2,583+ academic papers on AI security, attacks, and defenses
Showing 1501–1520 of 1,929 papers
Clear filtersM P V S Gopinadh, S Mahaboob Hussain
Large Language Models (LLMs) are integral to modern AI applications, but their safety alignment mechanisms can be bypassed through adversarial prompt...
Zhenhong Zhou, Shilinlu Yan, Chuanpu Liu +3 more
Large language models (LLMs) are increasingly deployed in cost-sensitive and on-device scenarios, and safety guardrails have advanced mainly in...
Yueyan Dong, Minghui Xu, Qin Hu +5 more
Low-Rank Adaptation (LoRA) has become a popular solution for fine-tuning large language models (LLMs) in federated settings, dramatically reducing...
Vidyut Sriram, Sawan Pandita, Achintya Lakshmanan +2 more
Large Language Models (LLMs) can generate code but often introduce security vulnerabilities, logical inconsistencies, and compilation errors. Prior...
Hyunjun Kim
Guardrail models are essential for ensuring the safety of Large Language Model (LLM) deployments, but processing full multi-turn conversation...
Muhammad Bilal, Omer Tariq, Hasan Ahmed
Timing and burst patterns can leak through encryption, and an adaptive adversary can exploit them. This undermines metadata-only detection in a...
Md Mahbub Hasan, Marcus Sternhagen, Krishna Chandra Roy
Additive manufacturing (AM) is rapidly integrating into critical sectors such as aerospace, automotive, and healthcare. However, this cyber-physical...
Nandish Chattopadhyay, Abdul Basit, Amira Guesmi +3 more
Adversarial attacks pose a significant challenge to the reliable deployment of machine learning models in EdgeAI applications, such as autonomous...
Sixue Xing, Xuanye Xia, Kerui Wu +3 more
Clinical trial failure remains a central bottleneck in drug development, where minor protocol design flaws can irreversibly compromise outcomes...
Weijie Wang, Peizhuo Lv, Yan Wang +7 more
Graph Retrieval-Augmented Generation (GraphRAG) has emerged as a key technique for enhancing Large Language Models (LLMs) with proprietary Knowledge...
Fumiya Morimoto, Ryuto Morita, Satoshi Ono
Deep neural network-based classifiers are prone to errors when processing adversarial examples (AEs). AEs are minimally perturbed input data...
Md Hasan Saju, Maher Muhtadi, Akramul Azim
The rapid advancement of Large Language Models (LLMs) presents new opportunities for automated software vulnerability detection, a crucial task in...
Haoran Gu, Handing Wang, Yi Mei +2 more
The widespread deployment of large language models (LLMs) has raised growing concerns about their misuse risks and associated safety issues. While...
Xiaoze Liu, Weichen Yu, Matt Fredrikson +2 more
The open-weight language model ecosystem is increasingly defined by model composition techniques (such as weight merging, speculative decoding, and...
Yuchao Hou, Zixuan Zhang, Jie Wang +9 more
As a critical application of computational intelligence in remote sensing, deep learning-based synthetic aperture radar (SAR) image target...
Yiming Liang, Yizhi Li, Yantao Du +14 more
Benchmarks play a crucial role in tracking the rapid advancement of large language models (LLMs) and identifying their capability boundaries....
Bohan Liang, Zijian Chen, Qi Jia +3 more
Stock prediction, a subject closely related to people's investment activities in fully dynamic and live environments, has been widely studied....
Manish Bhatt, Adrian Wood, Idan Habler +1 more
Production LLM agents with tool-using capabilities require security testing despite their safety training. We adapt Go-Explore to evaluate...
Muhammad Abdullahi Said, Muhammad Sammani Sani
As Large Language Models (LLMs) integrate into critical global infrastructure, the assumption that safety alignment transfers zero-shot from English...
Ruben Neyroud, Sam Corley
While most LLMs are autoregressive, diffusion-based LLMs have recently emerged as an alternative method for generation. Greedy Coordinate Gradient...
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