2,077+ academic papers on AI security, attacks, and defenses
Showing 541–560 of 701 papers
Clear filtersMd. Mehedi Hasan, Ziaur Rahman, Rafid Mostafiz +1 more
This paper presents a real-time modular defense system named Sentra-Guard. The system detects and mitigates jailbreak and prompt injection attacks...
Dongyi Liu, Jiangtong Li, Dawei Cheng +1 more
Graph Neural Networks(GNNs) are vulnerable to backdoor attacks, where adversaries implant malicious triggers to manipulate model predictions....
Anum 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...
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....
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...
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...
Wu Yichao, Wang Yirui, Ding Panpan +3 more
With the wide application of deep reinforcement learning (DRL) techniques in complex fields such as autonomous driving, intelligent manufacturing,...
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...
Wm. Matthew Kennedy, Cigdem Patlak, Jayraj Dave +10 more
AI systems have the potential to produce both benefits and harms, but without rigorous and ongoing adversarial evaluation, AI actors will struggle to...
Euodia Dodd, Nataša Krčo, Igor Shilov +1 more
Membership inference attacks (MIAs) have emerged as the standard tool for evaluating the privacy risks of AI models. However, state-of-the-art...
Mohamed ElShehaby, Ashraf Matrawy
Adversarial attacks pose significant challenges to Machine Learning (ML) systems and especially Deep Neural Networks (DNNs) by subtly manipulating...
Ariana Yi, Ce Zhou, Liyang Xiao +1 more
As object detection models are increasingly deployed in cyber-physical systems such as autonomous vehicles (AVs) and surveillance platforms, ensuring...
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