2,560+ academic papers on AI security, attacks, and defenses
Showing 201–220 of 442 papers
Clear filtersDezhang Kong, Zhuxi Wu, Shiqi Liu +8 more
LLM-based web agents have become increasingly popular for their utility in daily life and work. However, they exhibit critical vulnerabilities when...
Xiaohui Hu, Wun Yu Chan, Yuejie Shi +5 more
Smart contract security is paramount, but identifying intricate business logic vulnerabilities remains a persistent challenge because existing...
Alireza Salemi, Hamed Zamani
Personalization is crucial for aligning Large Language Model (LLM) outputs with individual user preferences and background knowledge....
Marton Szep, Jorge Marin Ruiz, Georgios Kaissis +4 more
Fine-tuning Large Language Models (LLMs) on sensitive datasets carries a substantial risk of unintended memorization and leakage of Personally...
Dongshen Peng, Yi Wang, Austin Schoeffler +2 more
Large language models (LLMs) show promise in clinical decision support yet risk acquiescing to patient pressure for inappropriate care. We introduce...
Khoa Nguyen, Khiem Ton, NhatHai Phan +6 more
Although boosting software development performance, large language model (LLM)-powered code generation introduces intellectual property and data...
Andres Karjus, Kais Allkivi, Silvia Maine +3 more
Large language models (LLMs) enable rapid and consistent automated evaluation of open-ended exam responses, including dimensions of content and...
Akriti Vij, Benjamin Chua, Darshini Ramiah +43 more
As frontier AI models are deployed globally, it is essential that their behaviour remains safe and reliable across diverse linguistic and cultural...
Kristen Moore, Diksha Goel, Cody James Christopher +5 more
Realistic network traffic simulation is critical for evaluating intrusion detection systems, stress-testing network protocols, and constructing...
Xiaonan Jing, Gongqing Wu, Xingrui Zhuo +2 more
Open-domain Relational Triplet Extraction (ORTE) is the foundation for mining structured knowledge without predefined schemas. Despite the impressive...
Muhammad Khalifa, Lajanugen Logeswaran, Jaekyeom Kim +6 more
Large language models (LLMs) are increasingly used as judges to evaluate agent performance, particularly in non-verifiable settings where judgments...
Linbo Cao, Lihao Sun, Yang Yue
Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of actions with real-world impacts beyond text generation. While...
Yiyang Lu, Jinwen He, Yue Zhao +2 more
Large Language Models (LLMs) are widely integrated into interactive systems such as dialogue agents and task-oriented assistants. This growing...
Johannes Kaiser, Alexander Ziller, Eleni Triantafillou +2 more
Individual Differential Privacy (iDP) promises users control over their privacy, but this promise can be broken in practice. We reveal a previously...
Wenqi Zhang, Yulin Shen, Changyue Jiang +3 more
Large foundation models are integrated into Computer Use Agents (CUAs), enabling autonomous interaction with operating systems through graphical user...
Arunkumar V, Gangadharan G. R., Rajkumar Buyya
Artificial Intelligence is moving from models that only generate text to Agentic AI, where systems behave as autonomous entities that can perceive,...
Huanyi Ye, Jiale Guo, Ziyao Liu +1 more
RAG has emerged as a key technique for enhancing response quality of LLMs without high computational cost. In traditional architectures, RAG services...
Yixuan Du, Chenxiao Yu, Haoyan Xu +3 more
Vision-Language Models (VLMs) are rapidly replacing unimodal encoders in modern retrieval and recommendation systems. While their capabilities are...
Xiaomei Zhang, Zhaoxi Zhang, Leo Yu Zhang +3 more
Visual token compression is widely adopted to improve the inference efficiency of Large Vision-Language Models (LVLMs), enabling their deployment in...
Jun Liu, Leo Yu Zhang, Fengpeng Li +2 more
Hard-label black-box settings, where only top-1 predicted labels are observable, pose a fundamentally constrained yet practically important feedback...
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