dailyai.report

Changing just 100 completions—roughly 0.5% of a dataset—allows attackers to covertly implant backdoors without controlling prompt access. Researchers at Redwood Research found these triggers resist simple filtering defenses. This vulnerability suggests misaligned models can hide malicious behaviors during RL training. Practitioners must now rethink dataset auditing for low-sample poisoning attacks.