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20212026
most citedCan Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

4 citations · 5 across the 14 of their papers we have counts for

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7 papers · 1 filter

cs.CR2026

Defending Against Dormant Poisoning Attacks Across Language and Multimodal Agents

Wonjun Lee, Vaidehi Patil, Zaid Khan +4

Recent work shows that attackers can implant dormant malicious behaviors that remain hidden at release but emerge after benign fine-tuning. As foundation models are used as agents,…

math.NA2026

Scaled Companion Linearizations for Coefficientwise Backward Stability

Javier Pérez

A central goal in solving a polynomial eigenvalue problem is to compute eigenpairs that are exact for a nearby polynomial, with each coefficient perturbation bounded by a modest mu…

cs.CR2026

Safeguarding LLMs via Model-Agnostic Latent Safety Signals from Dark Knowledge

Wonjun Lee, Kyungsik Yang, Gaeun Ji +5

LLMs have advanced rapidly, raising growing concerns about their safety. Recent work has proposed approaches to detect and defend against attacks including defenses at decoding sta…

cs.MA2026

EvoHarnessBench: Can Your Agents Keep Pace with an Evolving Harness?

Zixuan Ke, Vaidehi Patil, Haizhou Shi +9

Modern LLM-based agents operate through a harness of tools, reusable skills, and specialist agents that shapes what they observe and what they can do. In practice, this harness con…

cs.LG2026

Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks

Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1

With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…

cs.CL2026

Playing Along: Learning a Double-Agent Defender for Belief Steering via Theory of Mind

Hanqi Xiao, Vaidehi Patil, Zaid Khan +3

As large language models (LLMs) become the engine behind conversational systems, their ability to reason about the intentions and states of their dialogue partners (i.e., form and…

cs.CV2026

Hierarchy-Aware Multimodal Unlearning for Medical AI

Fengli Wu, Vaidehi Patil, Jaehong Yoon +2

Pretrained Multimodal Large Language Models (MLLMs) are increasingly used in sensitive domains such as medical AI, where privacy regulations like HIPAA and GDPR require specific re…