BarAltiva

This research was completed for LASR Labs 2025 by Nelson Gardner-Challis, Jonathan Bostock, Georgiy Kozhevnikov and Morgan Sinclaire. The team was supervised by Joan Velja and Charlie Griffin (University of Oxford, UK AI Security Institute).

The full paper can be found here.

Tl;dr

We did a deep dive into untrusted monitoring, and explored several new collusion strategies:

• Empirically, we ran control evaluations using these new strategies, finding that naive self-recognition with SFT triples the red team’s win rate compared to the For/While signal in previous work.
• Conceptually, we developed a safety case sketch for untrusted monitoring, which leverages our taxonomy of collusion strategies, as well as our new statistical modelling approach.

Overall, our key finding is that arguing for the safety of an untrusted monitoring deployment requires resolving several important open problems that...