113 citations · 155 across the 30 of their papers we have counts for
10 papers · 1 filter
AgentSCOPE: Evaluating Contextual Privacy Across Agentic Workflows
Ivoline C. Ngong, Keerthiram Murugesan, Swanand Kadhe +3
Agentic systems are increasingly acting on users' behalf, accessing calendars, email, and personal files to complete everyday tasks. Privacy evaluation for these systems has focuse…
In-Context Probing for Membership Inference in Fine-Tuned Language Models
Zhexi Lu, Hongliang Chi, Nathalie Baracaldo +3
Membership inference attacks (MIAs) pose a critical privacy threat to fine-tuned large language models (LLMs), especially when models are adapted to domain-specific tasks using sen…
Protecting Users From Themselves: Safeguarding Contextual Privacy in Interactions with Conversational Agents
Ivoline Ngong, Swanand Kadhe, Hao Wang +4
Conversational agents are increasingly woven into individuals' personal lives, yet users often underestimate the privacy risks associated with them. The moment users share informat…
Towards a Re-evaluation of Data Forging Attacks in Practice
Mohamed Suliman, Anisa Halimi, Swanand Kadhe +2
Data forging attacks provide counterfactual proof that a model was trained on a given dataset, when in fact, it was trained on another. These attacks work by forging (replacing) mi…
Turning Generative Models Degenerate: The Power of Data Poisoning Attacks
Shuli Jiang, Swanand Ravindra Kadhe, Yi Zhou +3
The increasing use of large language models (LLMs) trained by third parties raises significant security concerns. In particular, malicious actors can introduce backdoors through po…
Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks
Shuli Jiang, Swanand Ravindra Kadhe, Yi Zhou +2
Growing applications of large language models (LLMs) trained by a third party raise serious concerns on the security vulnerability of LLMs.It has been demonstrated that malicious a…