2,077+ academic papers on AI security, attacks, and defenses
Showing 101–120 of 715 papers
Clear filtersYige 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...
Ian Steenstra, Paola Pedrelli, Weiyan Shi +2 more
Large Language Models (LLMs) are increasingly utilized for mental health support; however, current safety benchmarks often fail to detect the...
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...
Xingyu Shen, Tommy Duong, Xiaodong An +6 more
Age estimation systems are increasingly deployed as gatekeepers for age-restricted online content, yet their robustness to cosmetic modifications has...
Kunal Mukherjee
Trusted Execution Environments (TEEs) (e.g., Intel SGX and ArmTrustZone) aim to protect sensitive computation from a compromised operating system,...
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...
Mirae Kim, Seonghun Jeong, Youngjun Kwak
Jailbreaking poses a significant risk to the deployment of Large Language Models (LLMs) and Vision Language Models (VLMs). VLMs are particularly...
Phan The Duy, Nghi Hoang Khoa, Nguyen Tran Anh Quan +3 more
The increasing deployment of Federated Learning (FL) in Intrusion Detection Systems (IDS) introduces new challenges related to data privacy,...
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...
Priyaranjan Pattnayak, Sanchari Chowdhuri
Safety alignment of large language models (LLMs) is mostly evaluated in English and contract-bound, leaving multilingual vulnerabilities...
Thomas Michel, Debabrota Basu, Emilie Kaufmann
Modern AI models are not static. They go through multiple updates in their lifecycles. Thus, exploiting the model dynamics to create stronger...
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