2,529+ academic papers on AI security, attacks, and defenses
Showing 2401–2420 of 2,529 papers
Alexandrine Fortier, Thomas Thebaud, Jesús Villalba +2 more
Large Language Models (LLMs) and their multimodal extensions are becoming increasingly popular. One common approach to enable multimodality is to...
Guobin Shen, Dongcheng Zhao, Haibo Tong +3 more
Ensuring Large Language Model (LLM) safety remains challenging due to the absence of universal standards and reliable content validators, making it...
Yicheng Lang, Yihua Zhang, Chongyu Fan +3 more
Large language model (LLM) unlearning aims to surgically remove the influence of undesired data or knowledge from an existing model while preserving...
Chen-An Li, Tzu-Han Lin, Hung-yi Lee
Large audio-language models (LALMs) unify speech and text processing, but their robustness in noisy real-world settings remains underexplored. We...
Yen-Shan Chen, Sian-Yao Huang, Cheng-Lin Yang +1 more
Existing data poisoning attacks on retrieval-augmented generation (RAG) systems scale poorly because they require costly optimization of poisoned...
Shojiro Yamabe, Jun Sakuma
Diffusion language models (DLMs) generate tokens in parallel through iterative denoising, which can reduce latency and enable bidirectional...
Andrew Gan, Zahra Ghodsi
Machine learning systems increasingly rely on open-source artifacts such as datasets and models that are created or hosted by other parties. The...
Hongbo Liu, Jiannong Cao, Bo Yang +7 more
The rapid advancement of large language models (LLMs) in recent years has revolutionized the AI landscape. However, the deployment model and usage of...
Raik Dankworth, Gesina Schwalbe
Deep neural networks (NNs) for computer vision are vulnerable to adversarial attacks, i.e., miniscule malicious changes to inputs may induce...
Tsubasa Takahashi, Shojiro Yamabe, Futa Waseda +1 more
Differential Attention (DA) has been proposed as a refinement to standard attention, suppressing redundant or noisy context through a subtractive...
Yu Yan, Siqi Lu, Yang Gao +4 more
Recently, Bit-Flip Attack (BFA) has garnered widespread attention for its ability to compromise software system integrity remotely through hardware...
Chenxiang Luo, David K. Y. Yau, Qun Song
Federated learning (FL) enables collaborative model training without sharing raw data but is vulnerable to gradient inversion attacks (GIAs), where...
Dalal Alharthi, Ivan Roberto Kawaminami Garcia
Large Language Models (LLMs) have gained prominence in domains including cloud security and forensics. Yet cloud forensic investigations still rely...
Dalal Alharthi, Ivan Roberto Kawaminami Garcia
Large language models have gained widespread prominence, yet their vulnerability to prompt injection and other adversarial attacks remains a critical...
Haoran Xi, Minghao Shao, Brendan Dolan-Gavitt +2 more
Large language models show promise for vulnerability discovery, yet prevailing methods inspect code in isolation, struggle with long contexts, and...
Samar Fares, Nurbek Tastan, Noor Hussein +1 more
Generative models can generate photorealistic images at scale. This raises urgent concerns about the ability to detect synthetically generated images...
Ehsan Aghaei, Sarthak Jain, Prashanth Arun +1 more
Effective analysis of cybersecurity and threat intelligence data demands language models that can interpret specialized terminology, complex document...
Luis Burbano, Diego Ortiz, Qi Sun +5 more
Embodied Artificial Intelligence (AI) promises to handle edge cases in robotic vehicle systems where data is scarce by using common-sense reasoning...
João Vitorino, Eva Maia, Isabel Praça +1 more
Due to the susceptibility of Artificial Intelligence (AI) to data perturbations and adversarial examples, it is crucial to perform a thorough...
Anshul Nasery, Edoardo Contente, Alkin Kaz +2 more
Model fingerprinting has emerged as a promising paradigm for claiming model ownership. However, robustness evaluations of these schemes have mostly...
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