LLMs for code vulnerability detection show average susceptibility of 33.2% to framing, 23.5% to anchoring, and 18.4% to halo effects, with a black-box attack suppressing up to 97% of detections.
Beauty and the bias: Exploring the impact of attractiveness on multimodal large language models,
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LLM judges for code tasks show high sensitivity to prompt biases that systematically favor certain options, changing accuracy and model rankings even when code is unchanged.
citing papers explorer
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Words Speak Louder Than Code: Investigating Cognitive Heuristics in LLM-Based Code Vulnerability Detection
LLMs for code vulnerability detection show average susceptibility of 33.2% to framing, 23.5% to anchoring, and 18.4% to halo effects, with a black-box attack suppressing up to 97% of detections.
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Bias in the Loop: Auditing LLM-as-a-Judge for Software Engineering
LLM judges for code tasks show high sensitivity to prompt biases that systematically favor certain options, changing accuracy and model rankings even when code is unchanged.