2 citations · 5 across the 20 of their papers we have counts for
4 papers · 1 filter
MANATEE: Inference-Time Lightweight Diffusion Based Safety Defense for LLMs
Chun Yan Ryan Kan, Tommy Tran, Vedant Yadav +4
Defending LLMs against adversarial jailbreak attacks remains an open challenge. Existing defenses rely on binary classifiers that fail when adversarial input falls outside the lear…
Zero-Shot Embedding Drift Detection: A Lightweight Defense Against Prompt Injections in LLMs
Anirudh Sekar, Mrinal Agarwal, Rachel Sharma +4
Prompt injection attacks have become an increasing vulnerability for LLM applications, where adversarial prompts exploit indirect input channels such as emails or user-generated co…
Factor(U,T): Controlling Untrusted AI by Monitoring their Plans
Edward Lue Chee Lip, Anthony Channg, Diana Kim +2
As AI capabilities advance, we increasingly rely on powerful models to decompose complex tasks $\unicode{x2013}$ but what if the decomposer itself is malicious? Factored cognition…
AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models
Aashray Reddy, Andrew Zagula, Nicholas Saban
Large Language Models (LLMs) continue to exhibit vulnerabilities to jailbreaking attacks: carefully crafted malicious inputs intended to circumvent safety guardrails and elicit har…