15 papers
Open Technical Problems in Open-Weight AI Model Risk Management
Stephen Casper, Kyle O'Brien, Shayne Longpre +19
Frontier AI models with openly available weights are steadily becoming more powerful and widely adopted. However, compared to proprietary models, open-weight models pose different…
Prefill Awareness in Large Language Models
Andy Wang, Parv Mahajan, David Demitri Africa +3
Safety-relevant studies of language models, including alignment and jailbreaking evaluations and AI control protocols, often rely on prefilling model outputs. If AI models can reco…
Evaluating whether AI models would sabotage AI safety research
Robert Kirk, Alexandra Souly, Kai Fronsdal +2
We evaluate the propensity of frontier models to sabotage or refuse to assist with safety research when deployed as AI research agents within a frontier AI company. We apply two co…
UK AISI Alignment Evaluation Case-Study
Alexandra Souly, Robert Kirk, Jacob Merizian +2
This technical report presents methods developed by the UK AI Security Institute for assessing whether advanced AI systems reliably follow intended goals. Specifically, we evaluate…
Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs
Kyle O'Brien, Stephen Casper, Quentin Anthony +7
Open-weight AI systems offer unique benefits, including enhanced transparency, open research, and decentralized access. However, they are vulnerable to tampering attacks which can…
STACK: Adversarial Attacks on LLM Safeguard Pipelines
Ian R. McKenzie, Oskar J. Hollinsworth, Tom Tseng +5
Frontier AI developers are relying on layers of safeguards to protect against catastrophic misuse of AI systems. Anthropic and OpenAI guard their latest Opus 4 model and GPT-5 mode…