2,077+ academic papers on AI security, attacks, and defenses
Showing 1901–1920 of 2,077 papers
Ardalan Aryashad, Parsa Razmara, Amin Mahjoub +3 more
Autonomous driving perception systems are particularly vulnerable in foggy conditions, where light scattering reduces contrast and obscures fine...
Hangting Ye, Jinmeng Li, He Zhao +4 more
Existing anomaly detection (AD) methods for tabular data usually rely on some assumptions about anomaly patterns, leading to inconsistent performance...
Arina Kharlamova, Bowei He, Chen Ma +1 more
Online services rely on CAPTCHAs as a first line of defense against automated abuse, yet recent advances in multi-modal large language models (MLLMs)...
Yulin Chen, Haoran Li, Yuan Sui +2 more
With the development of technology, large language models (LLMs) have dominated the downstream natural language processing (NLP) tasks. However,...
Bumjun Kim, Dongjae Jeon, Dueun Kim +2 more
Diffusion large language models (dLLMs) have emerged as a promising alternative to autoregressive models, offering flexible generation orders and...
Rabeya Amin Jhuma, Mostafa Mohaimen Akand Faisal
This study explored how in-context learning (ICL) in large language models can be disrupted by data poisoning attacks in the setting of public health...
Maraz Mia, Mir Mehedi A. Pritom
Explainable Artificial Intelligence (XAI) has aided machine learning (ML) researchers with the power of scrutinizing the decisions of the black-box...
Tanqiu Jiang, Min Bai, Nikolaos Pappas +2 more
Vision-language model (VLM)-based web agents increasingly power high-stakes selection tasks like content recommendation or product ranking by...
Raquib Bin Yousuf, Aadyant Khatri, Shengzhe Xu +2 more
Recently proposed evaluation benchmarks aim to characterize the effective context length and the forgetting tendencies of large language models...
Fatmazohra Rezkellah, Ramzi Dakhmouche
With the increasing adoption of Large Language Models (LLMs), more customization is needed to ensure privacy-preserving and safe generation. We...
Kartik Pandit, Sourav Ganguly, Arnesh Banerjee +2 more
Ensuring safety is a foundational requirement for large language models (LLMs). Achieving an appropriate balance between enhancing the utility of...
Javad Rafiei Asl, Sidhant Narula, Mohammad Ghasemigol +2 more
Large Language Models (LLMs) have revolutionized natural language processing but remain vulnerable to jailbreak attacks, especially multi-turn...
Sanket Badhe
We present LegalSim, a modular multi-agent simulation of adversarial legal proceedings that explores how AI systems can exploit procedural weaknesses...
Imene Kerboua, Sahar Omidi Shayegan, Megh Thakkar +7 more
Web agents powered by large language models (LLMs) must process lengthy web page observations to complete user goals; these pages often exceed tens...
Xinzhe Huang, Wenjing Hu, Tianhang Zheng +5 more
Existing gradient-based jailbreak attacks on Large Language Models (LLMs) typically optimize adversarial suffixes to align the LLM output with...
Yu He, Yifei Chen, Yiming Li +5 more
In recent years, RAG has emerged as a key paradigm for enhancing large language models (LLMs). By integrating externally retrieved information, RAG...
Léo Boisvert, Abhay Puri, Chandra Kiran Reddy Evuru +6 more
While finetuning AI agents on interaction data -- such as web browsing or tool use -- improves their capabilities, it also introduces critical...
Nikoo Naghavian, Mostafa Tavassolipour
Vision-language models like CLIP demonstrate impressive zero-shot generalization but remain highly vulnerable to adversarial attacks. In this work,...
Zhixin Xie, Xurui Song, Jun Luo
Despite substantial efforts in safety alignment, recent research indicates that Large Language Models (LLMs) remain highly susceptible to jailbreak...
Abrar Shahid, Ibteeker Mahir Ishum, AKM Tahmidul Haque +2 more
This paper presents a controlled study of adversarial reinforcement learning in network security through a custom OpenAI Gym environment that models...
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