arXiv subjects

Cathy Yuanchen Li

Publications and source records attributed to Cathy Yuanchen Li.

3 recordsLinked to original sources

arXiv2024

Inception Attacks: Immersive Hijacking in Virtual Reality Systems

Today's virtual reality (VR) systems provide immersive interactions that seamlessly connect users with online services and one another. However, these immersive interfaces also introduce new vulnerabilities, making it easier for users to fall prey to new attacks. In this work, we introduce the immersive hijacking attack, where a remote attacker takes control of a user's interaction with their VR system, by trapping them inside a malicious app that masquerades as the full VR interface. Once trapped, all of the user's interactions with apps, services and other users can be recorded and modified without their knowledge. This not only allows traditional privacy attacks but also introduces new interaction attacks, where two VR users encounter vastly different immersive experiences during their interaction. We present our implementation of the immersive hijacking attack on Meta Quest headsets and conduct IRB-approved user studies that validate its efficacy and stealthiness. Finally, we examine effectiveness and tradeoffs of various potential defenses, and propose a multifaceted defense pipeline.

cs.CR↗

arXiv2024

Machine learning for modular multiplication

Motivated by cryptographic applications, we investigate two machine learning approaches to modular multiplication: namely circular regression and a sequence-to-sequence transformer model. The limited success of both methods demonstrated in our results gives evidence for the hardness of tasks involving modular multiplication upon which cryptosystems are based.

cs.LG↗

arXiv2023

SALSA VERDE: a machine learning attack on Learning With Errors with sparse small secrets

Learning with Errors (LWE) is a hard math problem used in post-quantum cryptography. Homomorphic Encryption (HE) schemes rely on the hardness of the LWE problem for their security, and two LWE-based cryptosystems were recently standardized by NIST for digital signatures and key exchange (KEM). Thus, it is critical to continue assessing the security of LWE and specific parameter choices. For example, HE uses secrets with small entries, and the HE community has considered standardizing small sparse secrets to improve efficiency and functionality. However, prior work, SALSA and PICANTE, showed that ML attacks can recover sparse binary secrets. Building on these, we propose VERDE, an improved ML attack that can recover sparse binary, ternary, and narrow Gaussian secrets. Using improved preprocessing and secret recovery techniques, VERDE can attack LWE with larger dimensions ($n=512$) and smaller moduli ($\log_2 q=12$ for $n=256$), using less time and power. We propose novel architectures for scaling. Finally, we develop a theory that explains the success of ML LWE attacks.

cs.CR↗