CVE-2021-1143 (GCVE-0-2021-1143)
Vulnerability from cvelistv5 – Published: 2021-01-13 21:20 – Updated: 2024-11-12 20:47
VLAI
EPSS
VEX
Title
Cisco Connected Mobile Experiences User Enumeration Vulnerability
Summary
A vulnerability in Cisco Connected Mobile Experiences (CMX) API authorizations could allow an authenticated, remote attacker to enumerate what users exist on the system. The vulnerability is due to a lack of authorization checks for certain API GET requests. An attacker could exploit this vulnerability by sending specific API GET requests to an affected device. A successful exploit could allow the attacker to enumerate users of the CMX system.
Severity
4.3 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-11-08 20:54 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 Connected Mobile Experiences |
Affected:
n/a
|
guessed |
Date Public
2021-01-13 00:00
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Drag nodes to arrange them. Double-click a related vulnerability to open it, or a source to visit its link. The Physics button on the left tunes the layout and the simulation; the legend at the bottom left hides or shows nodes by kind and by sighting type.
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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.
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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.