2,077+ academic papers on AI security, attacks, and defenses
Showing 201–220 of 259 papers
Clear filtersJiaxiang Liu, Jiawei Du, Xiao Liu +2 more
Pre-trained vision-language models (VLMs) such as CLIP have demonstrated strong zero-shot capabilities across diverse domains, yet remain highly...
Devon A. Kelly, Christiana Chamon
Wide-bandgap (WBG) technologies offer unprecedented improvements in power system efficiency, size, and performance, but also introduce unique sensor...
Sarah Ball, Niki Hasrati, Alexander Robey +4 more
Discrete optimization-based jailbreaking attacks on large language models aim to generate short, nonsensical suffixes that, when appended onto input...
Soham Hans, Stacy Marsella, Sophia Hirschmann +1 more
Understanding adversarial behavior in cybersecurity has traditionally relied on high-level intelligence reports and manual interpretation of attack...
Austin Jia, Avaneesh Ramesh, Zain Shamsi +2 more
Retrieval-Augmented Generation (RAG) has emerged as the dominant architectural pattern to operationalize Large Language Model (LLM) usage in Cyber...
Daniel Gilkarov, Ran Dubin
Pretrained deep learning model sharing holds tremendous value for researchers and enterprises alike. It allows them to apply deep learning by...
Tushar Nayan, Ziqi Zhang, Ruimin Sun
With the increasing deployment of Large Language Models (LLMs) on mobile and edge platforms, securing them against model extraction attacks has...
Petar Radanliev
Problem Space: AI Vulnerabilities and Quantum Threats Generative AI vulnerabilities: model inversion, data poisoning, adversarial inputs. Quantum...
Yushi Yang, Shreyansh Padarha, Andrew Lee +1 more
Agentic reinforcement learning (RL) trains large language models to autonomously call tools during reasoning, with search as the most common...
Elias Hossain, Swayamjit Saha, Somshubhra Roy +1 more
Even when prompts and parameters are secured, transformer language models remain vulnerable because their key-value (KV) cache during inference...
Jie Zhang, Meng Ding, Yang Liu +2 more
We present a novel approach for attacking black-box large language models (LLMs) by exploiting their ability to express confidence in natural...
Asmita Mohanty, Gezheng Kang, Lei Gao +1 more
Large Language Models (LLMs) have demonstrated strong performance across diverse tasks, but fine-tuning them typically relies on cloud-based,...
Sarah Egler, John Schulman, Nicholas Carlini
Large Language Model (LLM) providers expose fine-tuning APIs that let end users fine-tune their frontier LLMs. Unfortunately, it has been shown that...
Andrew Zhao, Reshmi Ghosh, Vitor Carvalho +4 more
Large language model (LLM) systems increasingly power everyday AI applications such as chatbots, computer-use assistants, and autonomous robots,...
Fanchao Meng, Jiaping Gui, Yunbo Li +1 more
Modern Network Intrusion Detection Systems generate vast volumes of low-level alerts, yet these outputs remain semantically fragmented, requiring...
Jianzhu Yao, Hongxu Su, Taobo Liao +4 more
Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces). Yet ML-as-a-Service reveals little...
Daniel Pulido-Cortázar, Daniel Gibert, Felip Manyà
Over the last decade, machine learning has been extensively applied to identify malicious Android applications. However, such approaches remain...
Deeksha Hareesha Kulal, Chidozie Princewill Arannonu, Afsah Anwar +2 more
Phishing remains a critical cybersecurity threat, especially with the advent of large language models (LLMs) capable of generating highly convincing...
Sean Oesch, Jack Hutchins, Luke Koch +1 more
In living off the land attacks, malicious actors use legitimate tools and processes already present on a system to avoid detection. In this paper, we...
Rui Xu, Jiawei Chen, Zhaoxia Yin +2 more
The widespread use of large language models (LLMs) and open-source code has raised ethical and security concerns regarding the distribution and...
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