1 citations · 1 across the 7 of their papers we have counts for
9 papers
RATION: Towards Quantitative Risk-Based Access Control for AI Agents
Yuntao Du, Harsh Chaudhari, Yuetian Chen +8
AI agents increasingly interact with external environments to complete complex user tasks. These interactions can lead to unexpected behavior and expose agents to security threats…
BrowseSafe++: Benchmarking Prompt Injection Detection Across Web Contexts, Languages, and Browser Views
Yuetian Chen, Kaiyuan Zhang, Yuntao Du +6
Browser agents need to make decisions and execute actions on behalf of users while consuming untrusted webpages, creating opportunities for malicious instructions embedded in webpa…
ActGov: Governing LLM Agent Actions via Policy-Constrained Validation
Kaiyuan Zhang, Yuke Peng, Ke Jiang +1
Large language model (LLM) agents increasingly execute long-horizon workflows through external tools, allowing untrusted outputs to influence subsequent actions and exceed user aut…
PII-TRACE: A Benchmark for Context-Aware PII Detection in Multi-Turn LLM Conversations
Kaiyuan Zhang, Chuan Wang, Joey Zhong +4
LLM assistants and agentic systems log long multi-turn conversations. AI providers often scan these conversations for Personally Identifiable Information (PII) and mask the PII bef…
Security Considerations for Artificial Intelligence Agents
Ninghui Li, Kaiyuan Zhang, Kyle Polley +1
This article, a lightly adapted version of Perplexity's response to NIST/CAISI Request for Information 2025-0035, details our observations and recommendations concerning the securi…
Membership Inference Attacks Against Fine-tuned Diffusion Language Models
Yuetian Chen, Kaiyuan Zhang, Yuntao Du +5
Diffusion Language Models (DLMs) represent a promising alternative to autoregressive language models, using bidirectional masked token prediction. Yet their susceptibility to priva…
Window-based Membership Inference Attacks Against Fine-tuned Large Language Models
Yuetian Chen, Yuntao Du, Kaiyuan Zhang +4
Most membership inference attacks (MIAs) against Large Language Models (LLMs) rely on global signals, like average loss, to identify training data. This approach, however, dilutes…
BrowseSafe: Understanding and Preventing Prompt Injection Within AI Browser Agents
Kaiyuan Zhang, Mark Tenenholtz, Kyle Polley +3
The integration of artificial intelligence (AI) agents into web browsers introduces security challenges that go beyond traditional web application threat models. Prior work has ide…
Cascading and Proxy Membership Inference Attacks
Yuntao Du, Jiacheng Li, Yuetian Chen +5
A Membership Inference Attack (MIA) assesses how much a trained machine learning model reveals about its training data by determining whether specific query instances were included…