2,560+ academic papers on AI security, attacks, and defenses
Showing 481–500 of 726 papers
Clear filtersJiani Liu, Yixin He, Lanlan Fan +5 more
Navigation agents powered by large language models (LLMs) convert natural language instructions into executable plans and actions. Compared to...
Bingxin Xu, Yuzhang Shang, Binghui Wang +1 more
Vision-Language-Action (VLA) models are increasingly deployed in safety-critical robotic applications, yet their security vulnerabilities remain...
Asen Dotsinski, Panagiotis Eustratiadis
As open-weight large language models (LLMs) increase in capabilities, safeguarding them against malicious prompts and understanding possible attack...
Diego Gosmar, Deborah A. Dahl
Prompt injection remains a central obstacle to the safe deployment of large language models, particularly in multi-agent settings where intermediate...
Xiaolei Zhang, Xiaojun Jia, Liquan Chen +1 more
Introducing reasoning models into Retrieval-Augmented Generation (RAG) systems enhances task performance through step-by-step reasoning, logical...
Advije Rizvani, Giovanni Apruzzese, Pavel Laskov
Large Language Models (LLMs) are increasingly adopted in the financial domain. Their exceptional capabilities to analyse textual data make them...
Jesus-German Ortiz-Barajas, Jonathan Tonglet, Vivek Gupta +1 more
Multimodal large language models (MLLMs) are increasingly used to automate chart generation from data tables, enabling efficient data analysis and...
Murat Bilgehan Ertan, Emirhan Böge, Min Chen +2 more
As large language models (LLMs) are trained on increasingly opaque corpora, membership inference attacks (MIAs) have been proposed to audit whether...
Zhixin Xie, Xurui Song, Jun Luo
The demand of customized large language models (LLMs) has led to commercial LLMs offering black-box fine-tuning APIs, yet this convenience introduces...
Anirudh Sekar, Mrinal Agarwal, Rachel Sharma +4 more
Prompt injection attacks have become an increasing vulnerability for LLM applications, where adversarial prompts exploit indirect input channels such...
János Kramár, Joshua Engels, Zheng Wang +4 more
Frontier language model capabilities are improving rapidly. We thus need stronger mitigations against bad actors misusing increasingly powerful...
Marco Arazzi, Antonino Nocera
Backdoored and privacy-leaking deep neural networks pose a serious threat to the deployment of machine learning systems in security-critical...
Aiman Al Masoud, Marco Arazzi, Antonino Nocera
Retrieval-Augmented Generation (RAG) has attracted significant attention due to its ability to combine the generative capabilities of Large Language...
Yipu Dou, Wang Yang
Large language model (LLM) safety evaluation is moving from content moderation to action security as modern systems gain persistent state, tool...
Chetan Pathade, Vinod Dhimam, Sheheryar Ahmad +1 more
Serverless computing has achieved widespread adoption, with over 70% of AWS organizations using serverless solutions [1]. Meanwhile, machine learning...
Yinzhi Zhao, Ming Wang, Shi Feng +3 more
Large language models (LLMs) have achieved impressive performance across natural language tasks and are increasingly deployed in real-world...
Christina Lu, Jack Gallagher, Jonathan Michala +2 more
Large language models can represent a variety of personas but typically default to a helpful Assistant identity cultivated during post-training. We...
Luoming Hu, Jingjie Zeng, Liang Yang +1 more
Enhancing the moral alignment of Large Language Models (LLMs) is a critical challenge in AI safety. Current alignment techniques often act as...
Yuansen Liu, Yixuan Tang, Anthony Kum Hoe Tun
Current LLM safety research predominantly focuses on mitigating Goal Hijacking, preventing attackers from redirecting a model's high-level objective...
Murat Bilgehan Ertan, Marten van Dijk
Differentially Private Stochastic Gradient Descent (DP-SGD) is the dominant paradigm for private training, but its fundamental limitations under...
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