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Security of Language Models for Code: A Systematic Literature Review

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arxiv 2410.15631 v2 pith:W7TZLDCD submitted 2024-10-21 cs.SE cs.CR

Yuchen Chen , Weisong Sun , Chunrong Fang , Zhenpeng Chen , Yifei Ge , Tingxu Han , Quanjun Zhang , Yang Liu
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Zhenyu Chen Baowen Xu
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classification cs.SEcs.CR
keywords modelscodelmslanguageresearchsecuritycodereviewtools
verification ladder T0 review T1 audit T2 compute T3 formal
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Language models for code (CodeLMs) have emerged as powerful tools for code-related tasks, outperforming traditional methods and standard machine learning approaches. However, these models are susceptible to security vulnerabilities, drawing increasing research attention from domains such as software engineering, artificial intelligence, and cybersecurity. Despite the growing body of research focused on the security of CodeLMs, a comprehensive survey in this area remains absent. To address this gap, we systematically review 67 relevant papers, organizing them based on attack and defense strategies. Furthermore, we provide an overview of commonly used language models, datasets, and evaluation metrics, and highlight open-source tools and promising directions for future research in securing CodeLMs.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  4. MT4DP: Data Poisoning Attack Detection for DL-based Code Search Models via Metamorphic Testing

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    In 52 open-source LLM projects, 75.8% of those with dependency configs use at least one vulnerable package, and half of supply chain vulnerabilities stay undisclosed for over 56 months.

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