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20242026
most citedExploring the Robustness of In-Context Learning with Noisy Labels

4 citations · 7 across the 16 of their papers we have counts for

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6 papers · 1 filter

cs.CR2026

MOSAIC: Knowledge-Guided CLI Command Composition Attack in LLM Coding Agents

Jiangrong Wu, Huaijin Wang, Yihao Zhang +2

LLM coding agents increasingly complete development tasks by issuing ordinary CLI commands. Following the Unix design, these commands cooperate through shared operating-system stat…

cs.SC2026

Symbolic-Neural Soft-Logic Reasoning: Towards Robust and Verifiable Thinking Chains via Cooperative Evolution

Rui Wang, Zeming Wei, Yihao Zhang +1

Large Language Models (LLMs) have demonstrated impressive progress in complex reasoning tasks, largely driven by the Chain-of-Thought (CoT) paradigm, which decomposes difficult pro…

cs.CR2026

VOW: Verifiable and Oblivious Watermark Detection for Large Language Models

Xiaokun Luan, Yihao Zhang, Pengcheng Su +4

Regulation requires generated text to be marked and detectable, but symmetric-key watermarks leave detection to the key-holding provider. We study first-party provenance audit (FPA…

cs.CR2026

The Salami Slicing Threat: Exploiting Cumulative Risks in LLM Systems

Yihao Zhang, Kai Wang, Jiangrong Wu +7

Large Language Models (LLMs) face prominent security risks from jailbreaking, a practice that manipulates models to bypass built-in security constraints and generate unethical or u…

cs.CR2026

AgentWorm: Self-Propagating Attacks Across LLM Agent Ecosystems

Yihao Zhang, Zeming Wei, Xiaokun Luan +7

Autonomous LLM-based agents increasingly operate as long-running processes forming densely interconnected multi-agent ecosystems, whose security properties remain largely unexplore…

cs.SE2026

RACC: Representation-Aware Coverage Criteria for LLM Safety Testing

Zeming Wei, Zhixin Zhang, Chengcan Wu +3

Large Language Models (LLMs) face severe safety risks from jailbreak attacks, yet current safety testing largely relies on static datasets and lacks systematic criteria to evaluate…