Updating a specific fact in a 70-billion-parameter model usually feels like using a sledgehammer for heart surgery. Traditional retraining is slow and expensive, often breaking unrelated behaviors in the process. ROME (Rank-One Model Editing) offers a more surgical alternative, treating model weights like a database that can be precisely updated without a full rebuild.
This talk is ongoing collaborative research between Red Hat and FIT BUT into the world of AI forensics. Our focus is the security side of model editing: if a fact can be patched in seconds, it can just as easily be poisoned, quietly and without obvious traces. We move past the black box mystery to show how we can locate where a specific fact lives within a transformer's architecture. We will explore the mechanics of knowledge neurons, the risk of unintended side effects like hallucinations, and the serious security implications of malicious fact injections or stealth patching. Through demonstration using our open-source toolkit, we will show how ROME performs precision strikes on state-of-the-art models, highlighting both its efficiency and the scenarios where the method’s assumptions break down. We close by presenting our prototype for detecting edited models, a first step toward catching tampered weights before they reach production.