AI Security
Your developers are using AI to write code. Who is auditing it?
Sherlock Forensics provides security audits for AI-generated code from GitHub Copilot, Claude, ChatGPT, Cursor and other AI assistants. Led by Ryan Purita (CISSP, ISSAP, ISSMP), audits cover hallucinated dependencies, injection flaws, hardcoded secrets, broken authentication and OWASP Top 10 compliance. Established circa 2004 in Vancouver, BC. Quick audits from $1,500 CAD.
AI code assistants produce code that compiles, passes tests and ships to production. But that code carries a class of vulnerabilities that human-written code rarely exhibits. We find them before attackers do.
Whether your team calls it AI slop, vibe code or AI-assisted development, we audit it all. Every AI code assistant produces the same classes of vulnerabilities. We have seen them across Copilot, Claude, ChatGPT, Cursor, Bolt and Lovable. The name does not matter. The security gaps are the same.
The Problem
AI assistants frequently reference packages that do not exist. Attackers register these phantom package names on npm, PyPI and RubyGems, then wait for developers to install them. A single npm install of a hallucinated dependency can deliver malware directly into your build pipeline. This is not theoretical. Researchers have documented thousands of hallucinated package names from popular AI assistants and confirmed that attackers actively exploit this vector.
AI models default to simple implementations. When generating authentication tokens, session identifiers or API keys, they routinely use Math.random() instead of crypto.getRandomValues() or Python's random module instead of secrets. The output looks random to a developer reading the code. It is trivially predictable to an attacker who understands the underlying PRNG.
AI assistants generate string-concatenated SQL queries and shell commands with alarming consistency. They produce code that works in development and demonstrates the correct logic but fails to use parameterized queries or proper input sanitization. The resulting injection vectors are invisible to developers who trust the AI output.
AI models trained on public repositories reproduce patterns they learned from training data. This includes embedding placeholder API keys, database credentials and JWT secrets directly in source files. These placeholders frequently survive code review because reviewers assume someone will replace them before deployment. They do not.
AI-generated code frequently deserializes untrusted input without validation. Python's pickle.loads(), Java's ObjectInputStream and PHP's unserialize() appear in AI output with no type checking or allowlisting. These patterns enable remote code execution when processing attacker-controlled data.
AI assistants generate authentication flows that look complete but contain critical gaps. Missing rate limiting on login endpoints, JWT tokens without expiration, password reset flows without proper token invalidation and session management that fails to rotate identifiers after privilege changes.
Scope
| AI Assistant | Common Patterns | Risk Level |
|---|---|---|
| GitHub Copilot | Hallucinated imports, inline secrets, weak crypto | High |
| Claude / Claude Code | Overly permissive configs, missing input validation | Medium-High |
| ChatGPT / GPT-4 | SQL concatenation, insecure deserialization, placeholder keys | High |
| Cursor / Windsurf / Others | Mixed patterns from underlying models | Variable |
Every AI code audit maps findings to the OWASP Top 10 framework. We test for injection, broken authentication, sensitive data exposure, XML external entities, broken access control, security misconfiguration, cross-site scripting, insecure deserialization, vulnerable components and insufficient logging.
We trace every import, require and include statement in AI-generated code against live package registries. Hallucinated packages are flagged. Existing packages are checked against the NIST National Vulnerability Database for known CVEs. Transitive dependencies are mapped and assessed.
Entropy analysis and pattern matching across the entire codebase to identify hardcoded credentials, API keys, tokens and certificates. We check git history for secrets that were committed and later removed but remain accessible in version control.
Pricing
Frequently Asked Questions
Authority Resources
Our code review team holds recognized certifications in application security.
Related
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Get Started
Quick audits from $1,500. Order online with no meetings required.
Order OnlineWhether you have a single AI-built application or an engineering team shipping Copilot-assisted code daily, we will scope an audit that matches your risk profile.
Call 604.229.1994