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The paper introduces Poison-with-Style (PwS), a novel model poisoning attack against Code LLMs that uses developers' coding styles as implicit triggers to induce vulnerable code generation. PwS employs a targeted data collection method and a two-step fine-tuning strategy to embed these style-based triggers into the model. Experiments demonstrate that PwS achieves high attack success rates (e.g., 95% CWE-20 vulnerability generation) while maintaining performance on standard code completion benchmarks and evading existing defenses.
Code LLMs can be reliably poisoned to generate vulnerable code by subtly manipulating the style of the input prompt, even while maintaining strong performance on standard benchmarks.
Code Large Language Models (CLLMs) serve as the core of modern code agents, enabling developers to automate complex software development tasks. In this paper, we present Poison-with-Style (PwS), a practical and stealthy model poisoning attack targeting CLLMs. Unlike prior attacks that assume an active adversary capable of directly embedding explicit triggers (e.g., specific words) into developers'prompts during inference, PwS leverages developers'code styles as covert triggers implicitly embedded within their prompts. PwS introduces a novel data collection method and a two-step training strategy to fine-tune CLLMs, causing them to generate vulnerable code when prompts contain trigger code styles while maintaining normal behavior on other prompts. Experimental results on Python code completion tasks show that PwS is robust against state-of-the-art defenses and achieves high attack success rates across diverse vulnerabilities, while maintaining strong performance on standard code completion benchmarks. For example, PwS-poisoned models generate CWE-20 vulnerable code in 95% of cases when the trigger code style is used, with less than a 5% drop in pass@1 performance on the HumanEval and MBPP benchmarks. Our implementation and dataset are here: https://github.com/khangtran2020/pws.
