2,560+ academic papers on AI security, attacks, and defenses
Showing 181–200 of 635 papers
Clear filtersTian 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...
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...
Sieun Kim, Yeeun Jo, Sungmin Na +5 more
Red-teaming, where adversarial prompts are crafted to expose harmful behaviors and assess risks, offers a dynamic approach to surfacing underlying...
Shenyang Chen, Liuwan Zhu
Standard evaluations of backdoor attacks on text-to-image (T2I) models primarily measure trigger activation and visual fidelity. We challenge this...
Zafir Shamsi, Nikhil Chekuru, Zachary Guzman +1 more
Large Language Models (LLMs) are increasingly integrated into high-stakes applications, making robust safety guarantees a central practical and...
Jingkai Guo, Chaitali Chakrabarti, Deliang Fan
Large language models (LLMs) are increasingly deployed in safety and security critical applications, raising concerns about their robustness to model...
Manuel Wirth
As Large Language Models (LLMs) are increasingly integrated into automated decision-making pipelines, specifically within Human Resources (HR), the...
Xinhao Deng, Jiaqing Wu, Miao Chen +3 more
Agent hijacking, highlighted by OWASP as a critical threat to the Large Language Model (LLM) ecosystem, enables adversaries to manipulate execution...
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