2,560+ academic papers on AI security, attacks, and defenses
Showing 761–780 of 982 papers
Clear filtersAashray Reddy, Andrew Zagula, Nicholas Saban
Large Language Models (LLMs) remain vulnerable to jailbreaking attacks where adversarial prompts elicit harmful outputs. Yet most evaluations focus...
Chen-Wei Chang, Shailik Sarkar, Hossein Salemi +7 more
Scam detection remains a critical challenge in cybersecurity as adversaries craft messages that evade automated filters. We propose a Hierarchical...
Daniyal Ganiuly, Assel Smaiyl
Large Language Models (LLMs) are increasingly used in intelligent systems that perform reasoning, summarization, and code generation. Their ability...
Hamin Koo, Minseon Kim, Jaehyung Kim
Identifying the vulnerabilities of large language models (LLMs) is crucial for improving their safety by addressing inherent weaknesses. Jailbreaks,...
Xin Liu, Aoyang Zhou, Aoyang Zhou
Visual-Language Pre-training (VLP) models have achieved significant performance across various downstream tasks. However, they remain vulnerable to...
Berk Atil, Rebecca J. Passonneau, Fred Morstatter
Large language models (LLMs) undergo safety alignment after training and tuning, yet recent work shows that safety can be bypassed through jailbreak...
Kasimir Schulz, Amelia Kawasaki, Leo Ring
Large language models (LLMs) are widely deployed across various applications, often with safeguards to prevent the generation of harmful or...
Peng Ding, Jun Kuang, Wen Sun +5 more
Large language models (LLMs) remain vulnerable to jailbreaking attacks despite their impressive capabilities. Investigating these weaknesses is...
Phil Blandfort, Robert Graham
Activation probes are attractive monitors for AI systems due to low cost and latency, but their real-world robustness remains underexplored. We ask:...
Ruofan Liu, Yun Lin, Zhiyong Huang +1 more
Large language models (LLMs) are increasingly integrated into IT infrastructures, where they process user data according to predefined instructions....
Xin Yao, Haiyang Zhao, Yimin Chen +3 more
The Contrastive Language-Image Pretraining (CLIP) model has significantly advanced vision-language modeling by aligning image-text pairs from...
Kayua Oleques Paim, Rodrigo Brandao Mansilha, Diego Kreutz +2 more
The rapid proliferation of Large Language Models (LLMs) has raised significant concerns about their security against adversarial attacks. In this...
David Lüdke, Tom Wollschläger, Paul Ungermann +2 more
We introduce a novel framework that transforms the resource-intensive (adversarial) prompt optimization problem into an \emph{efficient, amortized...
Chenghao Du, Quanfeng Huang, Tingxuan Tang +3 more
Large Language Models (LLMs) have transformed software development, enabling AI-powered applications known as LLM-based agents that promise to...
Alex Irpan, Alexander Matt Turner, Mark Kurzeja +2 more
An LLM's factuality and refusal training can be compromised by simple changes to a prompt. Models often adopt user beliefs (sycophancy) or satisfy...
David Schmotz, Sahar Abdelnabi, Maksym Andriushchenko
Enabling continual learning in LLMs remains a key unresolved research challenge. In a recent announcement, a frontier LLM company made a step towards...
Haohua Duan, Liyao Xiang, Xin Zhang
Watermarking schemes for large language models (LLMs) have been proposed to identify the source of the generated text, mitigating the potential...
Lisha Shuai, Jiuling Dong, Nan Zhang +5 more
Local Differential Privacy (LDP) is a widely adopted privacy-protection model in the Internet of Things (IoT) due to its lightweight, decentralized,...
Guangzhi Su, Shuchang Huang, Yutong Ke +3 more
Multimodal large language models (MLLMs) have achieved impressive performance across diverse tasks by jointly reasoning over textual and visual...
Svetlana Churina, Niranjan Chebrolu, Kokil Jaidka
We show that continual pretraining on plausible misinformation can overwrite specific factual knowledge in large language models without degrading...
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