Extracting Secrets from Encrypted Virtual Machines
January 07, 2019 Β· Declared Dead Β· π Conference on Data and Application Security and Privacy
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mathias Morbitzer, Manuel Huber, Julian Horsch
arXiv ID
1901.01759
Category
cs.CR: Cryptography & Security
Citations
44
Venue
Conference on Data and Application Security and Privacy
Last Checked
6 months ago
Abstract
AMD SEV is a hardware extension for main memory encryption on multi-tenant systems. SEV uses an on-chip coprocessor, the AMD Secure Processor, to transparently encrypt virtual machine memory with individual, ephemeral keys never leaving the coprocessor. The goal is to protect the confidentiality of the tenants' memory from a malicious or compromised hypervisor and from memory attacks, for instance via cold boot or DMA. The SEVered attack has shown that it is nevertheless possible for a hypervisor to extract memory in plaintext from SEV-encrypted virtual machines without access to their encryption keys. However, the encryption impedes traditional virtual machine introspection techniques from locating secrets in memory prior to extraction. This can require the extraction of large amounts of memory to retrieve specific secrets and thus result in a time-consuming, obvious attack. We present an approach that allows a malicious hypervisor quick identification and theft of secrets, such as TLS, SSH or FDE keys, from encrypted virtual machines on current SEV hardware. We first observe activities of a virtual machine from within the hypervisor in order to infer the memory regions most likely to contain the secrets. Then, we systematically extract those memory regions and analyze their contents on-the-fly. This allows for the efficient retrieval of targeted secrets, strongly increasing the chances of a fast, robust and stealthy theft.
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