model vetting

Model vetting is the process of evaluating large language models (LLMs) for hidden security risks before deployment. Recent discussions on WindowsForum highlight Microsoft's research into detecting LLM backdoors, which identifies three observable signatures of potential poisoning during training: an attention double triangle, memorized leakage of poisoning data, and fuzzy trigger activation. A lightweight, forward-pass-only scanner can reconstruct likely triggers, helping security teams and model consumers reduce the risk of deploying compromised models. The topic also touches on Microsoft's partnership with Hugging Face through Azure AI Foundry, which aims to provide a robust infrastructure for open source AI, emphasizing the importance of vetting models in enterprise AI deployments.