2,104+ academic papers on AI security, attacks, and defenses
Showing 861–880 of 2,104 papers
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...
Yuanxiang Liu, Songze Li, Xiaoke Guo +4 more
Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities but often grapple with reliability challenges like hallucinations....
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...
Kaiyu Zhou, Yongsen Zheng, Yicheng He +5 more
The agent--tool interaction loop is a critical attack surface for modern Large Language Model (LLM) agents. Existing denial-of-service (DoS) attacks...
Haoze Guo, Ziqi Wei
Retrieval-augmented generation (RAG) systems put more and more emphasis on grounding their responses in user-generated content found on the Web,...
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...
Jonah Ghebremichael, Saastha Vasan, Saad Ullah +6 more
Static Application Security Testing (SAST) tools using taint analysis are widely viewed as providing higher-quality vulnerability detection results...
Xinrui Zhang, Pincan Zhao, Jason Jaskolka +2 more
Machine Learning (ML) has emerged as a pivotal technology in the operation of large and complex systems, driving advancements in fields such as...
Federico Pierucci, Marcello Galisai, Marcantonio Syrnikov Bracale +6 more
As LLM-based systems increasingly operate as agents embedded within human social and technical systems, alignment can no longer be treated as a...
Hao Wang, Yanting Wang, Hao Li +2 more
Large Language Models (LLMs) have achieved remarkable capabilities but remain vulnerable to adversarial ``jailbreak'' attacks designed to bypass...
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...
Xingjun Ma, Yixu Wang, Hengyuan Xu +18 more
The rapid evolution of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has driven major gains in reasoning, perception, and...
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...
Yi Liu, Weizhe Wang, Ruitao Feng +5 more
The rise of AI agent frameworks has introduced agent skills, modular packages containing instructions and executable code that dynamically extend...
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...
Hao Li, Yankai Yang, G. Edward Suh +2 more
Large Language Models (LLMs) have enabled the development of powerful agentic systems capable of automating complex workflows across various fields....
Yutao Mou, Zhangchi Xue, Lijun Li +4 more
While LLM-based agents can interact with environments via invoking external tools, their expanded capabilities also amplify security risks....
Jiawen Zhang, Yangfan Hu, Kejia Chen +7 more
Fine-tuning is an essential and pervasive functionality for applying large language models (LLMs) to downstream tasks. However, it has the potential...
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