CVE-2022-28005 (GCVE-0-2022-28005)
Vulnerability from cvelistv5 – Published: 2022-05-06 00:00 – Updated: 2024-08-03 05:41Summary
An issue was discovered in the 3CX Phone System Management Console prior to version 18 Update 3 FINAL. An unauthenticated attacker could abuse improperly secured access to arbitrary files on the server (via /Electron/download directory traversal in conjunction with a path component that uses backslash characters), leading to cleartext credential disclosure. Afterwards, the authenticated attacker is able to upload a file that overwrites a 3CX service binary, leading to Remote Code Execution as NT AUTHORITY\SYSTEM on Windows installations. NOTE: this issue exists because of an incomplete fix for CVE-2022-48482.
Severity
9.8 (Critical)
CWE
- n/a
Assigner
References
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: bdf3b724-ac42-451b-aaba-a3620d6abb50
Exploited: Yes
Timestamps
First Seen: 2026-01-09
Asserted: 2026-01-09
Scope
Notes: An issue was discovered in the 3CX Phone System Management Console prior to version 18 Update 3 FINAL. An unauthenticated attacker could abuse... | Affected: 3CX / 3CX Phone System | CVSS: 9.8 (CRITICAL) | EPSS: 0.06729 | Used in malware: unknown | Not yet in CISA KEV: True
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: previdian
Details
| Feed | Previdian (previdian.com) |
|---|---|
| Title | An issue was discovered in the 3CX Phone System Management Console prior to version 18 Update 3 FINAL. An unauthenticated attacker could abuse... |
| Cve Id | CVE-2022-28005 |
| Vendor | 3CX |
| Ghsa Id | None |
| Product | 3CX Phone System |
| Added Date | 2026-01-09T00:00:00.000Z |
| Cvss Score | 9.8 |
| Epss Score | 0.06729 |
| Previous Ids | |
| Cvss Severity | CRITICAL |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.93746 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2022-28005 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-10-02 09:09 CEST
| Updated: 2026-10-02 09:09 CEST
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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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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.
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