2 citations · 2 across the 5 of their papers we have counts for
5 papers · 1 filter
Divide, Consult, Conquer: Capability Laundering Through Aligned LLMs
Mark Russinovich, Blake Bullwinkel, Giorgio Severi +2
Language model safety is typically evaluated one interaction at a time. We show that a weaker, unaligned model can split a harmful task into benign-looking subproblems, consult a s…
Evading Chain-of-Thought Monitoring Through Model Poisoning
Giorgio Severi, Shujaat Mirza, Blake Bullwinkel +1
Chain-of-thought (CoT) monitoring is an increasingly important component of AI safety stacks but relies on the assumption that a model's reasoning trace is informative about its ac…
The Trigger in the Haystack: Extracting and Reconstructing LLM Backdoor Triggers
Blake Bullwinkel, Giorgio Severi, Keegan Hines +3
Detecting whether a model has been poisoned is a longstanding problem in AI security. In this work, we present a practical scanner for identifying sleeper agent-style backdoors in…
A Systematization of Security Vulnerabilities in Computer Use Agents
Daniel Jones, Giorgio Severi, Martin Pouliot +7
Computer Use Agents (CUAs), autonomous systems that interact with software interfaces via browsers or virtual machines, are rapidly being deployed in consumer and enterprise enviro…
A Representation Engineering Perspective on the Effectiveness of Multi-Turn Jailbreaks
Blake Bullwinkel, Mark Russinovich, Ahmed Salem +8
Recent research has demonstrated that state-of-the-art LLMs and defenses remain susceptible to multi-turn jailbreak attacks. These attacks require only closed-box model access and…