2,583+ academic papers on AI security, attacks, and defenses
Showing 41–60 of 660 papers
Clear filtersHuiyu Xu, Zhibo Wang, Wenhui Zhang +4 more
Modern LLM agents solve complex tasks by operating in iterative execution loops, where they repeatedly reason, act, and self-evaluate progress to...
Md Farhamdur Reza, Richeng Jin, Tianfu Wu +1 more
Intent-obfuscation-based jailbreak attacks on multimodal large language models (MLLMs) transform a harmful query into a concealed multimodal input to...
Wesley Hanwen Deng, Mingxi Yan, Sunnie S. Y. Kim +5 more
Recent developments in AI safety research have called for red-teaming methods that effectively surface potential risks posed by generative AI models,...
Feiyue Xu, Hongsheng Hu, Chaoxiang He +9 more
Large Language Models (LLMs) have achieved remarkable success but remain highly susceptible to jailbreak attacks, in which adversarial prompts coerce...
Zhaorun Chen, Xun Liu, Haibo Tong +14 more
AI agents are increasingly deployed across diverse domains to automate complex workflows through long-horizon and high-stakes action executions. Due...
Zheng Fang, Xiaosen Wang, Shenyi Zhang +2 more
Jailbreak attacks on audio language models (ALMs) optimize audio perturbations to elicit unsafe generations, and they typically update the entire...
Zekun Fei, Zihao Wang, Weijie Liu +4 more
Mixture-of-Experts (MoE) architectures have emerged as a leading paradigm for scaling large language models through sparse, routing-based...
Raja Sekhar Rao Dheekonda, Will Pearce, Nick Landers
AI systems are entering critical domains like healthcare, finance, and defense, yet remain vulnerable to adversarial attacks. While AI red teaming is...
Shravya Kanchi, Xiaoyan Zang, Ying Zhang +2 more
Developers create modern software applications (Apps) on top of third-party libraries (Libs). When library vulnerabilities are reachable through...
Tejas Kulkarni, Antti Koskela, Laith Zumot
We show that remotely hosted applications employing in-context learning when augmented with a retrieval function to select in-context examples can be...
Haoyu Zhang, Mohammad Zandsalimy, Shanu Sushmita
Large language models (LLMs) employ safety mechanisms to prevent harmful outputs, yet these defenses primarily rely on semantic pattern matching. We...
Shihao Weng, Yang Feng, Jinrui Zhang +3 more
The rise of Large Language Model (LLM) agents, augmented with tool use, skills, and external knowledge, has introduced new security risks. Among...
Kemal Derya, Berk Sunar
Defending large language models (LLMs) against jailbreak attacks, such as Greedy Coordinate Gradient (GCG), remains a challenge, particularly under...
Ruichao Liang, Jing Chen, Xianglong Li +5 more
Smart contract vulnerabilities in Decentralized Finance caused over billions of dollars losses every year, yet the security community faces a...
Mario Rodríguez Béjar, Francisco J. Cortés-Delgado, S. Braghin +1 more
Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety alignment and elicit harmful responses. A growing body of work...
Arne Roszeitis, Bartosz Burgiel, Victor Jüttner +1 more
Smart devices, such as light bulbs, TVs, fridges, etc., equipped with computing capabilities and wireless communication, are part of everyday life in...
Adel ElZemity, Budi Arief, Shujun Li +6 more
Bare-metal operational technology (OT) devices -- especially the microcontrollers running Modbus/TCP and CoAP at the base of industrial control...
Ji Guo, Xiaolong Qin, Cencen Liu +3 more
Vision-Language Models (VLMs) have achieved remarkable success in tasks such as image captioning and visual question answering (VQA). However, as...
Mingyu Luo, Zihan Zhang, Zesen Liu +7 more
Bring-Your-Own-Key (BYOK) agent architectures let users route LLM traffic through third-party relays, creating a critical integrity gap: a malicious...
Yanting Wang, Chenlong Yin, Ying Chen +1 more
Long-context large language models (LLMs)-for example, Gemini-3.1-Pro and Qwen-3.5-are widely used to empower many real-world applications, such as...
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