Write+Sync: Software Cache Write Covert Channels Exploiting Memory-disk Synchronization
December 08, 2023 Β· Declared Dead Β· π IEEE Transactions on Information Forensics and Security
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Congcong Chen, Jinhua Cui, Gang Qu, Jiliang Zhang
arXiv ID
2312.11501
Category
cs.CR: Cryptography & Security
Citations
43
Venue
IEEE Transactions on Information Forensics and Security
Last Checked
6 months ago
Abstract
Memory-disk synchronization is a critical technology for ensuring data correctness, integrity, and security, especially in systems that handle sensitive information like financial transactions and medical records. We propose SYNC+SYNC, a group of attacks that exploit the memory-disk synchronization primitives. SYNC+SYNC works by subtly varying the timing of synchronization on the write buffer, offering several advantages: 1) implemented purely in software, enabling deployment on any hardware devices; 2) resilient against existing cache partitioning and randomization techniques; 3) unaffected by prefetching techniques and cache replacement strategies. We present the principles of SYNC+SYNC through the implementation of two write covert channel protocols, using either a single file or page, and introduce three enhanced strategies that utilize multiple files and pages. The feasibility of these channels is demonstrated in both cross-process and cross-sandbox scenarios across diverse operating systems (OSes). Experimental results show that, the average rate can reach 2.036 Kb/s (with a peak rate of 14.762 Kb/s) and the error rate is 0% on Linux; when running on macOS, the average rate achieves 10.211 Kb/s (with a peak rate of 253.022 Kb/s) and the error rate is 0.004%. To the best of our knowledge, SYNC+SYNC is the first high-speed write covert channel for software cache.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Cryptography & Security
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
π»
Ghosted
How To Backdoor Federated Learning
R.I.P.
π»
Ghosted
Evasion Attacks against Machine Learning at Test Time
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted