A Systematic Evaluation of Parameter-Efficient Fine-Tuning Methods for the Security of Code LLMs

• URL: http://arxiv.org/abs/2509.12649v1
• Date: Tue, 16 Sep 2025 04:09:41 GMT
• Title: A Systematic Evaluation of Parameter-Efficient Fine-Tuning Methods for the Security of Code LLMs
• Authors: Kiho Lee, Jungkon Kim, Doowon Kim, Hyoungshick Kim,
• Abstract summary: Large Language Models (LLMs) significantly accelerate software development, but their frequent generation of insecure code presents serious risks.<n>We present a comprehensive evaluation of seven parameter-efficient fine-tuning (PEFT) techniques, demonstrating substantial gains in secure code generation without compromising functionality.<n>Our research identifies prompt-tuning as the most effective PEFT method, achieving an 80.86% Overall-Secure-Rate on CodeGen2 16B, a 13.5-point improvement over the 67.28% baseline.
• Score: 14.754759160027959
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Code-generating Large Language Models (LLMs) significantly accelerate software development. However, their frequent generation of insecure code presents serious risks. We present a comprehensive evaluation of seven parameter-efficient fine-tuning (PEFT) techniques, demonstrating substantial gains in secure code generation without compromising functionality. Our research identifies prompt-tuning as the most effective PEFT method, achieving an 80.86% Overall-Secure-Rate on CodeGen2 16B, a 13.5-point improvement over the 67.28% baseline. Optimizing decoding strategies through sampling temperature further elevated security to 87.65%. This equates to a reduction of approximately 203,700 vulnerable code snippets per million generated. Moreover, prompt and prefix tuning increase robustness against poisoning attacks in our TrojanPuzzle evaluation, with strong performance against CWE-79 and CWE-502 attack vectors. Our findings generalize across Python and Java, confirming prompt-tuning's consistent effectiveness. This study provides essential insights and practical guidance for building more resilient software systems with LLMs.

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