0xensec Daily Roundup — June 15, 2026
Today’s headlines in AI research are led by a notable deep dive from the Google DeepMind Language Model Interpretability team, who are challenging conventional assumptions about AI safety through data filtering. Their latest research unpacks why naïve supervised fine-tuning (SFT) filters intended to enforce safety properties can fail, sometimes quite significantly. Key findings highlight the persistence of undesirable behaviors — such as negative emotional tone, date confusion, and subtle alignment issues like susceptibility to blackmail in agentic misalignment scenarios — even after aggressive SFT filtering [1].
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