2,077+ academic papers on AI security, attacks, and defenses
Showing 661–680 of 986 papers
Clear filtersAtharv Singh Patlan, Peiyao Sheng, S. Ashwin Hebbar +2 more
Language agents are rapidly expanding from single-user assistants to multi-user collaborators in shared workspaces and groups. However, today's...
Tom Perel
The recent boom and rapid integration of Large Language Models (LLMs) into a wide range of applications warrants a deeper understanding of their...
Chae-Gyun Lim, Seung-Ho Han, EunYoung Byun +51 more
The rapid evolution of generative AI necessitates robust safety evaluations. However, current safety datasets are predominantly English-centric,...
Wei Zhao, Zhe Li, Yige Li +1 more
Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in cross-modal understanding, but remain vulnerable to adversarial...
Huseein Jawad, Nicolas Brunel
System prompts are critical for guiding the behavior of Large Language Models (LLMs), yet they often contain proprietary logic or sensitive...
Samih Fadli
Large language model safety is usually assessed with static benchmarks, but key failures are dynamic: value drift under distribution shift, jailbreak...
Zhaoxin Zhang, Borui Chen, Yiming Hu +3 more
Recent research on large language model (LLM) jailbreaks has primarily focused on techniques that bypass safety mechanisms to elicit overtly harmful...
Bui Duc Son, Trinh Van Chien, Dong In Kim
The integration of generative artificial intelligence (GenAI) into 6G networks promises substantial performance gains while simultaneously exposing...
Jingzhuo Zhou
The rapid proliferation of Multimodal Large Language Models (MLLMs) has introduced unprecedented security challenges, particularly in phishing...
Saeefa Rubaiyet Nowmi, Jesus Lopez, Md Mahmudul Alam Imon +2 more
Quantum Machine Learning (QML) integrates quantum computational principles into learning algorithms, offering improved representational capacity and...
W. Bradley Knox, Katie Bradford, Samanta Varela Castro +6 more
Amid the growing prevalence of human-AI interaction, large language models and other AI-based entities increasingly provide forms of companionship to...
Abolfazl Younesi, Leon Kiss, Zahra Najafabadi Samani +2 more
Federated learning (FL) enables collaborative model training while preserving data privacy. However, it remains vulnerable to malicious clients who...
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...
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...
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...
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...
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...
Ayush Chaudhary, Sisir Doppalpudi
The deployment of robust malware detection systems in big data environments requires careful consideration of both security effectiveness and...
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