2,104+ academic papers on AI security, attacks, and defenses
Showing 701–720 of 2,104 papers
Haoyun Yang, Ronghong Huang, Yong Fang +4 more
Transport Layer Security (TLS) is fundamental to secure online communication, yet vulnerabilities in certificate validation that enable...
Yilong Huang, Songze Li
Diffusion-based face swapping achieves state-of-the-art performance, yet it also exacerbates the potential harm of malicious face swapping to violate...
Ivan K. Tung, Yu Xiang Shi, Alex Chien +2 more
Creating attack paths for cyber defence exercises requires substantial expert effort. Existing automation requires vulnerability graphs or exploit...
Jaehee Kim, Pilsung Kang
Modern LLMs are increasingly accessed via black-box APIs, requiring users to transmit sensitive prompts, outputs, and fine-tuning data to external...
Yanlin Wang, Ziyao Zhang, Chong Wang +5 more
Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, but their proficiency in producing secure code remains a...
Mingqian Feng, Xiaodong Liu, Weiwei Yang +3 more
Large Language Models (LLMs) are typically evaluated for safety under single-shot or low-budget adversarial prompting, which underestimates...
Miao Lin, Feng Yu, Rui Ning +6 more
Deep neural networks are highly susceptible to backdoor attacks, yet most defense methods to date rely on balanced data, overlooking the pervasive...
Tanusree Debi, Wentian Zhu
Large language model (LLM) based agents are increasingly used to automate financial transactions, yet their reliance on contextual reasoning exposes...
Naen Xu, Jinghuai Zhang, Ping He +6 more
Large language models (LLMs) have been widely integrated into critical automated workflows, including contract review and job application processes....
Aarush Noheria, Yuguang Yao
Vision-language models (VLMs) have become central to tasks such as visual question answering, image captioning, and text-to-image generation....
Chanwoo Park, Chanwoo Kim
Evasion attacks pose significant threats to AI systems, exploiting vulnerabilities in machine learning models to bypass detection mechanisms. The...
Yavuz Bakman, Duygu Nur Yaldiz, Salman Avestimehr +1 more
Large Language Models (LLMs) are rarely static and are frequently updated in practice. A growing body of alignment research has shown that models...
Javier Carnerero-Cano, Luis Muñoz-González, Phillippa Spencer +1 more
Regression models are widely used in industrial processes, engineering, and in natural and physical sciences, yet their robustness to poisoning has...
Amirhossein Taherpour, Xiaodong Wang
Federated learning (FL) enables collaborative model training while preserving data privacy, yet both centralized and decentralized approaches face...
Pedro H. Barcha Correia, Ryan W. Achjian, Diego E. G. Caetano de Oliveira +5 more
The rapid advancement and widespread adoption of generative artificial intelligence (GenAI) and large language models (LLMs) has been accompanied by...
Gloria Felicia, Michael Eniolade, Jinfeng He +4 more
Existing agent safety benchmarks report binary accuracy, conflating early intervention with post-mortem analysis. A detector that flags a violation...
Xiaogeng Liu, Xinyan Wang, Yechao Zhang +5 more
Large reasoning models (LRMs) extend large language models with explicit multi-step reasoning traces, but this capability introduces a new class of...
Xiaoyu Xu, Minxin Du, Kun Fang +6 more
Large language models (LLMs) demonstrate impressive capabilities across diverse tasks but raise concerns about privacy, copyright, and harmful...
Mingyang Liao, Yichen Wan, shuchen wu +6 more
LLM-based role-playing has rapidly improved in fidelity, yet stronger adherence to persona constraints commonly increases vulnerability to jailbreak...
Ningyuan He, Ronghong Huang, Qianqian Tang +3 more
In-context learning (ICL) has become a powerful, data-efficient paradigm for text classification using large language models. However, its robustness...
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