2,529+ academic papers on AI security, attacks, and defenses
Showing 1081–1100 of 1,207 papers
Clear filtersQizhou Peng, Yang Zheng, Yu Wen +2 more
Reinforcement learning (RL) has been an important machine learning paradigm for solving long-horizon sequential decision-making problems under...
Zaixi Zhang, Souradip Chakraborty, Amrit Singh Bedi +16 more
The rapid adoption of generative artificial intelligence (GenAI) in the biosciences is transforming biotechnology, medicine, and synthetic biology....
Tiarnaigh Downey-Webb, Olamide Jogunola, Oluwaseun Ajao
This paper presents a systematic security assessment of four prominent Large Language Models (LLMs) against diverse adversarial attack vectors. We...
Mohan Zhang, Yihua Zhang, Jinghan Jia +3 more
Modern large reasoning models (LRMs) exhibit impressive multi-step problem-solving via chain-of-thought (CoT) reasoning. However, this iterative...
Shaolun Liu, Sina Marefat, Omar Tsai +4 more
GraphQL's flexible query model and nested data dependencies expose APIs to complex, context-dependent vulnerabilities that are difficult to uncover...
Zonghao Ying, Yangguang Shao, Jianle Gan +9 more
Large vision-language model (LVLM)-based web agents are emerging as powerful tools for automating complex online tasks. However, when deployed in...
Yuyi Huang, Runzhe Zhan, Lidia S. Chao +2 more
As large language models (LLMs) are increasingly deployed for complex reasoning tasks, Long Chain-of-Thought (Long-CoT) prompting has emerged as a...
Ines Altemir Marinas, Anastasiia Kucherenko, Alexander Sternfeld +1 more
The performance of Large Language Models (LLMs) is determined by their training data. Despite the proliferation of open-weight LLMs, access to LLM...
Yongding Tao, Tian Wang, Yihong Dong +4 more
Data contamination poses a significant threat to the reliable evaluation of Large Language Models (LLMs). This issue arises when benchmark samples...
MingSheng Li, Guangze Zhao, Sichen Liu
Large Vision-Language Models (LVLMs) have achieved remarkable progress in multimodal perception and generation, yet their safety alignment remains a...
Sicheol Sung, Joonghyuk Hahn, Yo-Sub Han
Regular expressions (regexes) are foundational to modern computing for critical tasks like input validation and data parsing, yet their ubiquity...
Xiaonan Si, Meilin Zhu, Simeng Qin +7 more
Retrieval-augmented generation (RAG) systems enhance large language models (LLMs) with external knowledge but are vulnerable to corpus poisoning and...
Brandon Lit, Edward Crowder, Daniel Vogel +1 more
AI chatbots are an emerging security attack vector, vulnerable to threats such as prompt injection, and rogue chatbot creation. When deployed in...
Debeshee Das, Luca Beurer-Kellner, Marc Fischer +1 more
The increasing adoption of LLM agents with access to numerous tools and sensitive data significantly widens the attack surface for indirect prompt...
Abhishek K. Mishra, Antoine Boutet, Lucas Magnana
Large Language Models (LLMs) are increasingly deployed across multilingual applications that handle sensitive data, yet their scale and linguistic...
Aofan Liu, Lulu Tang
Vision-Language Models (VLMs) have garnered significant attention for their remarkable ability to interpret and generate multimodal content. However,...
Jiyang Qiu, Xinbei Ma, Yunqing Xu +2 more
The rapid deployment of large language model (LLM)-based agents in real-world applications has raised serious concerns about their trustworthiness....
Xiangtao Meng, Tianshuo Cong, Li Wang +4 more
Large Language Models (LLMs) have shown remarkable performance across various applications, but their deployment in real-world settings faces several...
Eric Hanchen Jiang, Weixuan Ou, Run Liu +8 more
Safety alignment of large language models currently faces a central challenge: existing alignment techniques often prioritize mitigating responses to...
Man Hu, Xinyi Wu, Zuofeng Suo +5 more
With the rise of advanced reasoning capabilities, large language models (LLMs) are receiving increasing attention. However, although reasoning...
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