2,077+ academic papers on AI security, attacks, and defenses
Showing 161–180 of 272 papers
Clear filtersZikai Mao, Lingchen Zhao, Lei Xu +4 more
On-device machine learning (ML) introduces new security concerns about model privacy. Storing valuable trained ML models on user devices exposes them...
Wenjie Zhang, Yun Lin, Chun Fung Amos Kwok +5 more
Detecting the anomalies of web applications, important infrastructures for running modern companies and governments, is crucial for providing...
Xiaoqi Li, Hailu Kuang, Wenkai Li +2 more
Traditional approaches for smart contract analysis often rely on intermediate representations such as abstract syntax trees, control-flow graphs, or...
Jehyeok Yeon, Federico Cinus, Yifan Wu +1 more
Large language models (LLMs) face critical safety challenges, as they can be manipulated to generate harmful content through adversarial prompts and...
Sheng Liu, Panos Papadimitratos
Federated Learning (FL) has drawn the attention of the Intelligent Transportation Systems (ITS) community. FL can train various models for ITS tasks,...
Jason Vega, Gagandeep Singh
A frustratingly easy technique known as the prefilling attack has been shown to effectively circumvent the safety alignment of frontier LLMs by...
Jiale Zhao, Xing Mou, Jinlin Wu +7 more
Medical Multimodal Large Language Models (Medical MLLMs) have achieved remarkable progress in specialized medical tasks; however, research into their...
Biagio Montaruli, Luca Compagna, Serena Elisa Ponta +1 more
The rise of supply chain attacks via malicious Python packages demands robust detection solutions. Current approaches, however, overlook two critical...
Xinzheng Wu, Junyi Chen, Naiting Zhong +1 more
The safe deployment of autonomous driving systems (ADSs) relies on comprehensive testing and evaluation. However, safety-critical scenarios that can...
Yixuan Tang, Yi Yang
Aligning Large Language Models (LLMs) with human preferences typically relies on external supervision, which faces critical limitations: human...
Weiwei Wang
Catastrophic forgetting remains a fundamental challenge in continual learning for large language models. Recent work revealed that performance...
Rongzhe Wei, Peizhi Niu, Xinjie Shen +7 more
Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety guardrails to elicit harmful outputs. Existing approaches...
Cen Lu, Yung-Chen Tang, Andrea Cavallaro
Large Vision-Language Models (LVLMs) have shown impressive multimodal understanding capabilities, yet their robustness is poorly understood. In this...
Henry Onyeka, Emmanuel Samson, Liang Hong +3 more
The increasing complexity of IoT edge networks presents significant challenges for anomaly detection, particularly in identifying sophisticated...
Neemesh Yadav, Francesco Ortu, Jiarui Liu +5 more
Large Language Models (LLMs) are trained to refuse to respond to harmful content. However, systematic analyses of whether this behavior is truly a...
Fouad Trad, Ali Chehab
Few-shot prompting has emerged as a practical alternative to fine-tuning for leveraging the capabilities of large language models (LLMs) in...
Yaw Osei Adjei, Frederick Ayivor, Davis Opoku
Business Email Compromise (BEC) is a sophisticated social engineering threat that manipulates organizational hierarchies, leading to significant...
Axel Constant, Mahault Albarracin, Karl J. Friston
This paper presents a computational account of how legal norms can influence the behavior of artificial intelligence (AI) agents, grounded in the...
Junbo Zhang, Ran Chen, Qianli Zhou +2 more
Large language models demonstrate powerful capabilities across various natural language processing tasks, yet they also harbor safety...
Onat Gungor, Roshan Sood, Jiasheng Zhou +1 more
Large Language Models (LLMs) are highly effective for cybersecurity question answering (QA) but are difficult to deploy on edge devices due to their...
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