kapynResearch

A fundamental flaw leaves LLMs strikingly vulnerable to attack

LLMs face inherent security vulnerabilities that cannot be fully patched due to foundational architectural flaws. Researchers presented these findings at the International Conference on Machine Learning, challenging the long-term viability of current alignment and safety guardrails. This realization forces AI developers to rethink threat modeling and accept residual risk when deploying models in production environments.

MIT Tech Review·Jul 30, 2026

Opening Kapyn…