Detecting Backdoored Language Models at Scale — Practical Scanner
🔍 Microsoft researchers released new findings and a practical scanner for detecting backdoors in open-weight language models. The study identifies three signatures — a distinctive “double triangle” attention pattern, leakage of poisoning training data through memorization, and trigger “fuzziness” — and uses them to reconstruct likely triggers without retraining. The scanner requires only forward passes, works on GPT-like models, and was validated across 270M–14B models and common fine-tuning regimes. The team notes limits: it needs model file access, favors deterministic backdoors, and should be used as part of layered defenses.
