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Fundamental Flaw Leaves LLMs Vulnerable To Attacks
Researchers presented a paper at the International Conference on Machine Learning arguing that LLMs cannot be fully secured. This vulnerability stems from a core architectural flaw in how these models process information. The finding suggests that current safety patches are insufficient. Practitioners must assume permanent susceptibility to sophisticated adversarial hacks.