2,077+ academic papers on AI security, attacks, and defenses
Showing 161–180 of 355 papers
Clear filtersYiming 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...
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...
Karolina Korgul, Yushi Yang, Arkadiusz Drohomirecki +7 more
Web-based agents powered by large language models are increasingly used for tasks such as email management or professional networking. Their reliance...
Yifan Huang, Xiaojun Jia, Wenbo Guo +4 more
Large language models (LLMs) have revolutionized software development through AI-assisted coding tools, enabling developers with limited programming...
Jiashuo Liu, Jiayun Wu, Chunjie Wu +5 more
The rapid proliferation of Large Language Models (LLMs) and diverse specialized benchmarks necessitates a shift from fragmented, task-specific...
Adam Elaoumari
The purpose of this project is to assess how well defenders can detect DNS-over-HTTPS (DoH) file exfiltration, and which evasion strategies can be...
Aaron Chan, Alex Ding, Frank Chen +3 more
The rapid integration of Large Language Models (LLMs) into decentralized physical infrastructure networks (DePIN) is currently bottlenecked by the...
Naseem Machlovi, Maryam Saleki, Ruhul Amin +5 more
As large language models (LLMs) become deeply embedded in daily life, the urgent need for safer moderation systems, distinguishing between naive from...
Naseem Machlovi, Maryam Saleki, Ruhul Amin +5 more
As large language models (LLMs) become deeply embedded in daily life, the urgent need for safer moderation systems that distinguish between naive and...
Sumanth Bharadwaj Hachalli Karanam, Dhiwahar Adhithya Kennady
Manual software beta testing is costly and time-consuming, while single-agent large language model (LLM) approaches suffer from hallucinations and...
Scott Thornton
AI coding assistants produce vulnerable code in 45\% of security-relevant scenarios~\cite{veracode2025}, yet no public training dataset teaches both...
Wei Qian, Chenxu Zhao, Yangyi Li +1 more
The rapid advancements in artificial intelligence (AI) have primarily focused on the process of learning from data to acquire knowledgeable learning...
Wang Bin, Ao Yang, Kedan Li +5 more
In the domain of software security testing, Directed Grey-Box Fuzzing (DGF) has garnered widespread attention for its efficient target localization...
Baolei Zhang, Minghong Fang, Zhuqing Liu +5 more
Federated Learning (FL) allows multiple clients to collaboratively train a model without sharing their private data. However, FL is vulnerable to...
Saksham Sahai Srivastava, Haoyu He
Large Language Model (LLM) agents increasingly rely on long-term memory and Retrieval-Augmented Generation (RAG) to persist experiences and refine...
Adam Kaufman, James Lucassen, Tyler Tracy +2 more
Future AI agents might run autonomously with elevated privileges. If these agents are misaligned, they might abuse these privileges to cause serious...
Xuanjun Zong, Zhiqi Shen, Lei Wang +2 more
Large language models (LLMs) are evolving into agentic systems that reason, plan, and operate external tools. The Model Context Protocol (MCP) is a...
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