2,077+ academic papers on AI security, attacks, and defenses
Showing 1341–1360 of 2,023 papers
Clear filtersYixin Wu, Rui Wen, Chi Cui +2 more
Inference attacks have been widely studied and offer a systematic risk assessment of ML services; however, their implementation and the attack...
Ryan Wong, Hosea David Yu Fei Ng, Dhananjai Sharma +2 more
Large Language Models (LLMs) remain susceptible to jailbreak exploits that bypass safety filters and induce harmful or unethical behavior. This work...
Juncheng Li, Yige Li, Hanxun Huang +5 more
Backdoor attacks undermine the reliability and trustworthiness of machine learning systems by injecting hidden behaviors that can be maliciously...
Onat Gungor, Roshan Sood, Jiasheng Zhou +1 more
Large Language Models (LLMs) are highly effective for cybersecurity question answering (QA) but are difficult to deploy on edge devices due to their...
Benyamin Tafreshian
Large language models (LLMs) are becoming increasingly integrated into mainstream development platforms and daily technological workflows, typically...
Adarsh Kumarappan, Ayushi Mehrotra
The SmoothLLM defense provides a certification guarantee against jailbreaking attacks, but it relies on a strict "k-unstable" assumption that rarely...
Adarsh Kumarappan, Ananya Mujoo
Multi-turn conversational attacks, which leverage psychological principles like Foot-in-the-Door (FITD), where a small initial request paves the way...
Jiangrui Zheng, Yingming Zhou, Ali Abdullah Ahmad +2 more
Bug bounty platforms (e.g., HackerOne, BugCrowd) leverage crowd-sourced vulnerability discovery to improve continuous coverage, reduce the cost of...
Yanxi Li, Ruocheng Shan
Large language models are increasingly used for text classification tasks such as sentiment analysis, yet their reliance on natural language prompts...
Yanting Wang, Runpeng Geng, Jinghui Chen +2 more
Many recent studies showed that LLMs are vulnerable to jailbreak attacks, where an attacker can perturb the input of an LLM to induce it to generate...
Hong-Hanh Nguyen-Le, Van-Tuan Tran, Dinh-Thuc Nguyen +1 more
The rapid advancement of generators (e.g., StyleGAN, Midjourney, DALL-E) has produced highly realistic synthetic images, posing significant...
Tyler Shoemaker
This position paper argues that literary scholars must engage with large language model (LLM) interpretability research. While doing so will involve...
Xiaoqing Wang, Keman Huang, Bin Liang +2 more
The rapid advancement of Large Language Model (LLM)-driven multi-agent systems has significantly streamlined software developing tasks, enabling...
Xiangrui Zhang, Zeyu Chen, Haining Wang +1 more
Large Language Models (LLMs) and their agent systems have recently demonstrated strong potential in automating code reasoning and vulnerability...
Qingsong He, Jing Nan, Jiayu Jiao +5 more
Large Language Models can break through knowledge and timeliness limitations by invoking external tools within the Model Context Protocol framework...
Swastik Bhattacharya, Sanjay Das, Anand Menon +3 more
Deep Neural Networks (DNNs) continue to grow in complexity with Large Language Models (LLMs) incorporating vast numbers of parameters. Handling these...
Mohamed Afane, Ying Wang, Juntao Chen
Public health agencies face critical challenges in identifying high-risk neighborhoods for childhood lead exposure with limited resources for...
Saeid Jamshidi, Amin Nikanjam, Negar Shahabi +4 more
As the number of connected IoT devices continues to grow, securing these systems against cyber threats remains a major challenge, especially in...
Pinaki Prasad Guha Neogi, Ahmad Mohammadshirazi, Dheeraj Kulshrestha +1 more
Mixture-of-Experts (MoE) architectures are increasingly adopted in large language models (LLMs) for their scalability and efficiency. However, their...
Junrui Zhang, Xinyu Zhao, Jie Peng +3 more
Multimodal learning has shown significant superiority on various tasks by integrating multiple modalities. However, the interdependencies among...
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