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Buffer Overflow in Mixture of Experts
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Mixture of Experts (MoE) has become a key ingredient for scaling large foundation models while keeping inference costs steady. We show that expert routing strategies that have cross-batch dependencies are vulnerable to attacks. Malicious queries can be sent to a model and can affect a model's output on other benign queries if they are grouped in the same batch. We demonstrate this via a proof-of-concept attack in a toy experimental setting.
Forward citations
Cited by 3 Pith papers
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SparSEEty: Extracting Tokens from Sparsity-Exploiting LLM Serving Systems via Deterministic Side Channels
Using page-fault side channels, an attacker can observe which FFN neurons a sparsity-exploiting LLM activates and invert those binary traces to recover prompt and response tokens with BLEU above 0.95.
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BadMoE: Backdooring Mixture-of-Experts LLMs via Optimizing Routing Triggers and Infecting Dormant Experts
BadMoE implants backdoors into dormant experts of MoE LLMs and uses routing-trigger optimization to activate them, achieving high attack success while preserving normal accuracy.
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Rerouting LLM Routers
Adversarially optimized, query-independent token prefixes can reroute nearly all queries to the expensive strong model in both open-source and commercial LLM routers.
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