activity
20242026
most citedModel Inversion in Split Learning for Personalized LLMs: New Insights from Information Bottleneck Theory

1 citations · 1 across the 7 of their papers we have counts for

collaborators

9 papers

cs.CR2026

Security of World-Model-Based Embodied AI: A Lifecycle of Threats, Defenses, and Evaluation

Fazhong Liu, Zhuoyan Chen, Haozhen Tan +3

World models give embodied AI a predictive core: they compress observations into states, simulate action-conditioned futures, and enable planning beyond reactive control. This pred…

cs.CR2026

DIPBox: A Multi-scale Testing Framework for Tracking Dataset Regeneration

Tian Dong, Yan Meng, Shaofeng Li +5

Training datasets have tremendous proprietary value and are vulnerable to unauthorized copying. Existing defenses mainly focus on tracking individual data points, but pay little at…

cs.CR2026

Trojan's Whisper: Stealthy Manipulation of OpenClaw through Injected Bootstrapped Guidance

Fazhong Liu, Zhuoyan Chen, Tu Lan +6

Autonomous coding agents are increasingly integrated into software development workflows, offering capabilities that extend beyond code suggestion to active system interaction and…

cs.CR2026

EmbTracker: Traceable Black-box Watermarking for Federated Language Models

Haodong Zhao, Jinming Hu, Yijie Bai +6

Federated Language Model (FedLM) allows a collaborative learning without sharing raw data, yet it introduces a critical vulnerability, as every untrustworthy client may leak the re…

cs.CR2026

SlowBA: An efficiency backdoor attack towards VLM-based GUI agents

Junxian Li, Tu Lan, Haozhen Tan +2

Modern vision-language-model (VLM) based graphical user interface (GUI) agents are expected not only to execute actions accurately but also to respond to user instructions with low…

cs.CR2025

Depth Gives a False Sense of Privacy: LLM Internal States Inversion

Tian Dong, Yan Meng, Shaofeng Li +3

Large Language Models (LLMs) are increasingly integrated into daily routines, yet they raise significant privacy and safety concerns. Recent research proposes collaborative inferen…