Adversarial Inputs Amplify Reasoning Costs In Large Language Models.

Adversarial AI for Password Security and Analysis in Digital Investigations

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Adversarial Inputs Amplify Reasoning Costs In Large Language Models.

Recent research demonstrates that adversarial inputs can significantly inflate the computational cost of large language models, such as DeepSeek-R1 and OpenAI’s o1, by exploiting tendencies towards excessive reasoning—specifically, initiating unnecessary inference paths or prolonging analysis—without impacting the accuracy of their outputs, a vulnerability addressed by a novel loss framework encouraging more efficient reasoning processes.