2,560+ academic papers on AI security, attacks, and defenses
Showing 541–560 of 727 papers
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Large language models remain vulnerable to jailbreak attacks, and single-layer defenses often trade security for usability. We present TRYLOCK, the...
Ahmed Ahmed, A. Feder Cooper, Sanmi Koyejo +1 more
Many unresolved legal questions over LLMs and copyright center on memorization: whether specific training data have been encoded in the model's...
Devang Kulshreshtha, Hang Su, Chinmay Hegde +1 more
Most jailbreak methods achieve high attack success rates (ASR) but require attacker LLMs to craft adversarial queries and/or demand high query...
Neusha Javidnia, Ruisi Zhang, Ashish Kundu +1 more
We present SWaRL, a robust and fidelity-preserving watermarking framework designed to protect the intellectual property of code LLM owners by...
Alexandre Le Mercier, Chris Develder, Thomas Demeester
State space models (SSMs) like Mamba offer efficient alternatives to Transformer-based language models, with linear time complexity. Yet, their...
Alexandre Le Mercier, Chris Develder, Thomas Demeester
State space models (SSMs) like Mamba offer efficient alternatives to Transformer-based language models, with linear time complexity. Yet, their...
Jiwei Guan, Haibo Jin, Haohan Wang
Recent advancements in Large Vision-Language Models (LVLMs) have shown groundbreaking capabilities across diverse multimodal tasks. However, these...
Davis Brown, Juan-Pablo Rivera, Dan Hendrycks +1 more
As frontier AIs become more powerful and costly to develop, adversaries have increasing incentives to steal model weights by mounting exfiltration...
Jiajie Zhu, Xia Du, Xiaoyuan Liu +4 more
The rapid advancements in artificial intelligence have significantly accelerated the adoption of speech recognition technology, leading to its...
M P V S Gopinadh, S Mahaboob Hussain
Large Language Models (LLMs) are integral to modern AI applications, but their safety alignment mechanisms can be bypassed through adversarial prompt...
Zhenhong Zhou, Shilinlu Yan, Chuanpu Liu +3 more
Large language models (LLMs) are increasingly deployed in cost-sensitive and on-device scenarios, and safety guardrails have advanced mainly in...
Md Mahbub Hasan, Marcus Sternhagen, Krishna Chandra Roy
Additive manufacturing (AM) is rapidly integrating into critical sectors such as aerospace, automotive, and healthcare. However, this cyber-physical...
Nandish Chattopadhyay, Abdul Basit, Amira Guesmi +3 more
Adversarial attacks pose a significant challenge to the reliable deployment of machine learning models in EdgeAI applications, such as autonomous...
Fumiya Morimoto, Ryuto Morita, Satoshi Ono
Deep neural network-based classifiers are prone to errors when processing adversarial examples (AEs). AEs are minimally perturbed input data...
Haoran Gu, Handing Wang, Yi Mei +2 more
The widespread deployment of large language models (LLMs) has raised growing concerns about their misuse risks and associated safety issues. While...
Xiaoze Liu, Weichen Yu, Matt Fredrikson +2 more
The open-weight language model ecosystem is increasingly defined by model composition techniques (such as weight merging, speculative decoding, and...
Manish Bhatt, Adrian Wood, Idan Habler +1 more
Production LLM agents with tool-using capabilities require security testing despite their safety training. We adapt Go-Explore to evaluate...
Ruben Neyroud, Sam Corley
While most LLMs are autoregressive, diffusion-based LLMs have recently emerged as an alternative method for generation. Greedy Coordinate Gradient...
Yalin E. Sagduyu, Tugba Erpek, Aylin Yener +1 more
Semantic communications conveys task-relevant meaning rather than focusing solely on message reconstruction, improving bandwidth efficiency and...
Pankayaraj Pathmanathan, Michael-Andrei Panaitescu-Liess, Cho-Yu Jason Chiang +1 more
Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm to enhance large language models (LLMs) with external knowledge, reducing...
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