2,077+ academic papers on AI security, attacks, and defenses
Showing 461–480 of 986 papers
Clear filtersZhuoran Tan, Run Hao, Jeremy Singer +2 more
Tool-augmented LLM agents raise new security risks: tool executions can introduce runtime-only behaviors, including prompt injection and unintended...
Jiajie Zhu, Xia Du, Xiaoyuan Liu +4 more
The rapid advancements in artificial intelligence have significantly accelerated the adoption of speech recognition technology, leading to its...
Milad Rahmati, Nima Rahmati
The proliferation of Internet of Things devices in critical infrastructure has created unprecedented cybersecurity challenges, necessitating...
Hyunjun Kim
Guardrail models are essential for ensuring the safety of Large Language Model (LLM) deployments, but processing full multi-turn conversation...
Muhammad Bilal, Omer Tariq, Hasan Ahmed
Timing and burst patterns can leak through encryption, and an adaptive adversary can exploit them. This undermines metadata-only detection in a...
Nandish Chattopadhyay, Abdul Basit, Amira Guesmi +3 more
Adversarial attacks pose a significant challenge to the reliable deployment of machine learning models in EdgeAI applications, such as autonomous...
Weijie Wang, Peizhuo Lv, Yan Wang +7 more
Graph Retrieval-Augmented Generation (GraphRAG) has emerged as a key technique for enhancing Large Language Models (LLMs) with proprietary Knowledge...
Fumiya Morimoto, Ryuto Morita, Satoshi Ono
Deep neural network-based classifiers are prone to errors when processing adversarial examples (AEs). AEs are minimally perturbed input data...
Xiaoze Liu, Weichen Yu, Matt Fredrikson +2 more
The open-weight language model ecosystem is increasingly defined by model composition techniques (such as weight merging, speculative decoding, and...
Yuchao Hou, Zixuan Zhang, Jie Wang +9 more
As a critical application of computational intelligence in remote sensing, deep learning-based synthetic aperture radar (SAR) image target...
Yiming Liang, Yizhi Li, Yantao Du +14 more
Benchmarks play a crucial role in tracking the rapid advancement of large language models (LLMs) and identifying their capability boundaries....
Bohan Liang, Zijian Chen, Qi Jia +3 more
Stock prediction, a subject closely related to people's investment activities in fully dynamic and live environments, has been widely studied....
Muhammad Abdullahi Said, Muhammad Sammani Sani
As Large Language Models (LLMs) integrate into critical global infrastructure, the assumption that safety alignment transfers zero-shot from English...
Zhe Huang, Hao Wen, Aiming Hao +6 more
Multimodal Large Language Models (MLLMs) have made remarkable progress in video understanding. However, they suffer from a critical vulnerability: an...
Pankayaraj Pathmanathan, Michael-Andrei Panaitescu-Liess, Cho-Yu Jason Chiang +1 more
Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm to enhance large language models (LLMs) with external knowledge, reducing...
Giuseppe Canale, Kashyap Thimmaraju
Large Language Models (LLMs) are rapidly transitioning from conversational assistants to autonomous agents embedded in critical organizational...
Ruixuan Huang, Qingyue Wang, Hantao Huang +4 more
Mixture-of-Experts architectures have become the standard for scaling large language models due to their superior parameter efficiency. To...
Samaresh Kumar Singh, Joyjit Roy, Martin So
Recent attacks on critical infrastructure, including the 2021 Oldsmar water treatment breach and 2023 Danish energy sector compromises, highlight...
Heba Osama, Omar Elebiary, Youssef Qassim +4 more
Web applications increasingly face evasive and polymorphic attack payloads, yet traditional web application firewalls (WAFs) based on static rule...
Armstrong Foundjem, Lionel Nganyewou Tidjon, Leuson Da Silva +1 more
Machine learning (ML) underpins foundation models in finance, healthcare, and critical infrastructure, making them targets for data poisoning, model...
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