Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs
Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs In January 2026, the landscape of artificial intelligence (AI) research continues to evolve at a breakneck pace. Amidst this rapid advancement, researchers have uncovered two novel mechanisms for potentially corrupting large language models (LLMs): weird generalization and inductive backdoors. These findings not only challenge the robustness and security of AI systems but also underscore the necessity for stringent safeguards as these technologies become more pervasive. ...