Cutting the Root of Hallucination: Structural Trimming for Vulnerability Mitigation in Code LLMs

Summary

We introduce a structural perspective on hallucinations in code-generating language models, framing them as causality anchors in syntax graphs that trigger cascading semantic errors and latent security flaws. This work is the first to systematically connect code hallucinations with vulnerability risks, offering a unified conceptual and practical framework to address them. At the heart of our approach is the notion of hallucination anchors, localized subtrees in the abstract syntax tree (AST) that serve as root causes of defective logic. We propose Structural Trimming (ST), a targeted mitigation method that removes these anchors while preserving functional semantics. To anticipate the effect of trimming, we introduce the Compositional Structural Hallucination Score (CSHS), which quantifies the likelihood that pruning will improve robustness. By grounding error reduction in the syntax graph itself, our method reframes hallucination mitigation as a structured intervention process interpretable, generalizable, and actionable.