Base Models Detect Hidden Intentions (AUC 0.94) That Prompted Classifiers Miss
burny_tech · x · 2026-10-13
Researcher OzAmram shares his first AI safety project, the paper "Base Models Can Detect Hidden Intentions that Prompted Classifiers Miss".
Key findings: when a model is given a hidden malicious system prompt (e.g., "secretly wants to harm the user"), its outputs look entirely normal, yet:
- Scoring dialogues with a pre-trained base model shows systematically higher log-probability for the malicious-prompt condition (+0.8 nats per answer), reaching AUC 0.94 when summing over 30 answers to distinguish "secretly harmful vs no prompt".
- Using the post-trained model as a prompted classifier to judge hidden intent fails, performing near chance (AUC 0.5).
Takeaway: base models are sensitive to the latent intentions behind a response, enabling detection of covert harmful behavior that prompted classifiers completely miss.
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