23 citations · 34 across the 8 of their papers we have counts for
10 papers
Reflections and Fragments: Securing LLMs Against Sequential Mosaic Attacks
Emanuele La Malfa, Saar Cohen, Gabriele La Malfa +4
Self-play red-teaming improves language-model safety by pitting attacker and defender roles against each other in a zero-sum game. However, real adversaries increasingly use mosaic…
Mitigating Goal Misgeneralization via Minimax Regret
Karim Abdel Sadek, Matthew Farrugia-Roberts, Usman Anwar +4
Safe generalization in reinforcement learning requires not only that a learned policy acts capably in new situations, but also that it uses its capabilities towards the pursuit of…
REAL: Benchmarking Autonomous Agents on Deterministic Simulations of Real Websites
Divyansh Garg, Shaun VanWeelden, Diego Caples +15
We introduce REAL, a benchmark and framework for multi-turn agent evaluations on deterministic simulations of real-world websites. REAL comprises high-fidelity, deterministic repli…
Multi-Agent Risks from Advanced AI
Lewis Hammond, Alan Chan, Jesse Clifton +41
The rapid development of advanced AI agents and the imminent deployment of many instances of these agents will give rise to multi-agent systems of unprecedented complexity. These s…
Fundamental Limitations in Pointwise Defences of LLM Finetuning APIs
Xander Davies, Eric Winsor, Alexandra Souly +4
LLM developers have imposed technical interventions to prevent fine-tuning misuse attacks, attacks where adversaries evade safeguards by fine-tuning the model using a public API. P…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
Delta-Influence: Unlearning Poisons via Influence Functions
Wenjie Li, Jiawei Li, Pengcheng Zeng +3
Addressing data integrity challenges, such as unlearning the effects of data poisoning after model training, is necessary for the reliable deployment of machine learning models. St…
Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMs
Yohan Mathew, Ollie Matthews, Robert McCarthy +4
The rapid proliferation of frontier model agents promises significant societal advances but also raises concerns about systemic risks arising from unsafe interactions. Collusion to…
IDs for AI Systems
Alan Chan, Noam Kolt, Peter Wills +7
AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. A user may not be able to verify whether…
Unelicitable Backdoors in Language Models via Cryptographic Transformer Circuits
Andis Draguns, Andrew Gritsevskiy, Sumeet Ramesh Motwani +3
The rapid proliferation of open-source language models significantly increases the risks of downstream backdoor attacks. These backdoors can introduce dangerous behaviours during m…