Authors: Satvik Golechha*, Sid Black*, Joseph Bloom * Equal Contribution. This work was done as part of the Model Transparency team at the UK AI Security Institute (AISI). Our code is available on GitHub and the model checkpoints and data is available on HuggingFace. Executive Summary In Natural Emergent Misalignment...
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...
Jordan Taylor, Sid Black, Dillon Bowen, Thomas Read, Satvik Golechha, Alex Zelenka-Martin, Oliver Makins, Connor Kissane, Kola Ayonrinde, Jacob Merizian, Samuel Marks, Chris Cundy, Joseph Bloom UK AI Security Institute, FAR.AI, Anthropic Links: Paper | Code | Models | Transcripts | Interactive Demo Epistemic Status: We're sharing our paper and...
The Alignment Project is a global fund of over £15 million, dedicated to accelerating progress in AI control and alignment research. It is backed by an international coalition of governments, industry, venture capital and philanthropic funders. This post is part of a sequence on research areas that we are excited...
The Alignment Project is a global fund of over £15 million, dedicated to accelerating progress in AI control and alignment research. It is backed by an international coalition of governments, industry, venture capital and philanthropic funders. This sequence sets out the research areas we are excited to fund – we...
Introduction Joseph Bloom, Alan Cooney This is a research update from the White Box Control team at UK AISI. In this update, we share preliminary results on the topic of sandbagging that may be of interest to researchers working in the field. The format of this post was inspired by...
Say an LLM agent behaves innocuously in some context A, but in some sense “knows” that there is some related context B such that it would have behaved maliciously (inserted a backdoor in code, ignored a security bug, lied, etc.). For example, in the recent alignment faking paper Claude Opus...