Model1h ago
Fundamental Flaw Leaves LLMs Vulnerable To Attacks
A research team 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 data. The finding suggests that current security patches are insufficient. Practitioners must now assume that no model is entirely immune to adversarial hacks.