2,077+ academic papers on AI security, attacks, and defenses
Showing 1–20 of 42 papers
Clear filtersRui Yang Tan, Yujia Hu, Roy Ka-Wei Lee
Multimodal Large Language Models (MLLMs) extend text-only LLMs with visual reasoning, but also introduce new safety failure modes under visually...
Xinyue Liu, Niloofar Mireshghallah, Jane C. Ginsburg +1 more
Frontier LLM companies have repeatedly assured courts and regulators that their models do not store copies of training data. They further rely on...
Anders Giovanni Møller, Elisa Bassignana, Francesco Pierri +1 more
The ubiquity of multimedia content is reshaping online information spaces, particularly in social media environments. At the same time, search is...
Shawn Li, Yue Zhao
Large language model (LLM) agents increasingly rely on external tools (file operations, API calls, database transactions) to autonomously complete...
Rohan Siva, Kai Cheung, Lichi Li +1 more
Modern machine learning systems rely on complex data engineering workflows to extract, transform, and load (ELT) data into production pipelines....
Carlos Hinojosa, Clemens Grange, Bernard Ghanem
Vision-language models (VLMs) are increasingly deployed in real-world and embodied settings where safety decisions depend on visual context. However,...
Roberto Morabito, Mallik Tatipamula
The Internet has evolved by progressively expanding what humanity connects: first computers, then people, and later billions of devices through the...
Ce Zhang, Jinxi He, Junyi He +2 more
Multi-modal Large Language Models (MLLMs) have achieved remarkable performance across a wide range of visual reasoning tasks, yet their vulnerability...
Zhenheng Tang, Xiang Liu, Qian Wang +3 more
As Large Language Models (LLMs) become more powerful and autonomous, they increasingly face conflicts and dilemmas in many scenarios. We first...
Vanshaj Khattar, Md Rafi ur Rashid, Moumita Choudhury +4 more
Test-time training (TTT) has recently emerged as a promising method to improve the reasoning abilities of large language models (LLMs), in which the...
Yewon Han, Yumin Seol, EunGyung Kong +2 more
Existing jailbreak defence frameworks for Large Vision-Language Models often suffer from a safety utility tradeoff, where strengthening safety...
Max Hellrigel-Holderbaum, Edward James Young
As AI systems advance in capabilities, measuring their safety and alignment to human values is becoming paramount. A fast-growing field of AI...
Suvadeep Hajra, Palash Nandi, Tanmoy Chakraborty
Safety tuning through supervised fine-tuning and reinforcement learning from human feedback has substantially improved the robustness of large...
Pengcheng Li, Jie Zhang, Tianwei Zhang +5 more
Safety alignment in large language models is typically evaluated under isolated queries, yet real-world use is inherently multi-turn. Although...
Matthew Butler, Yi Fan, Christos Faloutsos
The proposed method (FraudFox) provides solutions to adversarial attacks in a resource constrained environment. We focus on questions like the...
Zonghao Ying, Xiao Yang, Siyang Wu +7 more
The rapid evolution of Large Language Models (LLMs) into autonomous, tool-calling agents has fundamentally altered the cybersecurity landscape....
Xinhao Deng, Yixiang Zhang, Jiaqing Wu +15 more
Autonomous Large Language Model (LLM) agents, exemplified by OpenClaw, demonstrate remarkable capabilities in executing complex, long-horizon tasks....
Lu Niu, Cheng Xue
Vision-language models offer strong few-shot capability through prompt tuning but remain vulnerable to noisy labels, which can corrupt prompts and...
Zhiyu Xue, Zimo Qi, Guangliang Liu +2 more
Safety alignment aims to ensure that large language models (LLMs) refuse harmful requests by post-training on harmful queries paired with refusal...
Ali Eslami, Jiangbo Yu
This paper develops a control-theoretic framework for analyzing agentic systems embedded within feedback control loops, where an AI agent may adapt...
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