2,560+ academic papers on AI security, attacks, and defenses
Showing 161–180 of 728 papers
Clear filtersHaochuan Kevin Wang
We present a stage-decomposed analysis of prompt injection attacks against five frontier LLM agents. Prior work measures task-level attack success...
Ruiyang Wang, Rong Pan, Zhengan Yao
Federated learning (FL) enables distributed clients to collaboratively train a global model using local private data. Nevertheless, recent studies...
Eyal Hadad, Mordechai Guri
On-device Vision-Language Models (VLMs) promise data privacy via local execution. However, we show that the architectural shift toward Dynamic...
Younes Salmi, Hanna Bogucka
Deep learning (DL) has been widely studied for assisting applications of modern wireless communications. One of the applications is automatic...
Younes Salmi, Hanna Bogucka
Deep Learning (DL) has become a key technology that assists radio frequency (RF) signal classification applications, such as modulation...
Younes Salmi, Hanna Bogucka
This paper investigates the susceptibility to model integrity attacks that overload virtual machines assigned by the k-means algorithm used for...
Hieu Xuan Le, Benjamin Goh, Quy Anh Tang
Prompt attacks, including jailbreaks and prompt injections, pose a critical security risk to Large Language Model (LLM) systems. In production,...
Haozhen Wang, Haoyue Liu, Jionghao Zhu +3 more
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of applications. However, their practical deployment is...
Ahmed Lekssays
Large Language Models (LLMs) face critical challenges when analyzing security vulnerabilities in real world codebases: token limits prevent loading...
Alexander Panfilov, Peter Romov, Igor Shilov +3 more
LLM agents like Claude Code can not only write code but also be used for autonomous AI research and engineering \citep{rank2026posttrainbench,...
Joseph G. Zalameda, Megan A. Witherow, Alexander M. Glandon +2 more
Machine learning models trained on small data sets for security applications are especially vulnerable to adversarial attacks. Person identification...
Yulin Shen, Xudong Pan, Geng Hong +1 more
Recent advances in the Model Context Protocol (MCP) have enabled large language models (LLMs) to invoke external tools with unprecedented ease. This...
Qianlong Lan, Anuj Kaul
Deploying large language models (LLMs) as autonomous browser agents exposes a significant attack surface in the form of Indirect Prompt Injection...
Xingyu Zhu, Beier Zhu, Shuo Wang +4 more
As vision-language models (VLMs) are increasingly deployed in open-world scenarios, they can be easily induced by visual jailbreak attacks to...
Huamin Chen, Xunzhuo Liu, Bowei He +5 more
Over the past year, the vLLM Semantic Router project has released a series of work spanning: (1) core routing mechanisms -- signal-driven routing,...
Kwanyoung Kim, Byeongsu Sim
Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models with human preferences, inspiring the...
Zihui Chen, Yuling Wang, Pengfei Jiao +4 more
Text-attributed graphs (TAGs) enhance graph learning by integrating rich textual semantics and topological context for each node. While boosting...
Yasamin Medghalchi, Milad Yazdani, Amirhossein Dabiriaghdam +7 more
Ultrasound is widely used in clinical practice due to its portability, cost-effectiveness, safety, and real-time imaging capabilities. However, image...
Abed K. Musaffar, Ambuj Singh, Francesco Bullo
Large language models (LLMs) are increasingly deployed in human-AI teams as support agents for complex tasks such as information retrieval,...
Matta Varun, Ajay Kumar Dhakar, Yuan Hong +1 more
Graph neural network (GNN) is a powerful tool for analyzing graph-structured data. However, their vulnerability to adversarial attacks raises serious...
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