https://video.ut0pia.org/videos/watch/3b4d7479-3a34-459b-b705-e37c1bf4ca9d You fine-tune LLMs and ship them. Your evals are green, your behavioral monitors are green — and a sleeper-agent backdoor can still flip the model to harmful output on a trigger you never tested. Behavioral testing can't reach it, and the interpretability tool people reach for — joint cross-model features (crosscoders) — dilutes the signal until it sits at the noise floor. The fix is in what the training data changed. A backdoor is a directional shift that fine-tuning writes into the model's activations, so you isolate it by watching the difference between the base and fine-tuned model. In a controlled SQL-injection backdoor, a sparse autoencoder trained on that difference flags it with 40× the signal of joint features, perfect precision, and zero false positives — from a single cheap layer. You'll leave knowing how to wire a "delta monitor" into your fine-tuning pipeline as a quiet CI gate. Based on my peer-reviewed paper accepted at IJCNN. Speakers: Sachin Kumar (LexisNexis): Sachin Kumar is a Senior Data Scientist III and Tech Lead at LexisNexis, building agentic AI for the legal domain. His independent AI-safety and interpretability research has been accepted at top-tier venues including ACL, AAAI, and IJCNN. LinkedIn: https://www.linkedin.com/in/techsachinkumar/ GitHub: https://github.com/techsachinkr Thu, 09 Jul 2026 15:57:46 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://video.ut0pia.org https://video.ut0pia.org/lazy-static/avatars/0287a09a-aae7-4840-9843-b416426e7046.webp https://video.ut0pia.org/videos/watch/3b4d7479-3a34-459b-b705-e37c1bf4ca9d All rights reserved, unless otherwise specified in the terms specified at https://video.ut0pia.org/about and potential licenses granted by each content's rightholder.