2,583+ academic papers on AI security, attacks, and defenses
Showing 281–300 of 890 papers
Clear filtersXingyu 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...
Doron Shavit
Jailbreak prompts are a practical and evolving threat to large language models (LLMs), particularly in agentic systems that execute tools over...
Yiwen Lu
Federated Learning (FL) enables collaborative model training without exposing clients' private data, and has been widely adopted in privacy-sensitive...
Yu Yin, Shuai Wang, Bevan Koopman +1 more
Large Language Models (LLMs) have emerged as powerful re-rankers. Recent research has however showed that simple prompt injections embedded within a...
Scott Thornton
AI-assisted code review is widely used to detect vulnerabilities before production release. Prior work shows that adversarial prompt manipulation can...
Xianglin Yang, Yufei He, Shuo Ji +2 more
Self-evolving LLM agents update their internal state across sessions, often by writing and reusing long-term memory. This design improves performance...
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