WeSee: Using Malicious #VC Interrupts to Break AMD SEV-SNP
April 04, 2024 Β· Declared Dead Β· π IEEE Symposium on Security and Privacy
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Benedict SchlΓΌter, Supraja Sridhara, Andrin Bertschi, Shweta Shinde
arXiv ID
2404.03526
Category
cs.CR: Cryptography & Security
Citations
37
Venue
IEEE Symposium on Security and Privacy
Last Checked
3 months ago
Abstract
AMD SEV-SNP offers VM-level trusted execution environments (TEEs) to protect the confidentiality and integrity for sensitive cloud workloads from untrusted hypervisor controlled by the cloud provider. AMD introduced a new exception, #VC, to facilitate the communication between the VM and the untrusted hypervisor. We present WeSee attack, where the hypervisor injects malicious #VC into a victim VM's CPU to compromise the security guarantees of AMD SEV-SNP. Specifically, WeSee injects interrupt number 29, which delivers a #VC exception to the VM who then executes the corresponding handler that performs data and register copies between the VM and the hypervisor. WeSee shows that using well-crafted #VC injections, the attacker can induce arbitrary behavior in the VM. Our case-studies demonstrate that WeSee can leak sensitive VM information (kTLS keys for NGINX), corrupt kernel data (firewall rules), and inject arbitrary code (launch a root shell from the kernel space).
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