Security in AI-assisted development: auditing LLM-generated code

AI-generated code is fast, but it isn't secure by default. We audit code generated by GitHub Copilot, ChatGPT, Claude and other tools to catch the vulnerabilities LLMs don't see.

Why does AI-generated code introduce security risks?

LLMs generate plausible code, not secure code. Recent studies show that up to 40% of code generated by GitHub Copilot contains security vulnerabilities. Models reproduce insecure patterns present in their training data, don't understand the security context of your specific application, and can't reason about the business impact of a vulnerability. "Vibe coding" — developing without understanding the code the AI generates — amplifies these risks exponentially.

What we audit

  • SQL, NoSQL, LDAP and command injections generated by LLMs
  • Insecure handling of secrets and hardcoded credentials
  • Weak authentication and authorization (broken auth, IDOR, privilege escalation)
  • Insufficient input validation and XSS in generated code
  • Insecure dependencies suggested by AI (supply chain risk)
  • Insecure cryptography patterns (obsolete algorithms, reused IVs)
  • Sensitive data exposure in logs, errors and API responses
  • Race conditions and concurrency issues in async code
  • OWASP Top 10 2021 + OWASP ASVS as the evaluation criteria

Audit process

  1. Code origin analysis: We identify which parts of the codebase were AI-generated and which tools were used (Copilot, Cursor, ChatGPT, etc.).
  2. AI-augmented SAST: Automated static analysis with specialized tools, followed by manual review by experts with context on typical LLM vulnerabilities.
  3. Dependency review: Analysis of every AI-suggested dependency: active CVEs, maintenance status, licenses and supply chain risks.
  4. Dynamic testing: Validation of the vulnerabilities found in the development or staging environment.
  5. Report and training: A findings report plus a training session for the team on the risks of AI-generated code and best practices.

Deliverables

  • Code security report with findings classified by OWASP ASVS
  • List of insecure dependencies with recommended alternatives
  • Secure prompting guide for the LLMs your team uses
  • Training session: vibe coding risks and how to mitigate them
  • Security checklist to integrate into your code review process

Use cases

  • Startups that built their MVP with GitHub Copilot or Cursor
  • Teams using ChatGPT or Claude to generate production code
  • Companies that outsourced development and want to audit the delivered code
  • CTOs who want to establish a secure AI-assisted development process
  • Startups before launch or fundraising that need security guarantees

Frequently asked questions

Is AI-generated code less secure than code written by humans?

Not necessarily, but it introduces different risks. LLMs are very good at reproducing known patterns, but that includes insecure patterns too. The biggest risk is "vibe coding": the developer doesn't understand the code and can't identify the security issues.

Do you need access to the full repository?

Yes, a complete analysis requires read access to the source code. We work with GitHub, GitLab and Bitbucket, and we sign an NDA before any access.

Can you audit code generated with Cursor, Devin or other AI tools?

Yes. The specific AI generation tool matters less than the vulnerability pattern in the resulting code. We evaluate the code regardless of which tool generated it.