2,077+ academic papers on AI security, attacks, and defenses
Showing 141–160 of 522 papers
Clear filtersYanshu Wang, Shuaishuai Yang, Jingjing He +1 more
Large Language Models (LLMs) face increasing threats from jailbreak attacks that bypass safety alignment. While prompt-based defenses such as...
Derin Gezgin, Amartya Das, Shinhae Kim +3 more
Recently Large Language Models (LLMs) have been used in security vulnerability detection tasks including generating proof-of-concept (PoC) exploits....
Hoang Long Do, Nasrin Sohrabi, Muneeb Ul Hassan
Large language models (LLMs) have been widely adopted in modern software development lifecycles, where they are increasingly used to automate and...
Shutong Fan, Lan Zhang, Xiaoyong Yuan
Most adversarial threats in artificial intelligence target the computational behavior of models rather than the humans who rely on them. Yet modern...
Xilong Wang, Yinuo Liu, Zhun Wang +2 more
Prompt injection attacks manipulate webpage content to cause web agents to execute attacker-specified tasks instead of the user's intended ones....
Chen Xiong, Zhiyuan He, Pin-Yu Chen +2 more
Activation steering is a practical post-training model alignment technique to enhance the utility of Large Language Models (LLMs). Prior to deploying...
Mengxuan Wang, Yuxin Chen, Gang Xu +3 more
Vision language models (VLMs) extend the reasoning capabilities of large language models (LLMs) to cross-modal settings, yet remain highly vulnerable...
Hicham Eddoubi, Umar Faruk Abdullahi, Fadi Hassan
Large Language Models (LLMs) have seen widespread adoption across multiple domains, creating an urgent need for robust safety alignment mechanisms....
Xiaozuo Shen, Yifei Cai, Rui Ning +2 more
The widespread adoption of Vision Transformers (ViTs) elevates supply-chain risk on third-party model hubs, where an adversary can implant backdoors...
Nirab Hossain, Pablo Moriano
Modern vehicles rely on electronic control units (ECUs) interconnected through the Controller Area Network (CAN), making in-vehicle communication a...
Samuel Nellessen, Tal Kachman
The evolution of large language models into autonomous agents introduces adversarial failures that exploit legitimate tool privileges, transforming...
Pengfei He, Ash Fox, Lesly Miculicich +7 more
Large language models (LLMs) have shown promise in assisting cybersecurity tasks, yet existing approaches struggle with automatic vulnerability...
Jiayao Wang, Yang Song, Zhendong Zhao +5 more
Federated self-supervised learning (FSSL) enables collaborative training of self-supervised representation models without sharing raw unlabeled data....
Mingrui Liu, Sixiao Zhang, Cheng Long +1 more
Large Language Models (LLMs) are increasingly vulnerable to Prompt Injection (PI) attacks, where adversarial instructions hidden within retrieved...
Seyed Mohammad Hadi Hosseini, Amir Najafi, Mahdieh Soleymani Baghshah
Bandit algorithms have recently emerged as a powerful tool for evaluating machine learning models, including generative image models and large...
Haobo Wang, Weiqi Luo, Xiaojun Jia +1 more
Large vision-language models (VLMs) are vulnerable to transfer-based adversarial perturbations, enabling attackers to optimize on surrogate models...
Xiaoyu Wen, Zhida He, Han Qi +7 more
Ensuring robust safety alignment is crucial for Large Language Models (LLMs), yet existing defenses often lag behind evolving adversarial attacks due...
Ziyue Wang, Jiangshan Yu, Kaihua Qin +3 more
Decentralized Finance (DeFi) has turned blockchains into financial infrastructure, allowing anyone to trade, lend, and build protocols without...
Terry Yue Zhuo, Yangruibo Ding, Wenbo Guo +1 more
For over a decade, cybersecurity has relied on human labor scarcity to limit attackers to high-value targets manually or generic automated attacks at...
Kaiyuan Cui, Yige Li, Yutao Wu +4 more
Vision-language models (VLMs) extend large language models (LLMs) with vision encoders, enabling text generation conditioned on both images and text....
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