2,529+ academic papers on AI security, attacks, and defenses
Showing 81–100 of 715 papers
Clear filtersMd Rysul Kabir, Zoran Tiganj
Open-weight language models can be rendered unsafe through several distinct interventions, but the resulting models may differ substantially in...
Thamilvendhan Munirathinam
Current open-source prompt-injection detectors converge on two architectural choices: regular-expression pattern matching and fine-tuned transformer...
Wentao Zhang, Yan Zhuang, ZhuHang Zheng +3 more
Existing jamming attacks on Retrieval-Augmented Generation (RAG) systems typically induce explicit refusals or denial-of-service behaviors, which are...
Jin Zhao, Marta Knežević, Tanja Käser
Large Language Models (LLMs) are increasingly used in education, yet their default helpfulness often conflicts with pedagogical principles. Prior...
Jianming Tong, Hanshen Xiao, Krishna Kumar Nair +5 more
Multi-user virtual reality enables immersive interaction. However, rendering avatars for numerous participants on each headset incurs prohibitive...
Haochun Tang, Yuliang Yan, Jiahua Lu +2 more
Cost-aware routing dynamically dispatches user queries to models of varying capability to balance performance and inference cost. However, the...
Xuanli He, Bilgehan Sel, Faizan Ali +3 more
Large Language Models (LLMs) are increasingly exposed to adaptive jailbreaking, particularly in high-stakes Chemical, Biological, Radiological, and...
Meng Chen, Kun Wang, Li Lu +2 more
Modern Large audio-language models (LALMs) power intelligent voice interactions by tightly integrating audio and text. This integration, however,...
Firas Ben Hmida, Philemon Hailemariam, Kashif Ali Khan +1 more
Deep neural networks (DNNs) remain largely opaque at inference time, limiting our ability to detect and diagnose malicious input manipulations such...
Fortunatus Aabangbio Wulnye, Justice Owusu Agyemang, Kwame Opuni-Boachie Obour Agyekum +3 more
Ensuring the reliability of machine learning-based intrusion detection systems remains a critical challenge in Internet of Things (IoT) environments,...
Pavel Chizhov, Egor Bogomolov, Ivan P. Yamshchikov
Efficiency and safety of Large Language Models (LLMs), among other factors, rely on the quality of tokenization. A good tokenizer not only improves...
Andrii Vakhnovskyi
The United States designates Food and Agriculture as one of sixteen critical infrastructure sectors, yet no mandatory cybersecurity requirements...
Yingying Zhao, Chengyin Hu, Qike Zhang +7 more
Vision-Language Models (VLMs) have shown remarkable performance, yet their security remains insufficiently understood. Existing adversarial studies...
Shaopeng Fu, Di Wang
Adversarial training (AT) is an effective defense for large language models (LLMs) against jailbreak attacks, but performing AT on LLMs is costly. To...
Anasuya Chattopadhyay, Daniel Reti, Hans D. Schotten
Cloud networks increasingly rely on machine learning based Network Intrusion Detection Systems to defend against evolving cyber threats. However,...
Jianhao Chen, Haoyang Chen, Hanjie Zhao +2 more
The rapid evolution of Vision-Language Models (VLMs) has catalyzed unprecedented capabilities in artificial intelligence; however, this continuous...
Vladimir A. Mazin, Mikhail A. Zorin, Dmitrii S. Korzh +3 more
Passwords still remain a dominant authentication method, yet their security is routinely subverted by predictable user choices and large-scale...
Junyu Ren, Xingjian Pan, Wensheng Gan +1 more
Prompt injection has emerged as a critical security threat to large language models (LLMs), yet existing studies predominantly focus on...
Ravikumar Balakrishnan, Sanket Mendapara, Ankit Garg
We study typographic prompt injection attacks on vision-language models (VLMs), where adversarial text is rendered as images to bypass safety...
Yulin Chen, Tri Cao, Haoran Li +7 more
Web agents powered by vision-language models (VLMs) enable autonomous interaction with web environments by perceiving and acting on both visual and...
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