AI Agent Introduced a Security Vulnerability: Triage and Fix Guide

When an AI agent introduces a security vulnerability into your codebase — SQL injection, hardcoded credentials, disabled authentication, exposed endpoints, or insecure configurations — you need to identify, assess, and remediate the issue before it is exploited. SafeClaw by Authensor reduces this risk by gating what files agents can modify and blocking writes to security-critical code paths through deny-by-default policies. If a vulnerability has already been introduced, follow the triage process below.

Triage: Assess Severity

Step 1: Identify What the Agent Changed

# List all files modified by the agent
git log --author="agent" --name-only -10
Or find recent changes
git log --oneline -10
git diff <before-agent> <after-agent> --stat

Step 2: Classify the Vulnerability

Critical (Immediate fix required):

• Hardcoded secrets or credentials in code


• Authentication bypass (disabled auth checks, removed middleware)


• SQL injection (unsanitized inputs in queries)


• Remote code execution (eval, exec with user input)


• Exposed admin endpoints without authentication

• Cross-site scripting (XSS) via unsanitized output


• Insecure deserialization


• Path traversal vulnerabilities


• Missing authorization checks on API endpoints


• CORS misconfiguration allowing any origin

• Information disclosure (verbose error messages, stack traces)


• Insecure random number generation for security purposes


• Missing rate limiting on sensitive endpoints


• Weak cryptographic algorithms

• Missing security headers


• Verbose logging that might expose minor details


• Suboptimal but not exploitable patterns

Step 3: Check If the Vulnerability Is Deployed

If the vulnerable code has been deployed to production, prioritize the fix. If it is only in a feature branch, you have more time but should still fix it before merge.

Fix the Vulnerability

Quick Revert (If the Change Is Recent)

git revert <agent-commit-hash>
git push origin <branch-name>

Targeted Fix (If the Agent's Change Has Other Good Parts)

Keep the beneficial changes, fix only the security issue:

# Review the specific vulnerable code
git diff <agent-commit> -- path/to/vulnerable/file

Edit the file to fix the vulnerability
Then commit the fix
git add path/to/vulnerable/file
git commit -m "Fix: remediate security vulnerability from agent changes"

Run Security Scanning

After fixing, verify with automated tools:

# npm audit for dependency vulnerabilities
npm audit

Static analysis
npx eslint --rule 'security/*' src/

If using Snyk
snyk test

If using Semgrep
semgrep --config auto src/

Common Vulnerabilities AI Agents Introduce

Hardcoded Credentials

The agent writes API keys or passwords directly in code:

// BAD - agent wrote this
const apiKey = "sk-live-abc123...";

// FIX
const apiKey = process.env.API_KEY;

Disabled Authentication

The agent comments out or removes auth middleware:

// BAD - agent removed auth
app.get('/admin', adminController);

// FIX - restore auth middleware
app.get('/admin', requireAuth, requireAdmin, adminController);

SQL Injection

The agent uses string concatenation in queries:

// BAD - agent wrote this
const query = SELECT * FROM users WHERE id = ${userId};

// FIX - use parameterized queries
const query = SELECT * FROM users WHERE id = $1;
const result = await db.query(query, [userId]);

Exposed Debug Endpoints

The agent adds debug routes that expose internal state:

// BAD - agent added this
app.get('/debug/env', (req, res) => res.json(process.env));

// FIX - remove it entirely

Review the Audit Trail

npx @authensor/safeclaw audit --filter "action:file.write" --last 30

SafeClaw's hash-chained audit trail shows every file the agent modified. Cross-reference with the vulnerability to understand how it was introduced.

Install SafeClaw and Prevent Future Vulnerabilities

npx @authensor/safeclaw

Protect Security-Critical Code

Add to your safeclaw.policy.yaml:

rules:
  # Block modifications to authentication code
  - action: file.write
    resource: "/src/auth/**"
    effect: deny
    reason: "Auth code requires human security review"

- action: file.write
    resource: "/src/middleware/auth*"
    effect: deny
    reason: "Auth middleware requires human review"

# Block modifications to security configuration
  - action: file.write
    resource: "/src/config/security*"
    effect: deny
    reason: "Security config requires human review"

- action: file.write
    resource: "/src/config/cors*"
    effect: deny
    reason: "CORS config requires human review"

# Block modifications to database query layers
  - action: file.write
    resource: "/src/db/**"
    effect: deny
    reason: "Database layer requires human review"

# Block modifications to API route definitions
  - action: file.write
    resource: "/src/routes/**"
    effect: deny
    reason: "Route definitions require security review"

# Allow writing to safe areas
  - action: file.write
    resource: "/src/components/**"
    effect: allow
    reason: "UI components are lower risk"

- action: file.write
    resource: "/tests/**"
    effect: allow
    reason: "Test files are safe to modify"

Block Credential Hardcoding

rules:
  - action: file.write
    resource: "*/.env"
    effect: deny
    reason: "Env files blocked"

- action: file.read
    resource: "*/.env"
    effect: deny
    reason: "Prevent agents from reading secrets to hardcode them"

Prevention

AI agents generate code that looks correct but may contain subtle security flaws. SafeClaw's deny-by-default model limits what an agent can modify, ensuring security-critical code paths remain human-reviewed. The 446-test suite validates file gating across Claude and OpenAI integrations. Combine SafeClaw with static analysis (Semgrep, ESLint security rules) and code review to catch what agents miss.

Related Resources

• AI Agent Made Unexpected File Changes: Recovery
• Scenario: Agent Created Backdoor
• AI Agent Changed File Permissions: Restore and Prevent
• Pattern: Defense in Depth for Agents
• Best Practices: Securing AI Agents