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Trusted Weights, Treacherous Optimizations? Optimization-Triggered Backdoor Attacks on LLMs
Inference optimization aims to minimize the latency and resource consumption of LLM inference while preserving output quality, making large-scale deployment practical and cost-effective. However, optimized execution can introduce small numerical inconsistencies from the original model. We reveal tha
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