Severity Levels

Each finding is assigned a severity level:

• ●CRITICAL — immediate exploitation risk, requires urgent fix (e.g., exposed database credentials)
• ●HIGH — significant vulnerability that should be fixed before deployment (e.g., SQL injection)
• ●MEDIUM — security weakness that should be addressed in the current sprint (e.g., missing rate limiting)
• ●LOW — minor improvement recommended (e.g., verbose error messages in production)

Overall Security Rating

After analyzing all findings, the audit assigns an overall security rating from A to F:

• ●A — no significant issues found
• ●B — minor issues only, low risk
• ●C — some medium-severity issues need attention
• ●D — high-severity issues present
• ●F — critical vulnerabilities detected, do not deploy

This rating gives you a quick go/no-go signal for deployment decisions.

Building a Security Audit Workflow

Here's a recommended workflow for teams using AI coding agents:

• ●During development: Run audit periodically as agents complete features
• ●Before PR review: Run a final audit and include the results in the pull request description
• ●Pre-deployment: Mandatory audit with a minimum B rating required for production deployment
• ●Post-incident: Audit the affected codebase to check for related vulnerabilities

Audit Limitations

The security audit analyzes code visible in the terminal output. For the most thorough results, ensure the relevant source files have been recently displayed or modified in the pane. The audit works best when the AI agent has been actively editing the files in question, as the terminal history will contain the most recent code state.

The audit is a first-pass security check, not a replacement for dedicated security tools like SAST scanners or penetration testing. Use it as an early warning system that catches common issues while the code is still being written.