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CVE-2021-3656 (GCVE-0-2021-3656)

Vulnerability from cvelistv5 – Published: 2022-03-04 18:41 – Updated: 2024-08-03 17:01
VLAI
Summary
A flaw was found in the KVM's AMD code for supporting SVM nested virtualization. The flaw occurs when processing the VMCB (virtual machine control block) provided by the L1 guest to spawn/handle a nested guest (L2). Due to improper validation of the "virt_ext" field, this issue could allow a malicious L1 to disable both VMLOAD/VMSAVE intercepts and VLS (Virtual VMLOAD/VMSAVE) for the L2 guest. As a result, the L2 guest would be allowed to read/write physical pages of the host, resulting in a crash of the entire system, leak of sensitive data or potential guest-to-host escape.
CWE
Impacted products
Vendor Product Version CPE status
n/a KVM Affected: Fixed-In v5.14-rc7 and above
Show details on NVD website

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}



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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

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Detection rules are retrieved from Rulezet.

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Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.


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