2,529+ academic papers on AI security, attacks, and defenses
Showing 2501–2520 of 2,529 papers
Taeyoung Yun, Pierre-Luc St-Charles, Jinkyoo Park +2 more
We address the challenge of generating diverse attack prompts for large language models (LLMs) that elicit harmful behaviors (e.g., insults, sexual...
Bochuan Cao, Changjiang Li, Yuanpu Cao +3 more
Large language models (LLMs) have been widely adopted across various applications, leveraging customized system prompts for diverse tasks. Facing...
Jaehan Kim, Minkyoo Song, Seungwon Shin +1 more
Recent large language models (LLMs) have increasingly adopted the Mixture-of-Experts (MoE) architecture for efficiency. MoE-based LLMs heavily depend...
Jingkai Guo, Chaitali Chakrabarti, Deliang Fan
Model integrity of Large language models (LLMs) has become a pressing security concern with their massive online deployment. Prior Bit-Flip Attacks...
Daiki Chiba, Hiroki Nakano, Takashi Koide
Phishing attacks are a significant societal threat, disproportionately harming vulnerable populations and eroding trust in essential digital...
Miao Yu, Zhenhong Zhou, Moayad Aloqaily +5 more
Fine-tuned Large Language Models (LLMs) are vulnerable to backdoor attacks through data poisoning, yet the internal mechanisms governing these...
Prakhar Sharma, Haohuang Wen, Vinod Yegneswaran +3 more
The evolution toward 6G networks is being accelerated by the Open Radio Access Network (O-RAN) paradigm -- an open, interoperable architecture that...
Haibo Tong, Dongcheng Zhao, Guobin Shen +4 more
The remarkable capabilities of Large Language Models (LLMs) have raised significant safety concerns, particularly regarding "jailbreak" attacks that...
Wei Huang, De-Tian Chu, Lin-Yuan Bai +6 more
Modern email spam and phishing attacks have evolved far beyond keyword blacklists or simple heuristics. Adversaries now craft multi-modal campaigns...
Jiahao Huo, Shuliang Liu, Bin Wang +5 more
Semantic-level watermarking (SWM) for large language models (LLMs) enhances watermarking robustness against text modifications and paraphrasing...
Runqi Lin, Alasdair Paren, Suqin Yuan +4 more
The integration of new modalities enhances the capabilities of multimodal large language models (MLLMs) but also introduces additional...
Ping He, Changjiang Li, Binbin Zhao +2 more
The remarkable capability of large language models (LLMs) has led to the wide application of LLM-based agents in various domains. To standardize...
Panagiotis Michelakis, Yiannis Hadjiyiannis, Dimitrios Stamoulis
Evaluating AI agents that solve real-world tasks through function-call sequences remains an open challenge. Existing agentic benchmarks often reduce...
Hanbo Huang, Yiran Zhang, Hao Zheng +4 more
Large Language Models (LLMs) watermarking has shown promise in detecting AI-generated content and mitigating misuse, with prior work claiming...
Anh Tu Ngo, Anupam Chattopadhyay, Subhamoy Maitra
In this paper we show that cryptographic backdoors in a neural network (NN) can be highly effective in two directions, namely mounting the attacks as...
Wenkai Guo, Xuefeng Liu, Haolin Wang +3 more
Fine-tuning large language models (LLMs) with local data is a widely adopted approach for organizations seeking to adapt LLMs to their specific...
Adam Swanda, Amy Chang, Alexander Chen +3 more
The widespread adoption of Large Language Models (LLMs) has revolutionized AI deployment, enabling autonomous and semi-autonomous applications across...
Maria Chiper, Radu Tudor Ionescu
Phishing attacks targeting both organizations and individuals are becoming an increasingly significant threat as technology advances. Current...
Dana A Abdullah, Dana Rasul Hamad, Bishar Rasheed Ibrahim +3 more
Altered fingerprint recognition (AFR) is challenging for biometric verification in applications such as border control, forensics, and fiscal...
Atousa Arzanipour, Rouzbeh Behnia, Reza Ebrahimi +1 more
Retrieval-Augmented Generation (RAG) is an emerging approach in natural language processing that combines large language models (LLMs) with external...
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