2,529+ academic papers on AI security, attacks, and defenses
Showing 661–680 of 729 papers
Clear filtersJunjie Li, Fazle Rabbi, Bo Yang +2 more
Although Large Language Models (LLMs) show promising solutions to automated code generation, they often produce insecure code that threatens software...
Riku Mochizuki, Shusuke Komatsu, Souta Noguchi +1 more
We analyze answers generated by generative engines (GEs) from the perspectives of citation publishers and the content-injection barrier, defined as...
Zhiyuan Wei, Xiaoxuan Yang, Jing Sun +1 more
The increasing complexity of modern software systems exacerbates the prevalence of security vulnerabilities, posing risks of severe breaches and...
Weidi Luo, Qiming Zhang, Tianyu Lu +9 more
Computer-use agent (CUA) frameworks, powered by large language models (LLMs) or multimodal LLMs (MLLMs), are rapidly maturing as assistants that can...
Ali Naseh, Anshuman Suri, Yuefeng Peng +3 more
Generative AI leaderboards are central to evaluating model capabilities, but remain vulnerable to manipulation. Among key adversarial objectives is...
Neeraja Kirtane, Yuvraj Khanna, Peter Relan
Large language models excel on math benchmarks, but their math reasoning robustness to linguistic variation is underexplored. While recent work...
Shadi 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...
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