2,077+ academic papers on AI security, attacks, and defenses
Showing 141–160 of 223 papers
Clear filtersFenghua Weng, Chaochao Lu, Xia Hu +2 more
As multimodal reasoning improves the overall capabilities of Large Vision Language Models (LVLMs), recent studies have begun to explore...
Zhibo Liang, Tianze Hu, Zaiye Chen +1 more
Autonomous Large Language Model (LLM) agents exhibit significant vulnerability to Indirect Prompt Injection (IPI) attacks. These attacks hijack agent...
Saeid Jamshidi, Kawser Wazed Nafi, Arghavan Moradi Dakhel +3 more
The Model Context Protocol (MCP) enables Large Language Models to integrate external tools through structured descriptors, increasing autonomy in...
Arush Sachdeva, Rajendraprasad Saravanan, Gargi Sarkar +2 more
Cybercrime increasingly exploits human cognitive biases in addition to technical vulnerabilities, yet most existing analytical frameworks focus...
Xianzong Wu, Xiaohong Li, Lili Quan +1 more
Large language models(LLMs) are increasingly expanding their real-world applications across domains, e.g., question answering, autonomous driving,...
Yuhang Huang, Junchao Li, Boyang Ma +6 more
Embodied AI systems integrate language models with real world sensing, mobility, and cloud connected mobile apps. Yet while model jailbreaks have...
Zag ElSayed, Craig Erickson, Ernest Pedapati
Healthcare AI systems have historically faced challenges in merging contextual reasoning, long-term state management, and human-verifiable workflows...
M Zeeshan, Saud Satti
Multimodal Artificial Intelligence (AI) systems, particularly Vision-Language Models (VLMs), have become integral to critical applications ranging...
Eranga Bandara, Amin Hass, Ross Gore +8 more
AI agent-based systems are becoming increasingly integral to modern software architectures, enabling autonomous decision-making, dynamic task...
Peter B. Walker, Hannah Davidson, Aiden Foster +3 more
Large Language Models (LLMs) have transformed natural language processing and hold growing promise for advancing science, healthcare, and...
Ruichao Liang, Le Yin, Jing Chen +5 more
LLM-based multi-agent systems (MASs) have reshaped the digital landscape with their emergent coordination and problem-solving capabilities. However,...
Rafflesia Khan, Declan Joyce, Mansura Habiba
The rapid deployment of large language model (LLM)-based agents introduces a new class of risks, driven by their capacity for autonomous planning,...
Mihai Christodorescu, Earlence Fernandes, Ashish Hooda +11 more
In recent years, agentic artificial intelligence (AI) systems are becoming increasingly widespread. These systems allow agents to use various tools,...
Kaixiang Wang, Zhaojiacheng Zhou, Bunyod Suvonov +2 more
Large Language Model (LLM)-based Multi-Agent Systems (MAS) are susceptible to linguistic attacks that can trigger cascading failures across the...
Shaona Ghosh, Barnaby Simkin, Kyriacos Shiarlis +9 more
This paper introduces a dynamic and actionable framework for securing agentic AI systems in enterprise deployment. We contend that safety and...
Trung Cuong Dang, David Mohaisen
Large language models, trained on massive corpora, are prone to verbatim memorization of training data, creating significant privacy and copyright...
Xiaoqing Wang, Keman Huang, Bin Liang +2 more
The rapid advancement of Large Language Model (LLM)-driven multi-agent systems has significantly streamlined software developing tasks, enabling...
Xiangrui Zhang, Zeyu Chen, Haining Wang +1 more
Large Language Models (LLMs) and their agent systems have recently demonstrated strong potential in automating code reasoning and vulnerability...
Qingsong He, Jing Nan, Jiayu Jiao +5 more
Large Language Models can break through knowledge and timeliness limitations by invoking external tools within the Model Context Protocol framework...
Adela Bara, Simona-Vasilica Oprea
Our paper introduces a generative, multiagent AI framework designed to overcome the rigidity, limited flexibility and technical barriers of current...
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