2,077+ academic papers on AI security, attacks, and defenses
Showing 541–560 of 603 papers
Clear filtersShadi Rahimian, Mario Fritz
Single nucleotide polymorphism (SNP) datasets are fundamental to genetic studies but pose significant privacy risks when shared. The correlation of...
Mary Llewellyn, Annie Gray, Josh Collyer +1 more
Before adopting a new large language model (LLM) architecture, it is critical to understand vulnerabilities accurately. Existing evaluations can be...
Yongan Yu, Xianda Du, Qingchen Hu +7 more
Historical archives on weather events are collections of enduring primary source records that offer rich, untapped narratives of how societies have...
Ruoxing Yang
Large language models (LLMs) such as ChatGPT have evolved into powerful and ubiquitous tools. Fine-tuning on small datasets allows LLMs to acquire...
Rishika Bhagwatkar, Kevin Kasa, Abhay Puri +5 more
AI agents are vulnerable to indirect prompt injection attacks, where malicious instructions embedded in external content or tool outputs cause...
Rishika Bhagwatkar, Kevin Kasa, Abhay Puri +5 more
AI agents are vulnerable to indirect prompt injection attacks, where malicious instructions embedded in external content or tool outputs cause...
Punya Syon Pandey, Hai Son Le, Devansh Bhardwaj +2 more
Large language models (LLMs) are increasingly deployed in contexts where their failures can have direct sociopolitical consequences. Yet, existing...
Peichao Lai, Jinhui Zhuang, Kexuan Zhang +6 more
Automating the conversion of UI images into web code is a critical task for front-end development and rapid prototyping. Advances in multimodal large...
Jehyeok Yeon, Isha Chaudhary, Gagandeep Singh
Large language models (LLMs) are increasingly deployed in agentic systems where they map user intents to relevant external tools to fulfill a task. A...
Chengxiao Wang, Isha Chaudhary, Qian Hu +3 more
Large Language Models (LLMs) can produce catastrophic responses in conversational settings that pose serious risks to public safety and security....
Ardalan Aryashad, Parsa Razmara, Amin Mahjoub +3 more
Autonomous driving perception systems are particularly vulnerable in foggy conditions, where light scattering reduces contrast and obscures fine...
Hangting Ye, Jinmeng Li, He Zhao +4 more
Existing anomaly detection (AD) methods for tabular data usually rely on some assumptions about anomaly patterns, leading to inconsistent performance...
Arina Kharlamova, Bowei He, Chen Ma +1 more
Online services rely on CAPTCHAs as a first line of defense against automated abuse, yet recent advances in multi-modal large language models (MLLMs)...
Raquib Bin Yousuf, Aadyant Khatri, Shengzhe Xu +2 more
Recently proposed evaluation benchmarks aim to characterize the effective context length and the forgetting tendencies of large language models...
Kartik Pandit, Sourav Ganguly, Arnesh Banerjee +2 more
Ensuring safety is a foundational requirement for large language models (LLMs). Achieving an appropriate balance between enhancing the utility of...
Imene Kerboua, Sahar Omidi Shayegan, Megh Thakkar +7 more
Web agents powered by large language models (LLMs) must process lengthy web page observations to complete user goals; these pages often exceed tens...
Léo Boisvert, Abhay Puri, Chandra Kiran Reddy Evuru +6 more
While finetuning AI agents on interaction data -- such as web browsing or tool use -- improves their capabilities, it also introduces critical...
Nikoo Naghavian, Mostafa Tavassolipour
Vision-language models like CLIP demonstrate impressive zero-shot generalization but remain highly vulnerable to adversarial attacks. In this work,...
Chengquan Guo, Chulin Xie, Yu Yang +6 more
Code agents have gained widespread adoption due to their strong code generation capabilities and integration with code interpreters, enabling dynamic...
Chenpei Huang, Lingfeng Yao, Hui Zhong +5 more
Ear canal scanning/sensing (ECS) has emerged as a novel biometric authentication method for mobile devices paired with wireless earbuds. Existing...
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