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20232026
most citedDefending Large Language Models Against Jailbreaking Attacks Through Goal Prioritization

7 citations · 13 across the 15 of their papers we have counts for

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7 papers · 1 filter

cs.CL2025★ 1 cited

When Smiley Turns Hostile: Interpreting How Emojis Trigger LLMs' Toxicity

Shiyao Cui, Xijia Feng, Yingkang Wang +6

Emojis are globally used non-verbal cues in digital communication, and extensive research has examined how large language models (LLMs) understand and utilize emojis across context…

cs.LG2025

Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers

Xingyue Huang, Rishabh, Gregor Franke +43

Recent advances in Large Language Models (LLMs) have shown that their reasoning capabilities can be significantly improved through Reinforcement Learning with Verifiable Reward (RL…

cs.CL2025

Be Careful When Fine-tuning On Open-Source LLMs: Your Fine-tuning Data Could Be Secretly Stolen!

Zhexin Zhang, Yuhao Sun, Junxiao Yang +4

Fine-tuning on open-source Large Language Models (LLMs) with proprietary data is now a standard practice for downstream developers to obtain task-specific LLMs. Surprisingly, we re…

cs.CL2025

How Should We Enhance the Safety of Large Reasoning Models: An Empirical Study

Zhexin Zhang, Xian Qi Loye, Victor Shea-Jay Huang +8

Large Reasoning Models (LRMs) have achieved remarkable success on reasoning-intensive tasks such as mathematics and programming. However, their enhanced reasoning capabilities do n…

cs.AI2025

BARREL: Boundary-Aware Reasoning for Factual and Reliable LRMs

Junxiao Yang, Jinzhe Tu, Haoran Liu +9

Recent advances in Large Reasoning Models (LRMs) have shown impressive capabilities in mathematical and logical reasoning. However, current LRMs rarely admit ignorance or respond w…

cs.LG2025

Guiding not Forcing: Enhancing the Transferability of Jailbreaking Attacks on LLMs via Removing Superfluous Constraints

Junxiao Yang, Zhexin Zhang, Shiyao Cui +2

Jailbreaking attacks can effectively induce unsafe behaviors in Large Language Models (LLMs); however, the transferability of these attacks across different models remains limited.…

cs.CL2025

AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement

Zhexin Zhang, Leqi Lei, Junxiao Yang +13

As AI models are increasingly deployed across diverse real-world scenarios, ensuring their safety remains a critical yet underexplored challenge. While substantial efforts have bee…