AI_Generated_Code_Audit
01 // THE_NEURAL_VULNERABILITY_BIAS
The adoption of AI-powered development tools like GitHub Copilot, Cursor, and Claude Dev has accelerated software delivery to unprecedented speeds. However, this velocity introduces a critical threat: Neural Vulnerability Bias.
LLMs are not security researchers; they are probabilistic pattern matchers. If the training data contains 1,000 examples of insecure SQL queries and only 100 examples of parameterized ones, the AI is statistically predisposed to suggest the insecure pattern unless perfectly prompted.
02 // COMMON_AI_CODING_FLAWS
In our research at SentinelScan, we've identified that ai coding flaws typically manifest in three specific areas:
Input Sanitization
"AI often skips server-side validation for 'efficiency' during prototyping vibes."
Cryptographic Weakness
"Generating MD5 or SHA-1 hashes for passwords because they are 'common' in old repos."
Lazy Error Handling
"Detailed stack traces returned to the client, disclosing DB paths and secrets."
03 // THE_STALE_DATA_TRAP
AI models have a knowledge cutoff. When you prompt for a "secure file uploader," the AI might suggest a library that was secure in 2023 but has a known Remote Code Execution (RCE) vulnerability in 2026. This "Stale Data Trap" is one of the most common ai coding vulnerabilities in modern SaaS startups.
04 // BROKEN_ACCESS_IN_AI_LOGIC
AI is excellent at writing functional code but poor at writing context-aware authorization. For instance, when asked to "build a dashboard," an AI might create a React component that hides buttons using CSS (\`display: none\`) for non-admins, but fails to implement the backend check on the API endpoint.
[SECURITY_ALERT]: In 60% of vibe-coded projects audited by SentinelScan, we found at least one instance of "Client-Side Only Authorization" where sensitive data was still transmitted to the browser and merely hidden from view.
05 // HOW_TO_AUDIT_AI_AUTHORED_CODE
Auditing AI code requires a different mindset than human review. Humans make "creative" mistakes; AI makes "statistical" ones.
- A
Dependency Check: Verify every package version suggested by the AI against Snyk or GitHub Advisory databases.
- B
Flow Tracing: Manually trace every data input point from the HTTP request down to the database sink.
- C
Sentiment Analysis: Look for 'convenience' comments like \`// TODO: Secure this later\` or \`// Use simple check for now\`. AI often inserts these when the prompt is too simple.
06 // THE_AI_CODE_ACCEPTANCE_CHECKLIST
_VERIFICATION_REQUIRED
[ ] Confirm AI did not use hardcoded secret values for testing.
[ ] Validate that NoSQL/SQL queries use bindings, not direct string interpolation.
[ ] Scan the output for "Ghost Logic"—features the AI added that weren't requested.
[ ] Verify that CORS policies are not set to wildcard (*) by default.
[ ] Submit your public URL to SentinelScan for an automated audit.
* Article intended for CTOs, Lead Engineers, and Security Researchers navigating the transition to AI-first development.