2,104+ academic papers on AI security, attacks, and defenses
Showing 621–640 of 2,027 papers
Clear filtersChen Xiong, Zhiyuan He, Pin-Yu Chen +2 more
Activation steering is a practical post-training model alignment technique to enhance the utility of Large Language Models (LLMs). Prior to deploying...
Omar Abdelnasser, Fatemah Alharbi, Khaled Khasawneh +2 more
Safety alignment in Language Models (LMs) is fundamental for trustworthy AI. However, while different stakeholders are trying to leverage Arabic...
Mengxuan Wang, Yuxin Chen, Gang Xu +3 more
Vision language models (VLMs) extend the reasoning capabilities of large language models (LLMs) to cross-modal settings, yet remain highly vulnerable...
Jiaqi Gao, Zijian Zhang, Yuqiang Sun +5 more
Business logic vulnerabilities have become one of the most damaging yet least understood classes of smart contract vulnerabilities. Unlike...
Hicham Eddoubi, Umar Faruk Abdullahi, Fadi Hassan
Large Language Models (LLMs) have seen widespread adoption across multiple domains, creating an urgent need for robust safety alignment mechanisms....
Matthew P. Lad, Louisa Conwill, Megan Levis Scheirer
With the rapid growth of Large Language Models (LLMs), criticism of their societal impact has also grown. Work in Responsible AI (RAI) has focused on...
Hao Li, Ruoyao Wen, Shanghao Shi +2 more
AI agents that autonomously interact with external tools and environments show great promise across real-world applications. However, the external...
Blake Bullwinkel, Giorgio Severi, Keegan Hines +3 more
Detecting whether a model has been poisoned is a longstanding problem in AI security. In this work, we present a practical scanner for identifying...
Xiaozuo Shen, Yifei Cai, Rui Ning +2 more
The widespread adoption of Vision Transformers (ViTs) elevates supply-chain risk on third-party model hubs, where an adversary can implant backdoors...
Sidahmed Benabderrahmane, Petko Valtchev, James Cheney +1 more
Detecting rare and diverse anomalies in highly imbalanced datasets-such as Advanced Persistent Threats (APTs) in cybersecurity-remains a fundamental...
Tomer Kordonsky, Maayan Yamin, Noam Benzimra +2 more
LLMs are increasingly used for code generation, but their outputs often follow recurring templates that can induce predictable vulnerabilities. We...
Nirab Hossain, Pablo Moriano
Modern vehicles rely on electronic control units (ECUs) interconnected through the Controller Area Network (CAN), making in-vehicle communication a...
Rohan Saxena
Fine-tuning language models on narrowly harmful data causes emergent misalignment (EM) -- behavioral failures extending far beyond training...
Patrick Cooper, Alireza Nadali, Ashutosh Trivedi +1 more
Large language models (LLMs) are known to exhibit brittle behavior under adversarial prompts and jailbreak attacks, even after extensive alignment...
Najmul Hasan, Prashanth BusiReddyGari
The Uniform Resource Locator (URL), introduced in a connectivity-first era to define access and locate resources, remains historically limited,...
Samuel Nellessen, Tal Kachman
The evolution of large language models into autonomous agents introduces adversarial failures that exploit legitimate tool privileges, transforming...
Rodrigo Tertulino, Ricardo Almeida, Laercio Alencar
The digitization of healthcare has generated massive volumes of Electronic Health Records (EHRs), offering unprecedented opportunities for training...
Hoang M. Ngo, Tre' R. Jeter, Incheol Shin +3 more
Quantum Machine Learning (QML) is becoming increasingly prevalent due to its potential to enhance classical machine learning (ML) tasks, such as...
Ching-Yun Ko, Pin-Yu Chen
Modern artificial intelligence (AI) models are deployed on inference engines to optimize runtime efficiency and resource allocation, particularly for...
Zeming Wei, Zhixin Zhang, Chengcan Wu +3 more
Recent advancements in LLMs have led to significant breakthroughs in various AI applications. However, their sophisticated capabilities also...
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