2,077+ academic papers on AI security, attacks, and defenses
Showing 521–540 of 598 papers
Clear filtersJian Wang, Xiaofei Xie, Qiang Hu +4 more
Automated Program Repair (APR) plays a critical role in enhancing the quality and reliability of software systems. While substantial progress has...
Jidong Li, Lingyong Fang, Haodong Zhao +2 more
Multimodal large language models (MLLMs) have witnessed astonishing advancements in recent years. Despite these successes, MLLMs remain vulnerable to...
Norbert Tihanyi, Bilel Cherif, Richard A. Dubniczky +2 more
In this paper, we present the first large-scale study exploring whether JavaScript code generated by Large Language Models (LLMs) can reveal which...
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...
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...
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...
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...
Haoran Ou, Kangjie Chen, Xingshuo Han +4 more
Large Language Models (LLMs) have been augmented with web search to overcome the limitations of the static knowledge boundary by accessing up-to-date...
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...
Rupam Patir, Keyan Guo, Haipeng Cai +1 more
The code generation capabilities of Large Language Models (LLMs) have transformed the field of software development. However, this advancement also...
Shen Dong, Mingxuan Zhang, Pengfei He +4 more
Large Language Model (LLM)-based Multi-Agent Systems (MAS) have emerged as a powerful paradigm for tackling complex, multi-step tasks across diverse...
Junjie 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...
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