2,077+ academic papers on AI security, attacks, and defenses
Showing 1601–1620 of 2,077 papers
Sathwik Narkedimilli, N V Saran Kumar, Aswath Babu H +4 more
Current quantum machine learning approaches often face challenges balancing predictive accuracy, robustness, and interpretability. To address this,...
Viktoriia Zinkovich, Anton Antonov, Andrei Spiridonov +6 more
Multimodal large language models (MLLMs) have shown impressive capabilities in vision-language tasks such as reasoning segmentation, where models...
Yiding Wang, Yuxuan Chen, Fanxu Meng +3 more
Since real-world legal experiments are often costly or infeasible, simulating legal societies with Artificial Intelligence (AI) systems provides an...
Yifan Wu, Xuewei Feng, Yuxiang Yang +1 more
As the core of the Internet infrastructure, the TCP/IP protocol stack undertakes the task of network data transmission. However, due to the...
María Sanz-Gómez, Víctor Mayoral-Vilches, Francesco Balassone +3 more
Cybersecurity spans multiple interconnected domains, complicating the development of meaningful, labor-relevant benchmarks. Existing benchmarks...
Xinwei Wu, Heng Liu, Jiang Zhou +5 more
Large Language Models (LLMs) have advanced machine translation but remain vulnerable to hallucinations. Unfortunately, existing MT benchmarks are not...
Ziyao Cui, Minxing Zhang, Jian Pei
Privacy concerns have become increasingly critical in modern AI and data science applications, where sensitive information is collected, analyzed,...
Xingyu Zhu, Beier Zhu, Shuo Wang +2 more
Vision-language models (VLMs) such as CLIP demonstrate strong generalization in zero-shot classification but remain highly vulnerable to adversarial...
Yufan Liu, Wanqian Zhang, Huashan Chen +4 more
Despite rapid advancements in text-to-image (T2I) models, their safety mechanisms are vulnerable to adversarial prompts, which maliciously generate...
Vladyslav Larin, Ihor Naumenko, Aleksei Ivashov +2 more
As centralized AI hits compute ceilings and diminishing returns from ever-larger training runs, meeting demand requires an inference layer that...
Yifan Zhang, Xin Zhang
Directed greybox fuzzing (DGF) aims to efficiently trigger bugs at specific target locations by prioritizing seeds whose execution paths are more...
Hiromu Takahashi, Shotaro Ishihara
We propose Fast-MIA (https://github.com/Nikkei/fast-mia), a Python library for efficiently evaluating membership inference attacks (MIA) against...
Yuchong Xie, Zesen Liu, Mingyu Luo +7 more
Modern coding agents integrated into IDEs orchestrate powerful tools and high-privilege system access, creating a high-stakes attack surface. Prior...
Bin Wang, Zexin Liu, Hao Yu +6 more
The Model Context Protocol (MCP) has emerged as a standardized interface enabling seamless integration between Large Language Models (LLMs) and...
Myeongseob Ko, Nikhil Reddy Billa, Adam Nguyen +3 more
The memorization of training data in large language models (LLMs) poses significant privacy and copyright concerns. Existing data extraction methods,...
Zesen Liu, Zhixiang Zhang, Yuchong Xie +1 more
LLM-powered agents often use prompt compression to reduce inference costs, but this introduces a new security risk. Compression modules, which are...
Bin Wang, YiLu Zhong, MiDi Wan +4 more
Large language models (LLMs) have become indispensable for automated code generation, yet the quality and security of their outputs remain a critical...
Armin Gerami, Kazem Faghih, Ramani Duraiswami
Retrieval Augmented Generation (RAG) enhances Large Language Models (LLMs) by connecting them to external knowledge, improving accuracy and reducing...
Jiaxiang Liu, Jiawei Du, Xiao Liu +2 more
Pre-trained vision-language models (VLMs) such as CLIP have demonstrated strong zero-shot capabilities across diverse domains, yet remain highly...
Xiaofeng Zhu, Yunshen Zhou
Microsoft Copilot suites serve as the universal entry point for various agents skilled in handling important tasks, ranging from assisting a customer...
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