2,077+ academic papers on AI security, attacks, and defenses
Showing 881–900 of 986 papers
Clear filtersShen 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...
Muris Sladić, Veronica Valeros, Carlos Catania +1 more
There are very few SotA deception systems based on Large Language Models. The existing ones are limited only to simulating one type of service,...
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...
Tavish McDonald, Bo Lei, Stanislav Fort +2 more
Models are susceptible to adversarially out-of-distribution (OOD) data despite large training-compute investments into their robustification. Zaremba...
Tiancheng Xing, Jerry Li, Yixuan Du +1 more
Large language models (LLMs) are increasingly used as rerankers in information retrieval, yet their ranking behavior can be steered by small,...
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...
Thusitha Dayaratne, Ngoc Duy Pham, Viet Vo +5 more
The quality and experience of mobile communication have significantly improved with the introduction of 5G, and these improvements are expected to...
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...
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...
Yasod Ginige, Akila Niroshan, Sajal Jain +1 more
Penetration testing and vulnerability assessment are essential industry practices for safeguarding computer systems. As cyber threats grow in scale...
Cade Houston Kennedy, Amr Hilal, Morteza Momeni
With the growth of digital financial systems, robust security and privacy have become a concern for financial institutions. Even though traditional...
Zizhao Wang, Dingcheng Li, Vaishakh Keshava +4 more
Large Language Model (LLM) agents can leverage tools such as Google Search to complete complex tasks. However, this tool usage introduces the risk of...
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...
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...
Shuai Zhao, Xinyi Wu, Shiqian Zhao +4 more
During fine-tuning, large language models (LLMs) are increasingly vulnerable to data-poisoning backdoor attacks, which compromise their reliability...
Anindya Sundar Das, Kangjie Chen, Monowar Bhuyan
Pre-trained language models have achieved remarkable success across a wide range of natural language processing (NLP) tasks, particularly when...
Rui Wu, Yihao Quan, Zeru Shi +3 more
Safety-aligned Large Language Models (LLMs) still show two dominant failure modes: they are easily jailbroken, or they over-refuse harmless inputs...
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