2,077+ academic papers on AI security, attacks, and defenses
Showing 161–180 of 809 papers
Clear filtersAkshat Naik, Jay Culligan, Yarin Gal +4 more
As Large Language Model (LLM) agents become more capable, their coordinated use in the form of multi-agent systems is anticipated to emerge as a...
Yiran Gao, Kim Hammar, Tao Li
Rapidly evolving cyberattacks demand incident response systems that can autonomously learn and adapt to changing threats. Prior work has extensively...
Alfous Tim, Kuniyilh Simi D
The Internet of Things (IoT) systems increasingly depend on continual learning to adapt to non-stationary environments. These environments can...
Osama Zafar, Shaojie Zhan, Tianxi Ji +1 more
In recent years, the widespread adoption of Machine Learning as a Service (MLaaS), particularly in sensitive environments, has raised considerable...
Oguzhan Baser, Elahe Sadeghi, Eric Wang +5 more
Most large language models (LLMs) run on external clouds: users send a prompt, pay for inference, and must trust that the remote GPU executes the LLM...
Yannick Assogba, Jacopo Cortellazzi, Javier Abad +3 more
Jailbreak attacks remain a persistent threat to large language model safety. We propose Context-Conditioned Delta Steering (CC-Delta), an SAE-based...
Dong Yan, Jian Liang, Ran He +1 more
Recent studies have shown that large language models (LLMs) can infer private user attributes (e.g., age, location, gender) from user-generated text...
Sri Durga Sai Sowmya Kadali, Evangelos E. Papalexakis
Jailbreaking large language models (LLMs) has emerged as a critical security challenge with the widespread deployment of conversational AI systems....
Abhishek Saini, Haolin Jiang, Hang Liu
The deployment of large language models (LLMs) on third-party devices requires new ways to protect model intellectual property. While Trusted...
J Alex Corll
Multi-turn prompt injection attacks distribute malicious intent across multiple conversation turns, exploiting the assumption that each turn is...
Shuyu Chang, Haiping Huang, Yanjun Zhang +3 more
Code models are increasingly adopted in software development but remain vulnerable to backdoor attacks via poisoned training data. Existing backdoor...
Qianli Wang, Boyang Ma, Minghui Xu +1 more
LLM agents often rely on Skills to describe available tools and recommended procedures. We study a hidden-comment prompt injection risk in this...
Tri Nguyen, Huy Hoang Bao Le, Lohith Srikanth Pentapalli +2 more
Detecting jailbreak attempts in clinical training large language models (LLMs) requires accurate modeling of linguistic deviations that signal unsafe...
Zhenyu Xu, Victor S. Sheng
Protecting the intellectual property of large language models (LLMs) is a critical challenge due to the proliferation of unauthorized derivative...
Georgios Syros, Evan Rose, Brian Grinstead +4 more
Large language model (LLM) based web agents are increasingly deployed to automate complex online tasks by directly interacting with web sites and...
Kotekar Annapoorna Prabhu, Andrew Gan, Zahra Ghodsi
Machine learning relies on randomness as a fundamental component in various steps such as data sampling, data augmentation, weight initialization,...
Yu Yan, Sheng Sun, Shengjia Cheng +3 more
Vision-Language Models (VLMs) with multimodal reasoning capabilities are high-value attack targets, given their potential for handling complex...
Suraj Ranganath, Atharv Ramesh
AI-text detectors face a critical robustness challenge: adversarial paraphrasing attacks that preserve semantics while evading detection. We...
Suraj Ranganath, Atharv Ramesh
AI-text detectors face a critical robustness challenge: adversarial paraphrasing attacks that preserve semantics while evading detection. We...
Jona te Lintelo, Lichao Wu, Stjepan Picek
The rapid adoption of Mixture-of-Experts (MoE) architectures marks a major shift in the deployment of Large Language Models (LLMs). MoE LLMs improve...
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