Turning Bias into Bugs: Bandit-Guided Style Manipulation Attacks on LLM Judges
The known stylistic biases in LLM judges, such as a preference for verbosity or specific sentence structures, present an underexplored security vulnerability. In this work, we introduce BITE (BIas exploraTion and Exploitation), a black-box adversarial framework that learns semantics-preserving edits to mislead an LLM judge and artificially inflate the scores it assigns. We cast the selection of stylistic edits as a contextual bandit problem and use a LinUCB policy to adaptively choose edits that maximize the judge's score without access to model parameters or gradients. Empirically, we test BITE across a diverse range of LLM judges and tasks, including both pointwise and pairwise comparis...
| Authors | Xianglin Yang, Bryan Hooi, Gelei Deng et al. |
|---|---|
| Primary category | cs.CR |
| Published | 24 May 2026 |
| Last updated at source | 28 May 2026 |
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