2,077+ academic papers on AI security, attacks, and defenses
Showing 681–700 of 715 papers
Clear filtersSimin Chen, Yixin He, Suman Jana +1 more
LLM-based agents are increasingly deployed for software maintenance tasks such as automated program repair (APR). APR agents automatically fetch...
Yein Park, Jungwoo Park, Jaewoo Kang
Large language models (LLMs), despite being safety-aligned, exhibit brittle refusal behaviors that can be circumvented by simple linguistic changes....
Jing-Jing Li, Jianfeng He, Chao Shang +6 more
As LLMs advance into autonomous agents with tool-use capabilities, they introduce security challenges that extend beyond traditional content-based...
Yuepeng Hu, Zhengyuan Jiang, Mengyuan Li +4 more
Large language models (LLMs) are often modified after release through post-processing such as post-training or quantization, which makes it...
Yupei Liu, Yanting Wang, Yuqi Jia +2 more
Prompt injection attacks pose a pervasive threat to the security of Large Language Models (LLMs). State-of-the-art prevention-based defenses...
Zhifang Zhang, Qiqi Tao, Jiaqi Lv +3 more
Large vision-language models (LVLMs) have achieved impressive performance across a wide range of vision-language tasks, while they remain vulnerable...
Weibo Zhao, Jiahao Liu, Bonan Ruan +2 more
Model Context Protocol (MCP) servers enable AI applications to connect to external systems in a plug-and-play manner, but their rapid proliferation...
Alireza Lotfi, Charalampos Katsis, Elisa Bertino
Software vulnerabilities remain a critical security challenge, providing entry points for attackers into enterprise networks. Despite advances in...
Jianshuo Dong, Sheng Guo, Hao Wang +6 more
Search agents connect LLMs to the Internet, enabling them to access broader and more up-to-date information. However, this also introduces a new...
Yixu Wang, Yan Teng, Yingchun Wang +1 more
Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA have transformed vision model adaptation, enabling the rapid deployment of customized...
Zhaoqi Wang, Daqing He, Zijian Zhang +4 more
Large language models (LLMs) have demonstrated remarkable capabilities, yet they also introduce novel security challenges. For instance, prompt...
Francesco Marchiori, Rohan Sinha, Christopher Agia +4 more
Large Language Models (LLMs) and Vision-Language Models (VLMs) are increasingly deployed in robotic environments but remain vulnerable to...
Zi Liang, Qingqing Ye, Xuan Liu +3 more
Synthetic data refers to artificial samples generated by models. While it has been validated to significantly enhance the performance of large...
Javad Forough, Mohammad Maheri, Hamed Haddadi
Large Language Models (LLMs) are increasingly susceptible to jailbreak attacks, which are adversarial prompts that bypass alignment constraints and...
Aashnan Rahman, Abid Hasan, Sherajul Arifin +5 more
Federated learning (FL) enables privacy-preserving model training by keeping data decentralized. However, it remains vulnerable to label-flipping...
Roie Kazoom, Yuval Ratzabi, Etamar Rothstein +1 more
Adversarial robustness in structured data remains an underexplored frontier compared to vision and language domains. In this work, we introduce a...
Hwan Chang, Yonghyun Jun, Hwanhee Lee
The growing deployment of large language model (LLM) based agents that interact with external environments has created new attack surfaces for...
Wonjun Lee, Haon Park, Doehyeon Lee +2 more
Along with the rapid advancement of numerous Text-to-Video (T2V) models, growing concerns have emerged regarding their safety risks. While recent...
Petar Radanliev
This study presents a structured approach to evaluating vulnerabilities within quantum cryptographic protocols, focusing on the BB84 quantum key...
Aravindhan G, Yuvaraj Govindarajulu, Parin Shah
Recent studies have demonstrated the vulnerability of Automatic Speech Recognition systems to adversarial examples, which can deceive these systems...
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