2 citations · 4 across the 10 of their papers we have counts for
11 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…
Automated alignment is harder than you think
Aleksandr Bowkis, Marie Davidsen Buhl, Jacob Pfau +1
A leading proposal for aligning artificial superintelligence (ASI) is to use AI agents to automate an increasing fraction of alignment research as capabilities improve. We argue th…
Boundary Point Jailbreaking of Black-Box LLMs
Xander Davies, Giorgi Giglemiani, Edmund Lau +3
Frontier LLMs are safeguarded against attempts to extract harmful information via adversarial prompts known as "jailbreaks". Recently, defenders have developed classifier-based sys…
Practical challenges of control monitoring in frontier AI deployments
David Lindner, Charlie Griffin, Tomek Korbak +4
Automated control monitors could play an important role in overseeing highly capable AI agents that we do not fully trust. Prior work has explored control monitoring in simplified…
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…
Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
Tomek Korbak, Mikita Balesni, Elizabeth Barnes +38
AI systems that "think" in human language offer a unique opportunity for AI safety: we can monitor their chains of thought (CoT) for the intent to misbehave. Like all other known A…
An alignment safety case sketch based on debate
Marie Davidsen Buhl, Jacob Pfau, Benjamin Hilton +1
If AI systems match or exceed human capabilities on a wide range of tasks, it may become difficult for humans to efficiently judge their actions -- making it hard to use human feed…
How to evaluate control measures for LLM agents? A trajectory from today to superintelligence
Tomek Korbak, Mikita Balesni, Buck Shlegeris +1
As LLM agents grow more capable of causing harm autonomously, AI developers will rely on increasingly sophisticated control measures to prevent possibly misaligned agents from caus…
Safety Cases: A Scalable Approach to Frontier AI Safety
Benjamin Hilton, Marie Davidsen Buhl, Tomek Korbak +1
Safety cases - clear, assessable arguments for the safety of a system in a given context - are a widely-used technique across various industries for showing a decision-maker (e.g.…
A sketch of an AI control safety case
Tomek Korbak, Joshua Clymer, Benjamin Hilton +2
As LLM agents gain a greater capacity to cause harm, AI developers might increasingly rely on control measures such as monitoring to justify that they are safe. We sketch how devel…
Safety case template for frontier AI: A cyber inability argument
Arthur Goemans, Marie Davidsen Buhl, Jonas Schuett +4
Frontier artificial intelligence (AI) systems pose increasing risks to society, making it essential for developers to provide assurances about their safety. One approach to offerin…