2,077+ academic papers on AI security, attacks, and defenses
Showing 621–640 of 796 papers
Clear filtersAnum Paracha, Junaid Arshad, Mohamed Ben Farah +1 more
Data poisoning attacks are a potential threat to machine learning (ML) models, aiming to manipulate training datasets to disrupt their performance....
Pavlos Ntais
Large language models (LLMs) remain vulnerable to sophisticated prompt engineering attacks that exploit contextual framing to bypass safety...
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...
Havva Alizadeh Noughabi, Julien Serbanescu, Fattane Zarrinkalam +1 more
Despite recent advances, Large Language Models remain vulnerable to jailbreak attacks that bypass alignment safeguards and elicit harmful outputs....
Kieu Dang, Phung Lai, NhatHai Phan +3 more
Large language models (LLMs) demonstrate remarkable capabilities across various tasks. However, their deployment introduces significant risks related...
Mahavir Dabas, Tran Huynh, Nikhil Reddy Billa +8 more
Large language models remain vulnerable to jailbreak attacks that bypass safety guardrails to elicit harmful outputs. Defending against novel...
Xingwei Zhong, Kar Wai Fok, Vrizlynn L. L. Thing
Multimodal large language models (MLLMs) comprise of both visual and textual modalities to process vision language tasks. However, MLLMs are...
Mingrui Liu, Sixiao Zhang, Cheng Long +1 more
As Large Language Models (LLMs) become integral to computing infrastructure, safety alignment serves as the primary security control preventing the...
Yukun Jiang, Mingjie Li, Michael Backes +1 more
Despite their superior performance on a wide range of domains, large language models (LLMs) remain vulnerable to misuse for generating harmful...
Nguyen Linh Bao Nguyen, Alsharif Abuadbba, Kristen Moore +1 more
The rapid advancement of generative models has enabled the creation of increasingly stealthy synthetic voices, commonly referred to as audio...
Zheng-Xin Yong, Stephen H. Bach
We discover a novel and surprising phenomenon of unintentional misalignment in reasoning language models (RLMs), which we call self-jailbreaking....
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...
Antônio H. Ribeiro, David Vävinggren, Dave Zachariah +2 more
Adversarial training has emerged as a key technique to enhance model robustness against adversarial input perturbations. Many of the existing methods...
Wei Shao, Yuhao Wang, Rongguang He +2 more
Existing defence mechanisms have demonstrated significant effectiveness in mitigating rule-based Denial-of-Service (DoS) attacks, leveraging...
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...
Chiyu Chen, Xinhao Song, Yunkai Chai +7 more
Vision-Language Models (VLMs) are increasingly deployed as autonomous agents to navigate mobile graphical user interfaces (GUIs). Operating in...
Divyanshu Kumar, Shreyas Jena, Nitin Aravind Birur +3 more
Multimodal large language models (MLLMs) have achieved remarkable progress, yet remain critically vulnerable to adversarial attacks that exploit...
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...
Mohamed ElShehaby, Ashraf Matrawy
Adversarial attacks pose significant challenges to Machine Learning (ML) systems and especially Deep Neural Networks (DNNs) by subtly manipulating...
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