6 citations · 17 across the 21 of their papers we have counts for
6 papers · 1 filter
Distillation Defenses Easily Break After Reinforcement Learning
Shidan Javaheri, Alexander Panfilov, Oliver Britton +2
Distillation attacks copy the reasoning capabilities of closed-source large language models, allowing bad actors to replicate state-of-the-art performance at low cost. Attackers sy…
Gaming AI-Assisted Peer Reviews Poses New Risks to the Scientific Community
Lin Li, Qi Zhang, Xander Davies +2
AI is increasingly used to support scientific peer review, from manuscript screening, reviewer assistance to editorial triage. Although such systems promise to reduce reviewer burd…
Running the Gauntlet: Hard Agentic Tasks
Mykola Vysotskyi, Runqi Lin, Grzegorz Biziel +21
As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capabilities. However, current benchma…
On Safety Risks in Experience-Driven Self-Evolving Agents
Weixiang Zhao, Yichen Zhang, Yingshuo Wang +8
Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduc…
OMNI-LEAK: Orchestrator Multi-Agent Network Induced Data Leakage
Akshat Naik, Jay Culligan, Yarin Gal +4
As Large Language Model (LLM) agents become more capable, their coordinated use in the form of multi-agent systems is anticipated to emerge as a practical paradigm. Prior work has…
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…