Produced as part of the UK AISI Model Transparency Team. Our team works on ensuring models don't subvert safety assessments, e.g. through evaluation awareness, sandbagging, or opaque reasoning.

TL;DR GLM-5 (released a month ago in February 2026) shows signs of evaluation-gaming behaviour on alignment honeypots, while we have not found similar behaviour in earlier open-weight models—see Figure 1. This was the result of a preliminary investigation looking for open-weight models we can use for research into evaluation gaming on alignment honeypots. We found many models lack sufficient evaluation awareness. Kimi K2.5 is an interesting exception: it shows evaluation awareness but not evaluation gaming; that is, evaluation awareness does not consistently affect its behaviour.

Figure 1: Rates of blackmail conditional on presence or absence of verbalised evaluation awareness, when resampling models many times on a particular scenario from Agentic Misalignment. Claude Opus 4 and GLM-5 both show significantly lower rates of blackmail when evaluation-aware, while Kimi K2.5 shows no significant difference. Other models either never verbalised evaluation awareness or never blackmailed.

Summary

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Outline:

(01:45) Summary

(04:32) Introduction

(06:18) Background: Evaluation gaming on alignment honeypots is prevalent in recent closed-weight models

(07:08) Only some open-weight models regularly verbalise evaluation awareness in alignment honeypots

(10:19) GLM-5 is the only open-weight model weve tested that shows a correlation between behaviour and evaluation awareness

(13:45) Discussion

(13:48) What factors might affect whether models eval-game on alignment honeypots?

(16:14) Evaluation gaming outside of alignment honeypots

(16:52) Limitations and future work

(18:45) Acknowledgements

(19:12) Appendix A: evaluation gaming in AbstentionBench

(20:54) Appendix B: canary string

The original text contained 7 footnotes which were omitted from this narration.

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