AI Code Security Audit

Security review tuned for AI-generated code.

A Galen©-powered audit of code produced by GPT, Claude, Copilot, or Gemini — focused on the authorization gaps, unsafe defaults, and hallucinated patterns LLMs reliably ship to production. Galen© is Telhawk's AI security reviewer; Telhawk does not run human code reviews.

Scope an AI code audit See code security audit

How AI code fails

The patterns LLMs reliably get wrong

AI accelerates feature work. It also industrializes a small set of security mistakes. A proper AI code security audit looks for those mistakes directly — not just whatever SAST happens to flag.

Missing authorization checks

LLMs generate routes and handlers that authenticate the user but never confirm they're allowed to touch the requested record. The number-one breach pattern in AI-built apps.

Insecure defaults

Wide-open CORS, disabled CSRF, permissive file uploads, plaintext secrets, and skipped input validation — because the model defaulted to the shortest working example.

Hallucinated APIs & unsafe libs

Calls to functions that don't exist, parameters used incorrectly, and obscure or unmaintained libraries pulled in to satisfy a prompt.

Copy-paste duplication

The same insecure pattern repeated across dozens of files, multiplying the blast radius of every missed check.

Why Telhawk

Galen© reviews what the model can't see in itself.

Telhawk does not staff human code reviewers. Every Telhawk audit is delivered by Galen©, our AI security reviewer built specifically to catch the patterns LLMs reliably get wrong in the code they generate.

Pattern-aware AI review

Galen© knows how GPT-5.5, Claude, Copilot, and Gemini fail. The same review catches the next twenty copies of the same mistake across the codebase.

Proof, not pattern matches

Every Galen© finding ships with a reproducible path. If Galen© can't prove it, it doesn't get reported.

Validated remediation

Galen© re-tests every fix — including AI-generated patches — to confirm the path actually closes.

Deliverables

What you get from a Telhawk AI code audit

Proof-backed finding report with reproduction steps for every issue

Map of recurring AI failure patterns across the codebase

File, line, and call-path references for fast remediation

Remediation guidance written for an AI-assisted workflow

Validation round confirming each fix closes the path

FAQ

Common questions about AI code security audits

How is this different from a normal code security audit?

Same depth, tuned for how AI-generated code fails. We look for the patterns LLMs reliably get wrong — missing authorization, insecure defaults, hallucinated APIs, and copy-paste duplication — and flag where the same mistake spread across the codebase.

Which AI coding tools do you cover?

Code produced by or with GPT-5.5, Claude, Gemini, Copilot, Cursor, and similar tools. The failure patterns are largely model-agnostic — what matters is reviewing the resulting code, not the tool that wrote it.

Do I get proof or just a list of findings?

Telhawk focuses on proof-backed findings. Where applicable, findings include affected paths, relevant data flows, missing controls, risk context, remediation guidance, and validation status after correction.

Shipping a codebase written mostly by AI?

Telhawk reviews AI-generated code the way an attacker reads it — and proves every finding before you spend engineering time on it.

Talk to an expert See AI agent security audit