2,529+ academic papers on AI security, attacks, and defenses
Showing 1–20 of 108 papers
Clear filtersMengqi He, Xinyu Tian, Xin Shen +6 more
Recent studies show that gradient-based universal image jailbreaks on vision-language models (VLMs) exhibit little or no cross-model transferability,...
Zheng Lin, Zhenxing Niu, Haoxuan Ji +2 more
This paper proposes a jailbreaking prompt detection method for large language models (LLMs) to defend against jailbreak attacks. Although recent LLMs...
Desen Sun, Jason Hon, Howe Wang +3 more
With the rapid advancement of generative AI, users increasingly rely on image-generation models for image design and creation. To achieve faithful...
Zheng Lin, Zhenxing Niu, Haoxuan Ji +1 more
This paper proposes a guaranteed defense method for large language models (LLMs) to safeguard against jailbreaking attacks. Drawing inspiration from...
Peiru Yang, Haoran Zheng, Tong Ju +6 more
Retrieval-augmented generation (RAG) is a widely adopted paradigm for enhancing LLMs in medical applications by incorporating expert multimodal...
Farzad Nourmohammadzadeh Motlagh, Mehrdad Hajizadeh, Mehryar Majd +3 more
Natural language interfaces to structured databases are becoming increasingly common, largely due to advances in large language models (LLMs) that...
Yue Li, Xiao Li, Hao Wu +5 more
Large Language Models (LLMs) are increasingly used for automated software development, making their ability to preserve secure coding practices...
Huilin Zhou, Jian Zhao, Yilu Zhong +7 more
Red teaming is critical for uncovering vulnerabilities in Large Language Models (LLMs). While automated methods have improved scalability, existing...
Ben Kereopa-Yorke, Guillermo Diaz, Holly Wright +3 more
We define Oracle Poisoning, an attack class in which an adversary corrupts a structured knowledge graph that AI agents query at runtime via tool-use...
Li Lixing
Modern large language models (LLMs) rely on system prompts to establish behavioral constraints and safety rules. Standard causal self-attention...
Yiyong Liu, Chia-Yi Hsu, Chun-Ying Huang +3 more
LLM-powered coding agents increasingly make software supply chain decisions. They generate imports, recommend packages, and write installation...
Isaac David, Arthur Gervais
Security updates create a short but important window in which defenders and attackers can compare vulnerable and patched software. Yet in many...
Zeyuan Chen, Yihan Ma, Xinyue Shen +2 more
Large language models (LLMs) show strong performance across many applications, but their ability to memorize and potentially reveal training data...
Huiyu 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,...
Samuel Korn
Retrieval-Augmented Generation (RAG) systems are vulnerable to knowledge base poisoning, yet existing attacks have been evaluated almost exclusively...
Yiwei Zhang, Jeremiah Birrell, Reza Ebrahimi +3 more
Large language models (LLMs) remain vulnerable to adversarial prompting despite advances in alignment and safety, often exhibiting harmful behaviors...
Marco Rando, Samuel Vaiter
Large language models (LLMs) are known to be vulnerable to jailbreak attacks, which typically rely on carefully designed prompts containing explicit...
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...
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