activity
20192026
most citedClean-Label Backdoor Attacks on Video Recognition Models

17 citations · 64 across the 51 of their papers we have counts for

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Showing cs.CLShow all

7 papers · 1 filter

cs.CL2026

HarmProfile: Characterizing Harmful Distributions in Frontier LLMs

Zhouyuan Ma, Yutao Wu, Hanxun Huang +6

Frontier large language models (LLMs) safety evaluation has largely treated harmful generation as an attack outcome rather than as an object of analysis. Consequently, little is kn…

cs.CL2026

OpenART: Scaling Agent Red Teaming via Open-Ended Environment Evolution

Yunhao Chen, Xin Wang, Yixu Wang +6

AI agents operate in persistent environments where early state changes can influence decisions far into the future. Unlike conventional language-model interactions, agent behavior…

cs.CL2026

BabelSafe: A Policy-Grounded Multilingual Safety Benchmark for LLMs

Yunhan Zhao, Zhaorun Chen, Xingjun Ma +2

As Large Language Models (LLMs) are increasingly deployed in cross-linguistic contexts, ensuring safety across diverse regulatory and cultural environments has become a critical ch…

cs.CL2026

Internal Safety Collapse in Frontier Large Language Models

Yutao Wu, Xiao Liu, Yifeng Gao +7

This work identifies a critical failure mode in frontier large language models (LLMs), which we term Internal Safety Collapse (ISC): under certain task conditions, models enter a s…

cs.CL2026

SciAgentGym: Benchmarking Multi-Step Scientific Tool-use in LLM Agents

Yujiong Shen, Yajie Yang, Zhiheng Xi +17

Scientific reasoning inherently demands integrating sophisticated toolkits to navigate domain-specific knowledge. Yet, current benchmarks largely overlook agents' ability to orches…

cs.CL2024★ 3 cited

Whose Side Are You On? Investigating the Political Stance of Large Language Models

Pagnarasmey Pit, Xingjun Ma, Mike Conway +6

Large Language Models (LLMs) have gained significant popularity for their application in various everyday tasks such as text generation, summarization, and information retrieval. A…

cs.CL2023★ 1 cited

Fake Alignment: Are LLMs Really Aligned Well?

Yixu Wang, Yan Teng, Kexin Huang +7

The growing awareness of safety concerns in large language models (LLMs) has sparked considerable interest in the evaluation of safety. This study investigates an under-explored is…