2,077+ academic papers on AI security, attacks, and defenses
Showing 61–80 of 179 papers
Clear filtersSiqi 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...
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...
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...
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...
Jiahe Guo, Xiangran Guo, Yulin Hu +8 more
Long-term memory enables large language model (LLM) agents to support personalized and sustained interactions. However, most work on personalized...
Xianya Fang, Xianying Luo, Yadong Wang +8 more
Despite the intrinsic risk-awareness of Large Language Models (LLMs), current defenses often result in shallow safety alignment, rendering models...
Saswat Das, Ferdinando Fioretto
This work addresses the computational challenge of enforcing privacy for agentic Large Language Models (LLMs), where privacy is governed by the...
Renmiao Chen, Yida Lu, Shiyao Cui +6 more
As Multimodal Large Language Models (MLLMs) acquire stronger reasoning capabilities to handle complex, multi-image instructions, this advancement may...
William Pan, Guiran Liu, Binrong Zhu +4 more
The rapid expansion of IoT deployments has intensified cybersecurity threats, notably Distributed Denial of Service (DDoS) attacks, characterized by...
Anudeex Shetty, Aditya Joshi, Salil S. Kanhere
Humans are susceptible to undesirable behaviours and privacy leaks under the influence of alcohol. This paper investigates drunk language, i.e., text...
Jiawen Zhang, Yangfan Hu, Kejia Chen +7 more
Fine-tuning is an essential and pervasive functionality for applying large language models (LLMs) to downstream tasks. However, it has the potential...
Caitlin A. Stamatis, Jonah Meyerhoff, Richard Zhang +3 more
Large language models (LLMs) are increasingly used for mental health support, yet existing safety evaluations rely primarily on small,...
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