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Quantized large language models are increasingly deployed on edge devices for their low latency and energy efficiency. However, model quantization weakens alignment safeguards, leaving qLLMs (quantized large language models) highly vulnerable to jailbreak attacks. To address this challenge, we present MOMAT (Mixture of...
An Authorization-Closure-Graph (ACG)-based framework that represents authorization and its dependencies as an evolving, versioned state that selectively invalidates authority affected by a revision while preserving unaffected portions of the authorization state, and computes a minimal repair.
Interactions are introduced as a fine-grained tool to analyze prompt sensitivity of LLMs and it is discovered that subtle changes to prompts can trigger severe instability in interactions, even when the outputs of the LLM remain the same.
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