CVE-2022-20857 (GCVE-0-2022-20857)
Vulnerability from cvelistv5 – Published: 2022-07-21 03:45 – Updated: 2024-11-01 18:59
VLAI
EPSS
VEX
Title
Cisco Nexus Dashboard Unauthorized Access Vulnerabilities
Summary
Multiple vulnerabilities in Cisco Nexus Dashboard could allow an unauthenticated, remote attacker to execute arbitrary commands, read or upload container image files, or perform a cross-site request forgery attack. For more information about these vulnerabilities, see the Details section of this advisory.
Severity
9.8 (Critical)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-11-01 18:44 UTC
CWE
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://tools.cisco.com/security/center/content/C… | vendor-advisoryx_refsource_CISCO |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Cisco | Cisco Nexus Dashboard |
Affected:
n/a
|
guessed |
Date Public
2022-07-20 00:00
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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
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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.
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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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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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.