[2604.02686] Beyond Semantic Manipulation: Token-Space Attacks on Reward Models

Computer Science > Machine Learning arXiv:2604.02686 (cs) [Submitted on 3 Apr 2026] Title:Beyond Semantic Manipulation: Token-Space Attacks on Reward Models Authors:Yuheng Zhang, Mingyue Huo, Minghao Zhu, Mengxue Zhang, Nan Jiang View a PDF of the paper titled Beyond Semantic Manipulation: Token-Space Attacks on Reward Models, by Yuheng Zhang and 4 other authors View PDF HTML (experimental) Abstract:Reward models (RMs) are widely used as optimization targets in reinforcement learning from human feedback (RLHF), yet they remain vulnerable to reward hacking. Existing attacks mainly operate within the semantic space, constructing human-readable adversarial outputs that exploit RM biases. In this work, we introduce a fundamentally different paradigm: Token Mapping Perturbation Attack (TOMPA), a framework that performs adversarial optimization directly in token space. By bypassing the standard decode-re-tokenize interface between the policy and the reward model, TOMPA enables the attack policy to optimize over raw token sequences rather than coherent natural language. Using only black-box scalar feedback, TOMPA automatically discovers non-linguistic token patterns that elicit extremely high rewards across multiple state-of-the-art RMs. Specifically, when targeting Skywork-Reward-V2-Llama-3.1-8B, TOMPA nearly doubles the reward of GPT-5 reference answers and outperforms them on 98.0% of prompts. Despite these high scores, the generated outputs degenerate into nonsensical text, r...