2,077+ academic papers on AI security, attacks, and defenses
Showing 1941–1960 of 2,077 papers
Jaiden Fairoze, Sanjam Garg, Keewoo Lee +1 more
As large language models (LLMs) advance, ensuring AI safety and alignment is paramount. One popular approach is prompt guards, lightweight mechanisms...
Isha Gupta, Rylan Schaeffer, Joshua Kazdan +2 more
The field of adversarial robustness has long established that adversarial examples can successfully transfer between image classifiers and that text...
Luca Cotti, Idilio Drago, Anisa Rula +2 more
System logs represent a valuable source of Cyber Threat Intelligence (CTI), capturing attacker behaviors, exploited vulnerabilities, and traces of...
Shoumik Saha, Jifan Chen, Sam Mayers +3 more
Code-capable large language model (LLM) agents are increasingly embedded into software engineering workflows where they can read, write, and execute...
Yinuo Liu, Ruohan Xu, Xilong Wang +2 more
Multiple prompt injection attacks have been proposed against web agents. At the same time, various methods have been developed to detect general...
Muhammad Faheemur Rahman, Wayne Burleson
Memristive crossbar arrays enable in-memory computing by performing parallel analog computations directly within memory, making them well-suited for...
Xiangfang Li, Yu Wang, Bo Li
With the rapid advancement of large language models (LLMs), ensuring their safe use becomes increasingly critical. Fine-tuning is a widely used...
Zhengliang Shi, Ruotian Ma, Jen-tse Huang +14 more
Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare. However, the principles and values that...
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...
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