2,077+ academic papers on AI security, attacks, and defenses
Showing 541–560 of 2,027 papers
Clear filtersFengpeng Li, Kemou Li, Qizhou Wang +2 more
Concept erasure helps stop diffusion models (DMs) from generating harmful content; but current methods face robustness retention trade off....
Yunlong Lyu, Yixuan Tang, Peng Chen +4 more
Modern AI-integrated IDEs are shifting from passive code completion to proactive Next Edit Suggestions (NES). Unlike traditional autocompletion, NES...
Ying Song, Balaji Palanisamy
Graph-structured data underpin a wide spectrum of modern applications. However, complex graph topologies and homophilic patterns can facilitate...
Li Lu, Yanjie Zhao, Hongzhou Rao +2 more
Large Language Models (LLMs) have demonstrated remarkable proficiency in vulnerability detection. However, a critical reliability gap persists:...
Mengyao Du, Han Fang, Haokai Ma +4 more
Suffix-based jailbreak attacks append an adversarial suffix, i.e., a short token sequence, to steer aligned LLMs into unsafe outputs. Since suffixes...
Haoyang Hu, Zhejun Jiang, Yueming Lyu +3 more
Retrieval-augmented generation (RAG) is increasingly deployed in real-world applications, where its reference-grounded design makes outputs appear...
Yi Liu, Zhihao Chen, Yanjun Zhang +5 more
Third-party agent skills extend LLM-based agents with instruction files and executable code that run on users' machines. Skills execute with user...
Hema Karnam Surendrababu, Nithin Nagaraj
Machine Learning (ML) models, including Large Language Models (LLMs), are characterized by a range of system-level attributes such as security and...
Haipeng Li, Rongxuan Peng, Anwei Luo +3 more
The rapid advancement of AI-Generated Content (AIGC) technologies poses significant challenges for authenticity assessment. However, existing...
Minkyoo Song, Jaehan Kim, Myungchul Kang +3 more
Graph-based retrieval-augmented generation (Graph RAG) is increasingly deployed to support LLM applications by augmenting user queries with...
Sung-Hoon Yoon, Ruizhi Qian, Minda Zhao +2 more
Large Language Models (LLMs) have become integral to many domains, making their safety a critical priority. Prior jailbreaking research has explored...
Guowei Guan, Yurong Hao, Jiaming Zhang +6 more
Multimodal large language models (MLLMs) are pushing recommender systems (RecSys) toward content-grounded retrieval and ranking via cross-modal...
Mona Rajhans, Vishal Khawarey
Machine learning (ML) models are increasingly deployed in cybersecurity applications such as phishing detection and network intrusion prevention....
Daniel Fein, Max Lamparth, Violet Xiang +2 more
Reward Models (RMs) are crucial for online alignment of language models (LMs) with human preferences. However, RM-based preference-tuning is...
Junhyeok Lee, Han Jang, Kyu Sung Choi
Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems are increasingly integrated into clinical workflows; however, prompt...
Navita Goyal, Hal Daumé
Model steering, which involves intervening on hidden representations at inference time, has emerged as a lightweight alternative to finetuning for...
José Ramón Pareja Monturiol, Juliette Sinnott, Roger G. Melko +1 more
Machine learning in clinical settings must balance predictive accuracy, interpretability, and privacy. Models such as logistic regression (LR) offer...
Xin Chen, Jie Zhang, Florian Tramèr
Prompt injection is one of the most critical vulnerabilities in LLM agents; yet, effective automated attacks remain largely unexplored from an...
Rui Jia, Ruiyi Lan, Fengrui Liu +7 more
Large language models (LLMs) have advanced the development of personalized learning in education. However, their inherent generation mechanisms often...
Tao Huang, Rui Wang, Xiaofei Liu +3 more
%Large vision-language models (LVLMs) have shown substantial advances in multimodal understanding and generation. However, when presented with...
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