Cyber Resilience

CVE-2021-1097

High

Published: 21 July 2021

Published
21 July 2021
Modified
21 November 2024
KEV Added
Patch
CVSS Score v3.1 7.8 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
EPSS Score 0.0013 31.9th percentile
Risk Priority 16 60% EPSS · 20% KEV · 20% CVSS

Summary

CVE-2021-1097 is a high-severity Improper Input Validation (CWE-20) vulnerability in Nvidia Virtual Gpu. Its CVSS base score is 7.8 (High).

Operationally, ranked at the 31.9th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.

EU & UK References

Vulnerability details

NVIDIA vGPU software contains a vulnerability in the Virtual GPU Manager (vGPU plugin), where it improperly validates the length field in a request from a guest. This flaw allows a malicious guest to send a length field that is inconsistent…

more

with the actual length of the input, which may lead to information disclosure, data tampering, or denial of service. This affects vGPU version 12.x (prior to 12.3), version 11.x (prior to 11.5) and version 8.x (prior 8.8).

CWE(s)

Related Threats

No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.

Affected Assets

nvidia
virtual gpu
8.0 — 8.8 · 11.0 — 11.5 · 12.0 — 12.3

Mitigating Controls

Likely Mitigating Controls AI

Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.

addresses: CWE-20

Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.

addresses: CWE-20

Security testing and evaluation at multiple SDLC stages directly detects missing or flawed input validation, with the required remediation process ensuring fixes are applied.

addresses: CWE-20

Directly implements checks on information inputs to reject invalid data before processing.

addresses: CWE-20

Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.

References