MarinosTBH
Mohamed Amine Terbah

Show HN: AI ships your code but can't fix the CVEs it creates

February 11, 2026

Technical Analysis: AI-Generated Code and CVE Risk

The premise that AI can ship code but fails to address CVEs (Common Vulnerabilities and Exposures) it introduces is a critical observation in modern software development. Here's a breakdown of the technical realities:

1. AI's Code Generation vs. Security Awareness

  • Speed vs. Diligence: AI (e.g., GitHub Copilot, GPT-4) accelerates development by generating functional code snippets, but it lacks contextual awareness of security implications.
  • Training Data Bias: AI models are trained on public repositories, which often contain vulnerable patterns (e.g., SQLi, XSS, hardcoded secrets). Without explicit security tuning, AI reproduces these flaws.
  • No Real-Time CVE Analysis: AI does not dynamically check generated code against vulnerability databases (e.g., NVD, CWE) unless explicitly integrated with security tooling.

2. Where AI Falls Short in CVE Mitigation

  • False Confidence: Developers assume AI-generated code is "correct" because it compiles/runs, ignoring subtle security gaps (e.g., improper input sanitization).
  • Lack of Patch Awareness: AI may suggest deprecated libraries (e.g., log4j 1.x) or outdated APIs without flagging known CVEs.
  • No Threat Modeling: AI doesn’t consider attack surfaces, data flows, or privilege boundaries—key elements in secure design.

3. Mitigation Strategies

  • Static Application Security Testing (SAST): Integrate SAST tools (e.g., Semgrep, SonarQube) into CI/CD to scan AI-generated code.
  • AI + Security Tooling: Augment AI with plugins that cross-reference code against CVE databases (e.g., Snyk, Dependabot).
  • Human-in-the-Loop Reviews: Treat AI output as untrusted drafts; mandate manual review for security-critical paths (auth, data handling).

4. The Future: Can AI Fix CVEs?

  • Emerging Solutions: Some tools (e.g., Amazon CodeWhisperer’s security scanning) are adding CVE detection, but coverage is incomplete.
  • Fine-Tuning for Security: Future models could be trained on CWE/SANS Top 25 vulnerabilities to reduce unsafe patterns.
  • Runtime Protection: AI-generated code may require compensating controls (WAFs, RASP) to mitigate undetected risks.

Final Take: AI is a powerful accelerant but not a replacement for secure coding practices. Until AI models deeply internalize security principles, the responsibility remains on engineers to audit, harden, and monitor AI-assisted outputs.

Actionable Steps for Teams:

  1. Treat AI code as untrusted third-party code.
  2. Enforce SAST/DAST in pipelines.
  3. Track AI-introduced debt via SBOMs (Software Bill of Materials).

The gap between shipping code and shipping secure code remains a human challenge—AI is just another tool in the chain.

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