2,077+ academic papers on AI security, attacks, and defenses
Showing 81–100 of 269 papers
Clear filtersZeming Wei, Zhixin Zhang, Chengcan Wu +3 more
Recent advancements in LLMs have led to significant breakthroughs in various AI applications. However, their sophisticated capabilities also...
Ali Mahdavi, Santa Aghapour, Azadeh Zamanifar +1 more
Existing Byzantine robust aggregation mechanisms typically rely on fulldimensional gradi ent comparisons or pairwise distance computations, resulting...
Siqi Wen, Shu Yang, Shaopeng Fu +3 more
Vision Language Action (VLA) models close the perception action loop by translating multimodal instructions into executable behaviors, but this very...
Siqi Wen, Shu Yang, Shaopeng Fu +3 more
Vision Language Action (VLA) models close the perception action loop by translating multimodal instructions into executable behaviors, but this very...
Cláudio Lúcio do Val Lopes, João Marcus Pitta, Fabiano Belém +2 more
The integration of Artificial Intelligence (AI) into clinical settings presents a software engineering challenge, demanding a shift from isolated...
Ranjith Krishnamurthy, Oshando Johnson, Goran Piskachev +1 more
Security vulnerabilities often arise unintentionally during development due to a lack of security expertise and code complexity. Traditional tools,...
Zeyuan He, Yupeng Chen, Lang Lin +7 more
Diffusion large language models (D-LLMs) offer an alternative to autoregressive LLMs (AR-LLMs) and have demonstrated advantages in generation...
Abhishek Mishra, Mugilan Arulvanan, Reshma Ashok +3 more
Emergent misalignment poses risks to AI safety as language models are increasingly used for autonomous tasks. In this paper, we present a population...
Saeid Jamshidi, Omar Abdul Wahab, Foutse Khomh +1 more
Federated learning (FL) has become an effective paradigm for privacy-preserving, distributed Intrusion Detection Systems (IDS) in cyber-physical and...
Edward Y. Chang, Longling Geng
Inference-time scaling can amplify reasoning pathologies: sycophancy, rung collapse, and premature certainty. We present RAudit, a diagnostic...
Yanghao Su, Wenbo Zhou, Tianwei Zhang +4 more
Emergent Misalignment refers to a failure mode in which fine-tuning large language models (LLMs) on narrowly scoped data induces broadly misaligned...
Charles Westphal, Keivan Navaie, Fernando E. Rosas
Fine-tuned LLMs can covertly encode prompt secrets into outputs via steganographic channels. Prior work demonstrated this threat but relied on...
Haoyun Yang, Ronghong Huang, Yong Fang +4 more
Transport Layer Security (TLS) is fundamental to secure online communication, yet vulnerabilities in certificate validation that enable...
Holly Trikilis, Pasindu Marasinghe, Fariza Rashid +1 more
Phishing continues to be one of the most prevalent attack vectors, making accurate classification of phishing URLs essential. Recently, large...
Pragatheeswaran Vipulanandan, Kamal Premaratne, Dilip Sarkar
Large language models (LLMs) exhibit strong generative capabilities but remain vulnerable to confabulations, fluent yet unreliable outputs that vary...
Binyan Xu, Fan Yang, Xilin Dai +2 more
Deep Neural Networks remain inherently vulnerable to backdoor attacks. Traditional test-time defenses largely operate under the paradigm of internal...
Eranga Bandara, Ross Gore, Sachin Shetty +9 more
6G networks are expected to be AI-native, intent-driven, and economically programmable, requiring fundamentally new approaches to network slice...
Xingcheng Xu, Jingjing Qu, Qiaosheng Zhang +4 more
The rapid deployment of Large Language Models and AI agents across critical societal and technical domains is hindered by persistent behavioral...
Henry Chen, Victor Aranda, Samarth Keshari +2 more
Prompt-based attack techniques are one of the primary challenges in securely deploying and protecting LLM-based AI systems. LLM inputs are an...
Zihan Wu, Jie Xu, Yun Peng +2 more
Large Language Models (LLMs) struggle to automate real-world vulnerability detection due to two key limitations: the heterogeneity of vulnerability...
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