arXiv AI

SafeCRS: Personalized Safety Alignment for LLM-Based Conversational Recommender Systems

arXiv:2603. 03536v2 Announce Type: replace-cross Abstract: Current LLM-based conversational recommender systems (CRS) primarily optimize recommendation accuracy and user satisfaction.

arXiv AI

Jun 9

Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs

arXiv:2606. 09038v1 Announce Type: new Abstract: Large Language Models (LLMs) have enabled increasingly personalized interactions by adapting to users' preferences, contexts, and long-term histories.

By Yanyan Luo, Xue Han, Ruiqiao Bai, Xin Huang, Yitong Wang, Qian Hu, Qing Wang, Chunxu Zhao, Jie Liu, Cong Geng, Lehao Xing, Pengwei Hu, Junlan Feng

llms agents reinforcement-learning fine-tuning efficiency multimodal safety

arXiv AI

Jun 16

CHILLGuard: Towards Fine-Grained Chinese LLM Safety Guardrail with Scalable Data Construction and Model-aware Preference Alignment

arXiv:2606. 15396v1 Announce Type: cross Abstract: Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns.

By Wenbo Yu, Bohua Wang, Hao Fang, Kuofeng Gao, Jingru Zeng, Xiaochen Yang, Tianyi Zhang, Xiaoxiao Ma, Jiawei Kong, Hao Wu, Bin Chen, Shu-Tao Xia, Min Zhang

llms rag benchmarks safety

arXiv AI

Jul 7

Oyster-II: Reinforcement Learning for Constructive Safety Alignment in Large Language Models

arXiv:2607. 02914v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across diverse applications, yet ensuring their simultaneous safety, helpfulness, and trustworthiness remains a persistent challenge.

By Jiyang Guan, Yong Xie, Jun Chen, Jiexi Liu, Zipeng Ye, Defeng Li, Jiayu Shen, Jialing Tao, Hui Xue

llms reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jun 2

Dialectics of Alignment: Harnessing Unsafe Knowledge for Dynamic Safety Routing

arXiv:2606. 00686v1 Announce Type: new Abstract: The prevailing paradigm in large language model (LLM) alignment operates via erasure, filtering unsafe data or training models to strictly refuse harmful prompts.

By Maryam Hashemzadeh, Jerry Huang, Minseon Kim, Marc-Alexandre C\^ot\'e, Sarath Chandar

llms fine-tuning benchmarks safety

arXiv AI

Jul 23

Matching Ranks Over Probability Yields Truly Deep Safety Alignment

arXiv:2512. 05518v2 Announce Type: replace-cross Abstract: Open-source Large Language Models (LLMs) play a critical role in the democratization of AI, yet their "open" nature introduces more avenues for malicious actors to misuse them for harmful purposes.

By Jason Vega, Gagandeep Singh

llms fine-tuning safety

arXiv AI

Jun 16

RoTRAG: Rule of Thumb Reasoning for Conversation Harm Detection with Retrieval-Augmented Generation

arXiv:2604. 17301v2 Announce Type: replace-cross Abstract: Detecting harmful content in multi turn dialogue requires reasoning over the full conversational context rather than isolated utterances.

By Juhyeon Lee, Wonduk Seo, Junseo Koh, Seunghyun Lee, Haihua Chen, Yi Bu

llms rag benchmarks safety

arXiv AI

Jun 4

AICompanionBench: Benchmarking LLMs-as-Judges for AI Companion Safety

arXiv:2606. 04867v1 Announce Type: new Abstract: As AI companion platforms such as Replika and Character.

By Yanjing Ren, Reza Ebrahimi, TengTeng Ma

llms robotics benchmarks safety

arXiv AI

Jun 8

SafeGene: Reusable Adapters for Transferable Safety Alignment

arXiv:2606. 06519v1 Announce Type: new Abstract: Open-weight LLMs are increasingly fine-tuned into customized assistants, but downstream fine-tuning can weaken safety alignment and make models more vulnerable to malicious prompts, even when the training data is not intentionally harmful.

By Yanghan Wang, Zhiqiang Kou, Fu Feng, Jing Wang, Xin Geng

llms fine-tuning safety

Hugging Face Trending Papers

Jun 4

Safety Paradox: How Enhanced Safety Awareness Leaves LLMs Vulnerable to Posterior Attack

Large language models (LLMs) are rigorously aligned to refuse harmful requests, a process that inherently cultivates a latent capacity to evaluate and recognize unsafe content. In this work, we reveal that this advanced safety awareness inadvertently introduces a fatal vulnerability.

llms reinforcement-learning safety

arXiv Machine Learning

5d ago

HiRoute: Hierarchical Routed Prompt Tuning for Safety Alignment of Large Language Models

arXiv:2608. 12821v1 Announce Type: new Abstract: Large language models (LLMs) remain vulnerable to harmful requests and jailbreak attacks.

By Fangzhou Chen, Shiji Zhao, Mengyang Wang, Qihui Zhu, Ranjie Duan, Maoxun Yuan, Xingxing Wei

llms rag benchmarks safety

arXiv AI

Jun 16

Adaptive and Explicit safe: Triggering Latent Safety Awareness in Large Reasoning Models

arXiv:2606. 16808v1 Announce Type: new Abstract: While Large Reasoning Models (LRMs) excel at complex tasks, they remain highly vulnerable to sophisticated jailbreaks and direct harmful queries.

By Ke Miao, Jiaxin Li, Hongliang Chen, Yuke Hu, Zhan Qin

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

1d ago

HarmTrace: Anchor-Calibrated Decoupled Optimization for Fine-Grained Target Identification in Harmful Memes

arXiv:2608. 16622v1 Announce Type: cross Abstract: Multimodal harmful meme detection is typically formulated as image--text harmfulness classification.

By Yujia Li, Yiqun Zhang, Zihan Cheng, Yijie Huang, Tenglong Ye, Zihan Wang, Xiaocui Yang, Shi Feng, Yifei Zhang, Daling Wang

llms fine-tuning multimodal

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