LLMs are increasingly deployed in real-world applications, making inference efficiency and service reliability critical concerns due to their substantial computational costs. However, the autoregressive generation mechanism of LLMs enables malicious prompts t...
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From Role Prompt to Infinite Thinking: Exploiting Persona Conditioning for Inference Cost Attacks in LLMs
LLMs are increasingly deployed in real-world applications, making inference efficiency and service reliability critical concerns due to their substantial computational costs. However, the autoregressive generation mechanism of LLMs enables malicious prompts t...
LLMs are increasingly deployed in real-world applications, making inference efficiency and service reliability critical concerns due to their substantial computational costs. However, the autoregressive generation mechanism of LLMs enables malicious prompts to manipulate generation behaviors, inducing excessive...
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Signal Strength 92%
Technical label WATCH
Public Interest 21
Category SECURITY
Reader Depth GENERAL
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Public Interest components
Recognizable Entity Score 0
Practical Impact Score 0
Novelty Interest Score 70
Consequence Score 18
Curiosity Score 0
Shareability Score 41
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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