2,104+ academic papers on AI security, attacks, and defenses
Showing 1221–1240 of 2,104 papers
Yinan Zhong, Qianhao Miao, Yanjiao Chen +3 more
Large Language Models (LLMs) have been integrated into many applications (e.g., web agents) to perform more sophisticated tasks. However,...
Xiaoqi Li, Lei Xie, Wenkai Li +1 more
In the case of upgrading smart contracts on blockchain systems, it is essential to consider the continuity of upgrades and subsequent maintenance. In...
Shiva Gaire, Srijan Gyawali, Saroj Mishra +3 more
The Model Context Protocol (MCP) has emerged as the de facto standard for connecting Large Language Models (LLMs) to external data and tools,...
Tailun Chen, Yu He, Yan Wang +9 more
Retrieval-Augmented Generation (RAG) systems enhance LLMs with external knowledge but introduce a critical attack surface: corpus poisoning. While...
Zafaryab Haider, Md Hafizur Rahman, Shane Moeykens +2 more
Hard-to-detect hardware bit flips, from either malicious circuitry or bugs, have already been shown to make transformers vulnerable in non-generative...
Md Nazmul Haque, Elizabeth Lin, Lawrence Arkoh +2 more
Large Language Models for code (LLMs4Code) are increasingly used to generate software artifacts, including library and package recommendations in...
Jinghao Wang, Ping Zhang, Carter Yagemann
Medical Large Language Models (LLMs) are increasingly deployed for clinical decision support across diverse specialties, yet systematic evaluation of...
Sampriti Soor, Suklav Ghosh, Arijit Sur
Language models are vulnerable to short adversarial suffixes that can reliably alter predictions. Previous works usually find such suffixes with...
Stephan Carney, Soham Hans, Sofia Hirschmann +4 more
Adversaries (hackers) attempting to infiltrate networks frequently face uncertainty in their operational environments. This research explores the...
Lukas Johannes Möller
The escalating sophistication and variety of cyber threats have rendered static honeypots inadequate, necessitating adaptive, intelligence-driven...
Jordan Taylor, Sid Black, Dillon Bowen +10 more
Future AI systems could conceal their capabilities ('sandbagging') during evaluations, potentially misleading developers and auditors. We...
Xiqiao Xiong, Ouxiang Li, Zhuo Liu +5 more
Large language models have seen widespread adoption, yet they remain vulnerable to multi-turn jailbreak attacks, threatening their safe deployment....
Sangha Park, Seungryong Yoo, Jisoo Mok +1 more
Although Multimodal Large Language Models (MLLMs) have advanced substantially, they remain vulnerable to object hallucination caused by language...
Alisha Ukani, Hamed Haddadi, Ali Shahin Shamsabadi +1 more
This paper presents a systematic evaluation of the privacy behaviors and attributes of eight recent, popular browser agents. Browser agents are...
JV Roig
We investigate how large language models (LLMs) fail when operating as autonomous agents with tool-use capabilities. Using the Kamiwaza Agentic Merit...
Zikai Mao, Lingchen Zhao, Lei Xu +4 more
On-device machine learning (ML) introduces new security concerns about model privacy. Storing valuable trained ML models on user devices exposes them...
Ziming Hong, Tianyu Huang, Runnan Chen +4 more
Recent studies have extended diffusion-based instruction-driven 2D image editing pipelines to 3D Gaussian Splatting (3DGS), enabling faithful...
Qiwei Tian, Chenhao Lin, Zhengyu Zhao +1 more
To address the trade-off between robustness and performance for robust VLM, we observe that function words could incur vulnerability of VLMs against...
Max Zhang, Derek Liu, Kai Zhang +2 more
Large language models (LLMs) are increasingly deployed worldwide, yet their safety alignment remains predominantly English-centric. This allows for...
Fenghua Weng, Chaochao Lu, Xia Hu +2 more
As multimodal reasoning improves the overall capabilities of Large Vision Language Models (LVLMs), recent studies have begun to explore...
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