2,560+ academic papers on AI security, attacks, and defenses
Showing 181–200 of 982 papers
Clear filtersYusheng 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...
Vicenç Torra, Maria Bras-Amorós
Memory poisoning attacks for Agentic AI and multi-agent systems (MAS) have recently caught attention. It is partially due to the fact that Large...
Qi Luo, Minghui Xu, Dongxiao Yu +1 more
Many learning systems now use graph data in which each node also contains text, such as papers with abstracts or users with posts. Because these...
Dong-Xiao Zhang, Hu Lou, Jun-Jie Zhang +2 more
Adversarial vulnerability in vision and hallucination in large language models are conventionally viewed as separate problems, each addressed with...
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,...
Pranay Anchuri, Matteo Campanelli, Paul Cesaretti +4 more
When large AI models are deployed as cloud-based services, clients have no guarantee that responses are correct or were produced by the intended...
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...
Xavier Cadet, Aditya Vikram Singh, Harsh Mamania +6 more
Investigating cybersecurity incidents requires collecting and analyzing evidence from multiple log sources, including intrusion detection alerts,...
Xavier Cadet, Aditya Vikram Singh, Harsh Mamania +6 more
Investigating cybersecurity incidents requires collecting and analyzing evidence from multiple log sources, including intrusion detection alerts,...
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...
Saikat Maiti
Autonomous AI agents powered by large language models are being deployed in production with capabilities including shell execution, file system...
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...
Patrick Levi
Retrieval augmented generation systems have become an integral part of everyday life. Whether in internet search engines, email systems, or service...
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...
Kushankur Ghosh, Mehar Klair, Kian Kyars +2 more
Provenance graphs model causal system-level interactions from logs, enabling anomaly detectors to learn normal behavior and detect deviations as...
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