Side-Channel Vulnerabilities
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Benchmarking Large Language Models for Cryptanalysis and Side-Channel Vulnerabilities
arXiv:2505.24621v3 Announce Type: replace Abstract: Recent advancements in large language models (LLMs) have transformed natural language understanding and generation, leading to extensive benchmarking across diverse tasks. However, cryptanalysis - a critical area for data security and its connection to LLMs' generalization abilities - remains underexplored in LLM evaluations. To address this gap, we evaluate the cryptanalytic potential of state-of-the-art LLMs on ciphertexts produced by a...
Websites Can Now Spy on You Through Your Hard Drive
Over the decades, there has been no shortage of sites using clever techniques to covertly track visitors’ browsing histories, device fingerprints, and keystrokes and mouse movements in real time. Even Meta and Yandex were recently caught joining in the privacy-invasive free-for-all. Now sites have a new way to spy on their visitors: by measuring subtle interactions with their solid-state drives.
Crypto-Funded Chinese Peptide Labs Are Booming
Meta has been quietly stashing dormant face recognition code on more than 50 million phones, WIRED reported this week, tucked inside the companion app that pairs with its Ray-Ban and Oakley smart glasses. If activated, the feature—known internally as NameTag—would let wearers identify people in front of them by matching captured faces against a biometric gallery sitting on the user’s device. It’s the same kind of technology Meta said it walked away from in 2021, after paying out billions of...
Fully Oblivious Differential Privacy for Frequency Estimation in the Augmented Shuffle Model with Trusted Processors
Announce Type: new Abstract: In the shuffle model of DP (Differential Privacy), a shuffler randomly permutes users' data to achieve high accuracy and privacy. Recent studies show that most existing shuffle protocols are vulnerable to collusion attacks by the data collector and users. They address this issue by introducing the augmented shuffle model that incorporates random sampling and dummy data addition into the shuffler.