Analysis
The Top 10 AI Coding Quality Risks and What to Do About Them
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Why it matters
AI coding assistants introduce AI-specific supply-chain risks like slopsquatting, where attackers register hallucinated package names to inject malware into builds that trust the model's suggestions, making these mitigations directly relevant to defenders.
A QualityLogic article enumerates the top 10 quality and security risks of AI-generated code, including insecure-by-default code (a 2025 Veracode study found 45% of AI samples introduced an OWASP Top 10 vulnerability), package hallucination / 'slopsquatting' supply-chain exposure (a USENIX study found 5-22% of suggested packages were fictional across 205,000+ fake names), and silent 'plausible but wrong' correctness bugs. It pairs each risk with mitigations such as SAST/SCA gating, dependency verification, SBOMs, and behavior-based testing.