2,104+ academic papers on AI security, attacks, and defenses
Showing 1121–1140 of 2,040 papers
Clear filtersZiqi Lin, Taiyu Hou
The use of large language model (LLM)-based AI chatbots among college students has increased rapidly, yet little is known about how individual...
Zehao Liu, Xi Lin
Large Language Models (LLMs) have gained considerable popularity and protected by increasingly sophisticated safety mechanisms. However, jailbreak...
Yueqiao Jin, Roberto Martinez-Maldonado, Dragan Gašević +1 more
Generative AI is increasingly embedded in collaborative learning, yet little is known about how AI personas shape learner agency when AI teammates...
Wei Qian, Chenxu Zhao, Yangyi Li +1 more
The rapid advancements in artificial intelligence (AI) have primarily focused on the process of learning from data to acquire knowledgeable learning...
Wang Bin, Ao Yang, Kedan Li +5 more
In the domain of software security testing, Directed Grey-Box Fuzzing (DGF) has garnered widespread attention for its efficient target localization...
Tung-Ling Li, Yuhao Wu, Hongliang Liu
Reward models and LLM-as-a-Judge systems are central to modern post-training pipelines such as RLHF, DPO, and RLAIF, where they provide scalar...
Yidong Chai, Yi Liu, Mohammadreza Ebrahimi +2 more
Social media platforms are plagued by harmful content such as hate speech, misinformation, and extremist rhetoric. Machine learning (ML) models are...
Abhivansh Gupta
As LLM-based agents grow more autonomous and multi-modal, ensuring they remain controllable, auditable, and faithful to deployer intent becomes...
Baolei Zhang, Minghong Fang, Zhuqing Liu +5 more
Federated Learning (FL) allows multiple clients to collaboratively train a model without sharing their private data. However, FL is vulnerable to...
Huixin Zhan
Genomic Foundation Models (GFMs), such as Evolutionary Scale Modeling (ESM), have demonstrated remarkable success in variant effect prediction....
Tomáš Souček, Pierre Fernandez, Hady Elsahar +5 more
Invisible watermarking is essential for tracing the provenance of digital content. However, training state-of-the-art models remains notoriously...
Nenad Tomašev, Matija Franklin, Julian Jacobs +2 more
AI safety and alignment research has predominantly been focused on methods for safeguarding individual AI systems, resting on the assumption of an...
Kai Hu, Abhinav Aggarwal, Mehran Khodabandeh +6 more
This paper introduces Jailbreak-Zero, a novel red teaming methodology that shifts the paradigm of Large Language Model (LLM) safety evaluation from a...
Xiao Li, Yue Li, Hao Wu +4 more
As large language models (LLMs) are increasingly adopted for code vulnerability detection, their reliability and robustness across diverse...
Himanshu Gharat, Himanshi Agrawal, Gourab K. Patro
Large Language Models (LLMs) have empowered AI agents with advanced capabilities for understanding, reasoning, and interacting across diverse tasks....
Hao Li, Yubing Ren, Yanan Cao +3 more
Benefiting from the superior capabilities of large language models in natural language understanding and generation, Embeddings-as-a-Service (EaaS)...
Safwan Shaheer, G. M. Refatul Islam, Mohammad Rafid Hamid +1 more
In this fast-evolving area of LLMs, our paper discusses the significant security risk presented by prompt injection attacks. It focuses on small...
Saksham Sahai Srivastava, Haoyu He
Large Language Model (LLM) agents increasingly rely on long-term memory and Retrieval-Augmented Generation (RAG) to persist experiences and refine...
Zhexi Lu, Hongliang Chi, Nathalie Baracaldo +3 more
Membership inference attacks (MIAs) pose a critical privacy threat to fine-tuned large language models (LLMs), especially when models are adapted to...
Hao Li, Yubing Ren, Yanan Cao +4 more
With the rapid development of cloud-based services, large language models (LLMs) have become increasingly accessible through various web platforms....
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