2,104+ academic papers on AI security, attacks, and defenses
Showing 1421–1440 of 2,104 papers
Zhengchunmin Dai, Jiaxiong Tang, Peng Sun +2 more
In decentralized machine learning paradigms such as Split Federated Learning (SFL) and its variant U-shaped SFL, the server's capabilities are...
Hongwei Liu, Junnan Liu, Shudong Liu +33 more
The rapid advancement of Large Language Models (LLMs) has led to performance saturation on many established benchmarks, questioning their ability to...
Eric Xue, Ruiyi Zhang, Pengtao Xie
Modern language models remain vulnerable to backdoor attacks via poisoned data, where training inputs containing a trigger are paired with a target...
Zheyu Lin, Jirui Yang, Yukui Qiu +3 more
Evaluating the safety robustness of LLMs is critical for their deployment. However, mainstream Red Teaming methods rely on online generation and...
Huiyi Chen, Jiawei Peng, Dehai Min +5 more
Evaluating the robustness of Large Vision-Language Models (LVLMs) is essential for their continued development and responsible deployment in...
Hajun Kim, Hyunsik Na, Daeseon Choi
As the use of large language models (LLMs) continues to expand, ensuring their safety and robustness has become a critical challenge. In particular,...
Ajesh Koyatan Chathoth, Stephen Lee
Sensor data-based recognition systems are widely used in various applications, such as gait-based authentication and human activity recognition...
Yule Liu, Heyi Zhang, Jinyi Zheng +6 more
Membership inference attacks (MIAs) on large language models (LLMs) pose significant privacy risks across various stages of model training. Recent...
Quoc Viet Vo, Tashreque M. Haq, Paul Montague +3 more
Certified defenses promise provable robustness guarantees. We study the malicious exploitation of probabilistic certification frameworks to better...
Badhan Chandra Das, Md Tasnim Jawad, Md Jueal Mia +2 more
Large Vision Language Models (LVLMs) demonstrate strong capabilities in multimodal reasoning and many real-world applications, such as visual...
Pascal Zimmer, Ghassan Karame
In this paper, we present the first detailed analysis of how training hyperparameters -- such as learning rate, weight decay, momentum, and batch...
Siyang Cheng, Gaotian Liu, Rui Mei +7 more
The rapid adoption of large language models (LLMs) has brought both transformative applications and new security risks, including jailbreak attacks...
Yuyang Xia, Ruixuan Liu, Li Xiong
Large language models (LLMs) perform in-context learning (ICL) by adapting to tasks from prompt demonstrations, which in practice often contain...
Fuyao Zhang, Jiaming Zhang, Che Wang +6 more
The reliance of mobile GUI agents on Multimodal Large Language Models (MLLMs) introduces a severe privacy vulnerability: screenshots containing...
Longfei Chen, Ruibin Yan, Taiyu Wong +2 more
Smart contracts are prone to vulnerabilities and are analyzed by experts as well as automated systems, such as static analysis and AI-assisted...
Aishwarya Agarwal, Srikrishna Karanam, Vineet Gandhi
Contrastive vision-language models (VLMs) such as CLIP achieve strong zero-shot recognition yet remain vulnerable to spurious correlations,...
Minjie Wang, Jinguang Han, Weizhi Meng
In federated learning, multiple parties can cooperate to train the model without directly exchanging their own private data, but the gradient leakage...
Mohammad Marufur Rahman, Guanchu Wang, Kaixiong Zhou +2 more
Catastrophic forgetting is a longstanding challenge in continual learning, where models lose knowledge from earlier tasks when learning new ones....
Ayush Chaudhary, Sisir Doppalpudi
The deployment of robust malware detection systems in big data environments requires careful consideration of both security effectiveness and...
Thomas Rivasseau
Current Large Language Model alignment research mostly focuses on improving model robustness against adversarial attacks and misbehavior by training...
Get breaking CVE alerts, compliance reports (ISO 42001, EU AI Act), and CISO risk assessments for your AI/ML stack.
Start 14-Day Free Trial
AI Threat Alert