2,077+ academic papers on AI security, attacks, and defenses
Showing 61–80 of 522 papers
Clear filtersOluseyi Olukola, Nick Rahimi
Machine learning based network intrusion detection systems are vulnerable to adversarial attacks that degrade classification performance under both...
Hsin Lin, Yan-Lun Chen, Ren-Hung Hwang +1 more
Backdoor attacks pose a critical threat to the security of deep neural networks, yet existing efforts on universal backdoors often rely on visually...
Yilian Liu, Xiaojun Jia, Guoshun Nan +6 more
Multimodal Large Language Models (MLLMs) have achieved remarkable performance but remain vulnerable to jailbreak attacks that can induce harmful...
Swapnil Parekh
Image captioning models are encoder-decoder architectures trained on large-scale image-text datasets, making them susceptible to adversarial attacks....
Linxi Jiang, Zhijie Liu, Haotian Luo +1 more
Browser-use agents are widely used for everyday tasks. They enable automated interaction with web pages through structured DOM based interfaces or...
Kennedy Edemacu, Mohammad Mahdi Shokri
Retrieval-augmented generation (RAG) has emerged as a powerful paradigm for enhancing multimodal large language models by grounding their responses...
Xun Huang, Simeng Qin, Xiaoshuang Jia +6 more
As Large Language Models (LLMs) are increasingly used, their security risks have drawn increasing attention. Existing research reveals that LLMs are...
Tian Zhang, Yiwei Xu, Juan Wang +8 more
Large language model (LLM) agents increasingly rely on external tools and retrieval systems to autonomously complete complex tasks. However, this...
Marcus Graves
We introduce Reverse CAPTCHA, an evaluation framework that tests whether large language models follow invisible Unicode-encoded instructions embedded...
Zhonghao Zhan, Krinos Li, Yefan Zhang +1 more
Edge deployment of LLM agents on IoT hardware introduces attack surfaces absent from cloud-hosted orchestration. We present an empirical security...
Qianlong Lan, Anuj Kaul, Shaun Jones +1 more
Agentic large language model systems increasingly automate tasks by retrieving URLs and calling external tools. We show that this workflow gives rise...
Zheng Gao, Xiaoyu Li, Zhicheng Bao +2 more
Generative images have proliferated on Web platforms in social media and online copyright distribution scenarios, and semantic watermarking has...
Piyush Jaiswal, Aaditya Pratap, Shreyansh Saraswati +2 more
Large Language Models (LLMs) are widely deployed in real-world systems. Given their broader applicability, prompt engineering has become an efficient...
Che Wang, Fuyao Zhang, Jiaming Zhang +6 more
Large Language Model (LLM) agents are susceptible to Indirect Prompt Injection (IPI) attacks, where malicious instructions in retrieved content...
Yige Liu, Yiwei Lou, Che Wang +2 more
As a distributed collaborative machine learning paradigm, vertical federated learning (VFL) allows multiple passive parties with distinct features...
David Schmotz, Luca Beurer-Kellner, Sahar Abdelnabi +1 more
LLM agents are evolving rapidly, powered by code execution, tools, and the recently introduced agent skills feature. Skills allow users to extend LLM...
Nadav Kadvil, Malak Fares, Ayellet Tal
Large Vision-Language Models (LVLMs) can be vulnerable to adversarial images that subtly bias their outputs toward plausible yet incorrect responses....
Xiaochong Jiang, Shiqi Yang, Wenting Yang +2 more
Agentic systems built on large language models (LLMs) extend beyond text generation to autonomously retrieve information and invoke tools. This...
Amirhossein Farzam, Majid Behabahani, Mani Malek +2 more
Large language models (LLMs) remain vulnerable to jailbreak prompts that are fluent and semantically coherent, and therefore difficult to detect with...
Charles Ye, Jasmine Cui, Dylan Hadfield-Menell
Language models remain vulnerable to prompt injection attacks despite extensive safety training. We trace this failure to role confusion: models...
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