2,583+ academic papers on AI security, attacks, and defenses
Showing 2121–2140 of 2,583 papers
Myeongseob Ko, Nikhil Reddy Billa, Adam Nguyen +3 more
The memorization of training data in large language models (LLMs) poses significant privacy and copyright concerns. Existing data extraction methods,...
Zesen Liu, Zhixiang Zhang, Yuchong Xie +1 more
LLM-powered agents often use prompt compression to reduce inference costs, but this introduces a new security risk. Compression modules, which are...
Bin Wang, YiLu Zhong, MiDi Wan +4 more
Large language models (LLMs) have become indispensable for automated code generation, yet the quality and security of their outputs remain a critical...
Armin Gerami, Kazem Faghih, Ramani Duraiswami
Retrieval Augmented Generation (RAG) enhances Large Language Models (LLMs) by connecting them to external knowledge, improving accuracy and reducing...
Jiaxiang Liu, Jiawei Du, Xiao Liu +2 more
Pre-trained vision-language models (VLMs) such as CLIP have demonstrated strong zero-shot capabilities across diverse domains, yet remain highly...
Xiaofeng Zhu, Yunshen Zhou
Microsoft Copilot suites serve as the universal entry point for various agents skilled in handling important tasks, ranging from assisting a customer...
Md. Mehedi Hasan, Ziaur Rahman, Rafid Mostafiz +1 more
This paper presents a real-time modular defense system named Sentra-Guard. The system detects and mitigates jailbreak and prompt injection attacks...
Julia Bazinska, Max Mathys, Francesco Casucci +4 more
AI agents powered by large language models (LLMs) are being deployed at scale, yet we lack a systematic understanding of how the choice of backbone...
Dongyi Liu, Jiangtong Li, Dawei Cheng +1 more
Graph Neural Networks(GNNs) are vulnerable to backdoor attacks, where adversaries implant malicious triggers to manipulate model predictions....
Hao Zheng, Zirui Pang, Ling li +5 more
Advances in Multimodal Large Language Models (MLLMs) intensify concerns about data privacy, making Machine Unlearning (MU), the selective removal of...
Devon A. Kelly, Christiana Chamon
Wide-bandgap (WBG) technologies offer unprecedented improvements in power system efficiency, size, and performance, but also introduce unique sensor...
Anum Paracha, Junaid Arshad, Mohamed Ben Farah +1 more
Data poisoning attacks are a potential threat to machine learning (ML) models, aiming to manipulate training datasets to disrupt their performance....
Wenxuan Bao, Ruxi Deng, Jingrui He
Pretrained vision-language models such as CLIP achieve strong zero-shot generalization but remain vulnerable to distribution shifts caused by input...
Pavlos Ntais
Large language models (LLMs) remain vulnerable to sophisticated prompt engineering attacks that exploit contextual framing to bypass safety...
Sarah Ball, Niki Hasrati, Alexander Robey +4 more
Discrete optimization-based jailbreaking attacks on large language models aim to generate short, nonsensical suffixes that, when appended onto input...
Havva Alizadeh Noughabi, Julien Serbanescu, Fattane Zarrinkalam +1 more
Despite recent advances, Large Language Models remain vulnerable to jailbreak attacks that bypass alignment safeguards and elicit harmful outputs....
Kieu Dang, Phung Lai, NhatHai Phan +3 more
Large language models (LLMs) demonstrate remarkable capabilities across various tasks. However, their deployment introduces significant risks related...
Mahavir Dabas, Tran Huynh, Nikhil Reddy Billa +8 more
Large language models remain vulnerable to jailbreak attacks that bypass safety guardrails to elicit harmful outputs. Defending against novel...
Adetayo Adebimpe, Helmut Neukirchen, Thomas Welsh
Honeypots are decoy systems used for gathering valuable threat intelligence or diverting attackers away from production systems. Maximising attacker...
Mojtaba Eshghie, Gabriele Morello, Matteo Lauretano +2 more
Smart contract vulnerabilities cost billions of dollars annually, yet existing automated analysis tools fail to generate deployable defenses. We...
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