2,529+ academic papers on AI security, attacks, and defenses
Showing 101–120 of 625 papers
Clear filtersJoseph 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...
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...
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...
Yusheng Zheng, Yiwei Yang, Wei Zhang +1 more
LLM agent frameworks increasingly offer checkpoint-restore for error recovery and exploration, advising developers to make external tool calls safe...
Wenjing Hong, Zhonghua Rong, Li Wang +5 more
Large Language Models (LLMs) have been widely deployed, especially through free Web-based applications that expose them to diverse user-generated...
Toan Tran, Olivera Kotevska, Li Xiong
Membership inference attacks (MIAs), which enable adversaries to determine whether specific data points were part of a model's training dataset, have...
Aravind Krishnan, Karolina Stańczak, Dietrich Klakow
As Spoken Language Models (SLMs) integrate speech and text modalities, they inherit the safety vulnerabilities of their LLM backbone and an expanded...
Sheng Liu, Panos Papadimitratos
FL has emerged as a transformative paradigm for ITS, notably camera-based Road Condition Classification (RCC). However, by enabling collaboration,...
Mohammadhossein Homaei, Iman Khazrak, Rubén Molano +2 more
Industrial Cyber-Physical Systems (ICPS) face growing threats from cyber-attacks that exploit sensor and control vulnerabilities. Digital Twin (DT)...
Jiahao Zhang, Yilong Wang, Suhang Wang
Graph neural networks (GNNs) are widely used for learning from graph-structured data in domains such as social networks, recommender systems, and...
Kun Wang, Meng Chen, Junhao Wang +6 more
With the widespread deployment of deep-learning-based speech models in security-critical applications, backdoor attacks have emerged as a serious...
Zhihua Wei, Qiang Li, Jian Ruan +4 more
Large vision-language models (VLMs) often exhibit weakened safety alignment with the integration of the visual modality. Even when text prompts...
Hammad Atta, Ken Huang, Kyriakos Rock Lambros +11 more
Agentic LLM systems equipped with persistent memory, RAG pipelines, and external tool connectors face a class of attacks - Logic-layer Prompt Control...
Shenao Yan, Shimaa Ahmed, Shan Jin +4 more
Code generation large language models (LLMs) are increasingly integrated into modern software development workflows. Recent work has shown that these...
Yong Zou, Haoran Li, Fanxiao Li +5 more
Recent progress in image generation models (IGMs) enables high-fidelity content creation but also amplifies risks, including the reproduction of...
Guangsheng Zhang, Huan Tian, Leo Zhang +4 more
Semantic segmentation models are widely deployed in safety-critical applications such as autonomous driving, yet their vulnerability to backdoor...
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