2,077+ academic papers on AI security, attacks, and defenses
Showing 1961–1980 of 2,077 papers
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...
Matheus Vinicius da Silva de Oliveira, Jonathan de Andrade Silva, Awdren de Lima Fontao
Large Language Models (LLMs) are widely used across multiple domains but continue to raise concerns regarding security and fairness. Beyond known...
Zheng Zhang, Ziwei Shan, Kaitao Song +2 more
Process Reward Models (PRMs) have emerged as a promising approach to enhance the reasoning capabilities of large language models (LLMs) by guiding...
Firas Ben Hmida, Abderrahmen Amich, Ata Kaboudi +1 more
Deep neural networks (DNNs) are increasingly being deployed in high-stakes applications, from self-driving cars to biometric authentication. However,...
Seiji Maekawa, Jackson Hassell, Pouya Pezeshkpour +2 more
Existing benchmarks for tool-augmented language models (TaLMs) lack fine-grained control over task difficulty and remain vulnerable to data...
Yao Tong, Haonan Wang, Siquan Li +2 more
Fingerprinting Large Language Models (LLMs) is essential for provenance verification and model attribution. Existing methods typically extract...
Shuai Shao, Qihan Ren, Chen Qian +8 more
Advances in Large Language Models (LLMs) have enabled a new class of self-evolving agents that autonomously improve through interaction with the...
Qinjian Zhao, Jiaqi Wang, Zhiqiang Gao +3 more
Large Language Models (LLMs) have achieved impressive performance across diverse natural language processing tasks, but their growing power also...
Joel Dyer, Daniel Jarne Ornia, Nicholas Bishop +2 more
Evaluating the safety of frontier AI systems is an increasingly important concern, helping to measure the capabilities of such models and identify...
Yixu Wang, Xin Wang, Yang Yao +4 more
The rapid integration of Large Language Models (LLMs) into high-stakes domains necessitates reliable safety and compliance evaluation. However,...
Xiaobao Wang, Ruoxiao Sun, Yujun Zhang +4 more
Graph Neural Networks (GNNs) have demonstrated strong performance across tasks such as node classification, link prediction, and graph...
Marco Zimmerli, Andreas Plesner, Till Aczel +1 more
Deep neural networks remain vulnerable to adversarial examples despite advances in architectures and training paradigms. We investigate how training...
Dennis Jacob, Emad Alghamdi, Zhanhao Hu +2 more
Large language models (LLMs) have become increasingly popular due to their ability to interact with unstructured content. As such, LLMs are now a key...
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