3 citations · 5 across the 5 of their papers we have counts for
7 papers
Toward Quantitative Modeling of Cybersecurity Risks Due to AI Misuse
Steve Barrett, Malcolm Murray, Otter Quarks +17
Advanced AI systems offer substantial benefits but also introduce risks. In 2025, AI-enabled cyber offense has emerged as a concrete example. This technical report applies a quanti…
A Methodology for Quantitative AI Risk Modeling
Malcolm Murray, Steve Barrett, Henry Papadatos +5
Although general-purpose AI systems offer transformational opportunities in science and industry, they simultaneously raise critical concerns about safety, misuse, and potential lo…
The Role of Risk Modeling in Advanced AI Risk Management
Chloé Touzet, Henry Papadatos, Malcolm Murray +6
Rapidly advancing artificial intelligence (AI) systems introduce novel, uncertain, and potentially catastrophic risks. Managing these risks requires a mature risk-management infras…
Evaluating the Goal-Directedness of Large Language Models
Tom Everitt, Cristina Garbacea, Alexis Bellot +4
To what extent do LLMs use their capabilities towards their given goal? We take this as a measure of their goal-directedness. We evaluate goal-directedness on tasks that require in…
Mapping Industry Practices to the EU AI Act's GPAI Code of Practice Safety and Security Measures
Lily Stelling, Mick Yang, Rokas Gipiškis +5
This report provides a detailed comparison between the Safety and Security measures proposed in the EU AI Act's General-Purpose AI (GPAI) Code of Practice (Third Draft) and the cur…
Mapping AI Benchmark Data to Quantitative Risk Estimates Through Expert Elicitation
Malcolm Murray, Henry Papadatos, Otter Quarks +2
The literature and multiple experts point to many potential risks from large language models (LLMs), but there are still very few direct measurements of the actual harms posed. AI…