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20232026
most citedEguard: Defending LLM Embeddings Against Inversion Attacks via Text Mutual Information Optimization

2 citations · 9 across the 18 of their papers we have counts for

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13 papers · 1 filter

cs.CR2026

ActBench: Self-Evolving Benchmark of Behavioral Safety in Cowork Agents

Hongwei Yao, Yiming Liu, Meihui Chen +6

Cowork agents may complete benign tasks while disclosing protected data, manipulating unauthorized state, invocate unauthorized API. We define behavioral safety and introduce ActBe…

cs.CR2026★ 1 cited

Combating Knowledge Corruption in Agent Systems: A Byzantine-Tolerant Secure Collaborative RAG Framework

Zhaoqi Wang, Daqing He, Zijian Zhang +13

While retrieval-augmented generation systems partially address the hallucination issues in large language models, it also introduces new vulnerabilities to knowledge corruption att…

cs.CR2026

Red-Teaming Agent Execution Contexts: Open-World Security Evaluation on OpenClaw

Hongwei Yao, Yiming Liu, Yiling He +1

Agentic language-model systems increasingly rely on mutable execution contexts, including files, memory, tools, skills, and auxiliary artifacts, creating security risks beyond expl…

cs.CR2026

AttriGuard: Defeating Indirect Prompt Injection in LLM Agents via Causal Attribution of Tool Invocations

Yu He, Haozhe Zhu, Yiming Li +4

LLM agents are highly vulnerable to Indirect Prompt Injection (IPI), where adversaries embed malicious directives in untrusted tool outputs to hijack execution. Most existing defen…

cs.CR2025

Black-Box Guardrail Reverse-engineering Attack

Hongwei Yao, Yun Xia, Shuo Shao +3

Large language models (LLMs) increasingly employ guardrails to enforce ethical, legal, and application-specific constraints on their outputs. While effective at mitigating harmful…

cs.CR2025

PromptCOS: Towards Content-only System Prompt Copyright Auditing for LLMs

Yuchen Yang, Yiming Li, Hongwei Yao +6

System prompts are critical for shaping the behavior and output quality of large language model (LLM)-based applications, driving substantial investment in optimizing high-quality…