Do Fine-Tuned LLMs Understand Vulnerabilities? An Investigation into the Semantic Trap
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| Format: | Preprint |
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2026
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| _version_ | 1866913151603179520 |
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| author | Huang, Feiyang Sun, Yuqiang Zhang, Fan Yang, Ziqi Liu, Han Liu, Yang |
| author_facet | Huang, Feiyang Sun, Yuqiang Zhang, Fan Yang, Ziqi Liu, Han Liu, Yang |
| contents | Large Language Models (LLMs) have shown promising performance in software vulnerability detection, particularly after domain-specific Supervised Fine-Tuning (SFT). However, it remains unclear whether these models genuinely internalize vulnerability root causes or merely exploit surface-level functional patterns. While prior work documented related failures on pre-trained or zero-shot models, the SFT process itself, and how explicit reasoning supervision modulates it, remains under-explored. We study fine-tuned decoder-only LLMs under vanilla SFT and SFT with reasoning supervision, identifying a failure mode we term the Semantic Trap, characterized by three symptoms: pairing-sensitive performance, gap-dictated decisions, and fragility to semantic-preserving changes. To probe this, we propose TrapEval, an evaluation framework comprising two real-world datasets, V2P (vulnerable paired with patched code) and V2N (vulnerable paired with unrelated normal code), alongside semantic perturbations, CodeBLEU-based gap analysis, and an LLM-assisted reasoning failure taxonomy. Evaluating five representative LLMs fine-tuned with and without explicit reasoning (Chain-of-Thought), our results show vanilla SFT yields deceptively high scores on unpaired data (V2N) while failing all three symptoms. Models suffer high false-positive rates on V2P, degrade under perturbations, and exhibit a systematic dependency on the textual gap between vulnerable and patched code. Finetuning with explicit reasoning reduces these symptoms but costs recall; its lack of measurable gap-dependency partly reflects a floor effect rather than escaping the trap. Furthermore, our taxonomy reveals these models still misinterpret control flow and hallucinate API behavior, indicating current fine-tuning mitigates but does not eliminate reliance on surface features. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22655 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Do Fine-Tuned LLMs Understand Vulnerabilities? An Investigation into the Semantic Trap Huang, Feiyang Sun, Yuqiang Zhang, Fan Yang, Ziqi Liu, Han Liu, Yang Cryptography and Security Software Engineering Large Language Models (LLMs) have shown promising performance in software vulnerability detection, particularly after domain-specific Supervised Fine-Tuning (SFT). However, it remains unclear whether these models genuinely internalize vulnerability root causes or merely exploit surface-level functional patterns. While prior work documented related failures on pre-trained or zero-shot models, the SFT process itself, and how explicit reasoning supervision modulates it, remains under-explored. We study fine-tuned decoder-only LLMs under vanilla SFT and SFT with reasoning supervision, identifying a failure mode we term the Semantic Trap, characterized by three symptoms: pairing-sensitive performance, gap-dictated decisions, and fragility to semantic-preserving changes. To probe this, we propose TrapEval, an evaluation framework comprising two real-world datasets, V2P (vulnerable paired with patched code) and V2N (vulnerable paired with unrelated normal code), alongside semantic perturbations, CodeBLEU-based gap analysis, and an LLM-assisted reasoning failure taxonomy. Evaluating five representative LLMs fine-tuned with and without explicit reasoning (Chain-of-Thought), our results show vanilla SFT yields deceptively high scores on unpaired data (V2N) while failing all three symptoms. Models suffer high false-positive rates on V2P, degrade under perturbations, and exhibit a systematic dependency on the textual gap between vulnerable and patched code. Finetuning with explicit reasoning reduces these symptoms but costs recall; its lack of measurable gap-dependency partly reflects a floor effect rather than escaping the trap. Furthermore, our taxonomy reveals these models still misinterpret control flow and hallucinate API behavior, indicating current fine-tuning mitigates but does not eliminate reliance on surface features. |
| title | Do Fine-Tuned LLMs Understand Vulnerabilities? An Investigation into the Semantic Trap |
| topic | Cryptography and Security Software Engineering |
| url | https://arxiv.org/abs/2601.22655 |