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20152026
most citedShielding Federated Learning: Robust Aggregation with Adaptive Client Selection

50 citations · 237 across the 42 of their papers we have counts for

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26 papers · 1 filter

cs.CR2026

MaliciousSkillBench: A Comprehensive Benchmark for Malicious Agent Skill Detection

Yue Wang, Yi Liu, Gelei Deng +4

Agent Skills extend LLM agents with reusable instruction packages that may also include scripts, resources, and service configuration. This creates a direct distribution channel fo…

cs.CR2026

Tracing Provenance and Detecting Tampering with Complementary LLM Watermarks

Xiaoyan Feng, Yanjun Zhang, He Zhang +2

Watermarking LLM-generated text is an important task for tracing its provenance. Existing LLM watermarks preserve provenance under editing, but this same robustness allows an adver…

cs.CR2026

Defending Jailbreak Attacks on Large Language Models via Manifold Trajectory Kinetics

Hangtao Zhang, Yucheng Zhao, Sishun Liu +8

Jailbreak prompts can bypass alignment guardrails in large language models (LLMs) and elicit unsafe outputs, making reliable deployment-time detection critical. Prior detection app…

cs.CR2026

SNARE: Adaptive Scenario Synthesis for Eliciting Overeager Behavior in Coding Agents

Yubin Qu, Yi Liu, Gelei Deng +4

A coding agent executes a benign task as a sequence of shell, file, and network actions, any of which can quietly exceed the authorized scope while the task still completes. We cal…

cs.CR2026

MIRAGE: Context-Aware Prompt Injection against Mobile GUI Agents via User-Generated Content

Ruoqi Guo, Yi Liu, Gelei Deng +7

Mobile graphical user interface (GUI) agents driven by vision-language models (VLMs) perceive the screen as rendered pixels and choose actions from what they see, so they cannot re…

cs.CR2026

How Your Credentials Are Leaked by LLM Agent Skills: An Empirical Study

Zhihao Chen, Ying Zhang, Yi Liu +7

Large Language Model (LLM) agents increasingly rely on third-party skills that operate within privileged execution environments and routinely handle sensitive credentials, yet how…