1 citations · 1 across the 1 of their papers we have counts for
5 papers
Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning
Kyle Domico, Jean-Charles Noirot Ferrand, Ryan Sheatsley +3
Attacks on machine learning models have been extensively studied through stateless optimization. In this paper, we demonstrate how a reinforcement learning (RL) agent can learn a n…
Targeting Alignment: Extracting Safety Classifiers of Aligned LLMs
Jean-Charles Noirot Ferrand, Yohan Beugin, Eric Pauley +2
Alignment in large language models (LLMs) is used to enforce guidelines such as safety. Yet, alignment fails in the face of jailbreak attacks that modify inputs to induce unsafe ou…
On the Robustness Tradeoff in Fine-Tuning
Kunyang Li, Jean-Charles Noirot Ferrand, Ryan Sheatsley +4
Fine-tuning has become the standard practice for adapting pre-trained models to downstream tasks. However, the impact on model robustness is not well understood. In this work, we c…
Efficient Storage Integrity in Adversarial Settings
Quinn Burke, Ryan Sheatsley, Yohan Beugin +4
Storage integrity is essential to systems and applications that use untrusted storage (e.g., public clouds, end-user devices). However, known methods for achieving storage integrit…
Securing Cloud File Systems with Trusted Execution
Quinn Burke, Yohan Beugin, Blaine Hoak +7
Cloud file systems offer organizations a scalable and reliable file storage solution. However, cloud file systems have become prime targets for adversaries, and traditional designs…