2,529+ academic papers on AI security, attacks, and defenses
Showing 21–40 of 312 papers
Clear filtersHan Liu, Shanghao Shi, Yevgeniy Vorobeychik +2 more
Low-Rank Adaptation (LoRA), which leverages the insight that model updates typically reside in a low-dimensional space, has significantly improved...
David Fernandez, Pedram MohajerAnsari, Amir Salarpour +1 more
Vision-language models (VLMs) are increasingly used in autonomous driving because they combine visual perception with language-based reasoning,...
Mahshid Rezakhani, Nowfel Mashnoor, Kimia Azar +1 more
As large language models (LLMs) are increasingly fine-tuned for hardware tasks like RTL code generation, the scarcity of high-quality datasets often...
Wenhao Lan, Shan Li, Junbin Yang +2 more
Safety-aligned language models must refuse harmful requests without collapsing into broad over-refusal, but the training-time mechanisms behind this...
Hanna Foerster, Ilia Shumailov, Cheng Zhang +3 more
Dynamic quantization emerged as a practical approach to increase the utilization and efficiency of the machine learning serving flow. Unlike static...
Mengnan Zhao, Lihe Zhang, Tianhang Zheng +2 more
Fast Adversarial Training (FAT) has attracted significant attention due to its efficiency in enhancing neural network robustness against adversarial...
Mengnan Zhao, Lihe Zhang, Bo Wang +3 more
Fast Adversarial Training (FAT) has proven effective in enhancing model robustness by encouraging networks to learn perturbation-invariant...
Irti Haq, Belén Saldías
As state-of-the-art Large Language Models (LLMs) have become ubiquitous, ensuring equitable performance across diverse demographics is critical....
Abhijit Talluri
Adversarial robustness evaluation underpins every claim of trustworthy ML deployment, yet the field suffers from fragmented protocols and undetected...
Ruixuan Liu, David Evans, Li Xiong
Indistinguishability properties such as differential privacy bounds or low empirically measured membership inference are widely treated as proxies to...
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...
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...
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...
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...
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,...
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...
Hongru Song, Yu-An Liu, Ruqing Zhang +4 more
Retrieval-augmented generation (RAG) enhances large language model (LLM) reasoning by retrieving external documents, but also opens up new attack...
Anes Abdennebi, Nadjia Kara, Laaziz Lahlou
The applications of Generative Artificial Intelligence (GenAI) and their intersections with data-driven fields, such as healthcare, finance,...
Shuhao Zhang, Yuli Chen, Jiale Han +2 more
Watermarking provides a critical safeguard for large language model (LLM) services by facilitating the detection of LLM-generated text....
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