The Semantic Trap: Do Fine-tuned LLMs Learn Vulnerability Root Cause or Just Functional Pattern?

• URL: http://arxiv.org/abs/2601.22655v2
• Date: Mon, 02 Feb 2026 09:48:57 GMT
• Title: The Semantic Trap: Do Fine-tuned LLMs Learn Vulnerability Root Cause or Just Functional Pattern?
• Authors: Feiyang Huang, Yuqiang Sun, Fan Zhang, Ziqi Yang, Han Liu, Yang Liu,
• Abstract summary: We propose TrapEval, a comprehensive evaluation framework designed to disentangle vulnerability root cause from functional pattern.<n>We fine-tune five representative state-of-the-art LLMs across three model families and evaluate them under cross-dataset testing, semantic-preservings, and varying degrees of semantic gap measured by CodeBLEU.<n>Our findings serve as a wake-up call: high benchmark scores on traditional datasets may be illusory, masking the model's inability to understand the true causal logic of vulnerabilities.
• Score: 14.472036099680961
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LLMs demonstrate promising performance in software vulnerability detection after fine-tuning. However, it remains unclear whether these gains reflect a genuine understanding of vulnerability root causes or merely an exploitation of functional patterns. In this paper, we identify a critical failure mode termed the "semantic trap," where fine-tuned LLMs achieve high detection scores by associating certain functional domains with vulnerability likelihood rather than reasoning about the underlying security semantics. To systematically evaluate this phenomenon, we propose TrapEval, a comprehensive evaluation framework designed to disentangle vulnerability root cause from functional pattern. TrapEval introduces two complementary datasets derived from real-world open-source projects: V2N, which pairs vulnerable code with unrelated benign code, and V2P, which pairs vulnerable code with its corresponding patched version, forcing models to distinguish near-identical code that differs only in subtle security-critical logic. Using TrapEval, we fine-tune five representative state-of-the-art LLMs across three model families and evaluate them under cross-dataset testing, semantic-preserving perturbations, and varying degrees of semantic gap measured by CodeBLEU. Our empirical results reveal that, despite improvements in metrics, fine-tuned LLMs consistently struggle to distinguish vulnerable code from its patched counterpart, exhibit severe robustness degradation under minor semantic-preserving transformations, and rely heavily on functional-context shortcuts when the semantic gap is small. These findings provide strong evidence that current fine-tuning practices often fail to impart true vulnerability reasoning. Our findings serve as a wake-up call: high benchmark scores on traditional datasets may be illusory, masking the model's inability to understand the true causal logic of vulnerabilities.

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