2,077+ academic papers on AI security, attacks, and defenses
Showing 2021–2040 of 2,077 papers
Zhaoqi Wang, Daqing He, Zijian Zhang +4 more
Large language models (LLMs) have demonstrated remarkable capabilities, yet they also introduce novel security challenges. For instance, prompt...
Luxuan Zhang, Douglas Jiang, Qinglong Wang +2 more
Large language models (LLMs) have shown strong ability in generating rich representations across domains such as natural language processing and...
Zeyu Shen, Basileal Imana, Tong Wu +3 more
Retrieval-Augmented Generation (RAG) enhances Large Language Models by grounding their outputs in external documents. These systems, however, remain...
Charles E. Gagnon, Steven H. H. Ding, Philippe Charland +1 more
Binary code similarity detection is a core task in reverse engineering. It supports malware analysis and vulnerability discovery by identifying...
Han Yan, Zheyuan Liu, Meng Jiang
With the rapid advancement of large language models, Machine Unlearning has emerged to address growing concerns around user privacy, copyright...
Francesco Marchiori, Rohan Sinha, Christopher Agia +4 more
Large Language Models (LLMs) and Vision-Language Models (VLMs) are increasingly deployed in robotic environments but remain vulnerable to...
Xiaotian Zou
Multimodal Large Language Models (MLLMs) have transformed text-to-image workflows, allowing designers to create novel visual concepts with...
M. Z. Haider, Tayyaba Noreen, M. Salman
Blockchain Business applications and cryptocurrencies such as enable secure, decentralized value transfer, yet their pseudonymous nature creates...
Zi Liang, Qingqing Ye, Xuan Liu +3 more
Synthetic data refers to artificial samples generated by models. While it has been validated to significantly enhance the performance of large...
Javad Forough, Mohammad Maheri, Hamed Haddadi
Large Language Models (LLMs) are increasingly susceptible to jailbreak attacks, which are adversarial prompts that bypass alignment constraints and...
Jeongyeon Hwang, Sangdon Park, Jungseul Ok
Watermarking offers a promising solution for detecting LLM-generated content, yet its robustness under realistic query-free (black-box) evasion...
Aashnan Rahman, Abid Hasan, Sherajul Arifin +5 more
Federated learning (FL) enables privacy-preserving model training by keeping data decentralized. However, it remains vulnerable to label-flipping...
Roie Kazoom, Yuval Ratzabi, Etamar Rothstein +1 more
Adversarial robustness in structured data remains an underexplored frontier compared to vision and language domains. In this work, we introduce a...
Hwan Chang, Yonghyun Jun, Hwanhee Lee
The growing deployment of large language model (LLM) based agents that interact with external environments has created new attack surfaces for...
Xingyu Li, Juefei Pu, Yifan Wu +13 more
Open-source software projects are foundational to modern software ecosystems, with the Linux kernel standing out as a critical exemplar due to its...
Antreas Ioannou, Andreas Shiamishis, Nora Hollenstein +1 more
In an era dominated by Large Language Models (LLMs), understanding their capabilities and limitations, especially in high-stakes fields like law, is...
Pooneh Mousavi, Lovenya Jain, Mirco Ravanelli +1 more
Large Audio Language Models (LALMs) integrate audio encoders with pretrained Large Language Models to perform complex multimodal reasoning tasks....
Wonjun Lee, Haon Park, Doehyeon Lee +2 more
Along with the rapid advancement of numerous Text-to-Video (T2V) models, growing concerns have emerged regarding their safety risks. While recent...
Stina Sundstedt, Mattias Wingren, Susanne Hägglund +1 more
Preschool children with language vulnerabilities -- such as developmental language disorders or immigration related language challenges -- often...
Nakyeong Yang, Dong-Kyum Kim, Jea Kwon +3 more
Large language models trained on web-scale data can memorize private or sensitive knowledge, raising significant privacy risks. Although some...
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