Chatter Attack: Resource Consumption Attack for Large Language Models

Summary

Chatter Attack is a resource consumption attack on large language models that optimizes adversarial prompts to induce short self-doubt target phrases, which sustains long generations without the search-space explosion of earlier methods. Using inference attention loss and global entropy loss, it raises maximum response length up to 31.5x across multiple models and datasets. For black-box settings, the authors add Bayesian Prompt Optimization (BPO), which builds a discrete token kernel to explore the search space.

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