Catching Hallucinations in AI-Generated Code

AI code hallucinations occur when AI assistants generate plausible but nonexistent packages, APIs, methods, configurations, or logic, often with enough confidence and readability to evade casual review. These errors can cause runtime failures, incorrect behavior, and supply-chain risks such as slopsquatting, in which attackers register fabricated package names and distribute malicious code to developers who install them. Hallucinations arise because language models predict likely text patterns rather than verify facts, particularly when information is outdated, limited, niche, or unsupported by execution feedback. Detecting them requires validating dependencies against real registries, checking APIs against official documentation and type definitions, compiling and linting code, and executing meaningful tests that fail when behavior is broken. Recommended safeguards include pinned dependencies, automated CI checks for compilation, scanning, and testing, grounded prompts using accurate project context, and human accountability for reviewing AI-generated changes.

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