2,560+ academic papers on AI security, attacks, and defenses
Showing 461–480 of 726 papers
Clear filtersJiankai Jin, Xiangzheng Zhang, Zhao Liu +2 more
Machine learning systems can produce personalized outputs that allow an adversary to infer sensitive input attributes at inference time. We introduce...
David Condrey
Recent proposals advocate using keystroke timing signals, specifically the coefficient of variation ($δ$) of inter-keystroke intervals, to...
Andy Zhu, Rongzhe Wei, Yupu Gu +1 more
Machine unlearning (MU) for large language models has become critical for AI safety, yet existing methods fail to generalize to Mixture-of-Experts...
Xing Su, Hao Wu, Hanzhong Liang +4 more
Blockchain systems are increasingly targeted by on-chain attacks that exploit contract vulnerabilities to extract value rapidly and stealthily,...
Jivnesh Sandhan, Fei Cheng, Tushar Sandhan +1 more
Large Language Models (LLMs) are increasingly deployed in domains such as education, mental health and customer support, where stable and consistent...
Song Xia, Meiwen Ding, Chenqi Kong +2 more
Multimodal large language models (MLLMs) exhibit strong capabilities across diverse applications, yet remain vulnerable to adversarial perturbations...
Fengheng Chu, Jiahao Chen, Yuhong Wang +4 more
While Large Language Models (LLMs) are aligned to mitigate risks, their safety guardrails remain fragile against jailbreak attacks. This reveals...
Mingyu Yu, Lana Liu, Zhehao Zhao +2 more
The rapid advancement of Multimodal Large Language Models (MLLMs) has introduced complex security challenges, particularly at the intersection of...
Md Nabi Newaz Khan, Abdullah Arafat Miah, Yu Bi
Graph neural network (GNN) have demonstrated exceptional performance in solving critical problems across diverse domains yet remain susceptible to...
Sahar Tahmasebi, Eric Müller-Budack, Ralph Ewerth
Misinformation and fake news have become a pressing societal challenge, driving the need for reliable automated detection methods. Prior research has...
Piyumi Bhagya Sudasinghe, Kushan Sudheera Kalupahana Liyanage, Harsha S. Gardiyawasam Pussewalage
The rapid growth of Internet of Things (IoT) devices has increased the scale and diversity of cyberattacks, exposing limitations in traditional...
Zhihao Chen, Zirui Gong, Jianting Ning +2 more
Federated Rank Learning (FRL) is a promising Federated Learning (FL) paradigm designed to be resilient against model poisoning attacks due to its...
Víctor Mayoral-Vilches, Stefan Rass, Martin Pinzger +14 more
Cybersecurity superintelligence -- artificial intelligence exceeding the best human capability in both speed and strategic reasoning -- represents...
Haodong Chen, Ziheng Zhang, Jinghui Jiang +2 more
Cloud environments face frequent DDoS threats due to centralized resources and broad attack surfaces. Modern cloud-native DDoS attacks further evolve...
Andrew Crossman, Jonah Dodd, Viralam Ramamurthy Chaithanya Kumar +5 more
MITRE ATT&CK is a cybersecurity knowledge base that organizes threat actor and cyber-attack information into a set of tactics describing the reasons...
Mohammad Shamim Ahsan, Peng Liu
In the network security domain, due to practical issues -- including imbalanced data and heterogeneous legitimate network traffic -- adversarial...
Zhihao Dou, Dongfei Cui, Weida Wang +7 more
Split Learning (SL) offers a framework for collaborative model training that respects data privacy by allowing participants to share the same dataset...
Xiaochen Zhu, Mayuri Sridhar, Srinivas Devadas
Modern machine learning models are increasingly deployed behind APIs. This renders standard weight-privatization methods (e.g. DP-SGD) unnecessarily...
Yilin Tang, Yu Wang, Lanlan Qiu +4 more
Large language models (LLMs) have shown strong capabilities in multi-step decision-making, planning and actions, and are increasingly integrated into...
Rishit Chugh
The deployment of large language models (LLMs) has raised security concerns due to their susceptibility to producing harmful or policy-violating...
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