CVE-2026-61400 (GCVE-0-2026-61400)
Vulnerability from cvelistv5 – Published: 2026-08-21 08:25 – Updated: 2026-08-22 03:56
VLAI
EPSS
VEX
Title
Apache CloudStack: Get and Run Diagnostics Command Injection
Summary
Improper Neutralization of Special Elements used in a Command ('Command Injection') vulnerability in Apache CloudStack's run and get diagnostics functionality for the system VMs and virtual routers.
An authenticated user holding the permissions required to invoke either `getDiagnosticsData` or `runDiagnostics` can achieve arbitrary command execution on the system VM and/or Virtual Router instances, with commands running as root (or as the diagnostics-process user, at minimum). This represents a full compromise of the affected instance and, depending on network segmentation, may provide a foothold for lateral movement within the CloudStack-managed infrastructure, including access to guest network traffic handled by the compromised Virtual Router.
The getDiagnosticsData and runDiagnostics APIs are restricted to only Admin role accounts by default.
This issue affects Apache CloudStack: from 4.20.0.0 through 4.20.3.0 and from 4.21.0.0 through 4.22.1.0.
Users are recommended to upgrade to version 4.20.3.1 or 4.22.1.1 or later, which fixes the issue.
Severity
8.8 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-21 00:00 UTC
CWE
- CWE-77 - Improper Neutralization of Special Elements used in a Command ('Command Injection')
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://lists.apache.org/thread/g6cwddtjrwbh1d56w… | vendor-advisory |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Apache Software Foundation | Apache CloudStack |
Affected:
4.14.0.0 , ≤ 4.20.3.0
(semver)
Affected: 4.21.0.0 , ≤ 4.22.1.0 (semver) |
guessed |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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