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该论文通过系统文献综述与128位从业者评估，提出并验证了AI鲁棒性审计框架的44项元需求（分为16个类别），以帮助组织构建和评估AI鲁棒性审计框架。
The paper presents the Groundhog Bit-Flip Attack (GBFA), a denial-of-wallet availability attack on Mixture-of-Experts (MoE) large language models, which uses bit flips to cause infinite generation loops by deactivating termination-related experts.
The study reveals substantial gaps in machine unlearning for LLMs, showing that adversarial evaluation uncovers recoverability of forgotten information despite strong standard metrics, highlighting the need for adversarial stress-testing.
Discusses how AI systems often trust sensor inputs without validation, using an example of a logistics company where spoofed temperature sensor data led to cargo damage, and questions whether AI can detect such spoofing.
This paper introduces FragileFlow, a plug-in regularizer that improves the robustness of LLMs and VLMs by controlling 'correct-but-fragile' predictions through spectral analysis and PAC-Bayes bounds.
The paper introduces PASA, a robust watermarking algorithm for LLM-generated text that operates at the semantic level using latent embedding spaces to resist semantic-invariant attacks like paraphrasing.
This article explains how incorporating Shannon entropy into reinforcement learning objectives creates more robust agents capable of handling unexpected or adversarial changes in rewards and dynamics.