CVE-2022-36537 (GCVE-0-2022-36537)

Vulnerability from cvelistv5 – Published: 2022-08-26 00:00 – Updated: 2025-10-21 23:15
VLAI CISA Previdian
Summary
ZK Framework v9.6.1, 9.6.0.1, 9.5.1.3, 9.0.1.2 and 8.6.4.1 allows attackers to access sensitive information via a crafted POST request sent to the component AuUploader.
Severity
7.5 (High)
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N
SSVC
Exploitation: active Automatable: yes Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-01-29 15:40 UTC
CWE
Assigner
References
CISA
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 8f03093b-700a-4dc2-9a3b-1c1739a8d535

Vulnerability ID: CVE-2022-36537

Status: Confirmed

Status Updated: 2023-02-27 01:00 CET

Exploited: Yes

Timestamps
First Seen: 2023-02-27
Asserted: 2023-02-27
Scope
Notes: ZK Framework AuUploader Unspecified Vulnerability | Affected: ZK Framework / AuUploader | Description: ZK Framework AuUploader servlets contain an unspecified vulnerability that could allow an attacker to retrieve the content of a file located in the web context. The ZK Framework is an open-source Java framework. This vulnerability can impact multiple products, including but not limited to ConnectWise R1Soft Server Backup Manager. | Required action: Apply updates per vendor instructions. | Due date: 2023-03-20 | Known ransomware campaign use (KEV): Known | Notes (KEV): https://tracker.zkoss.org/browse/ZK-5150; https://nvd.nist.gov/vuln/detail/CVE-2022-36537
Evidence

Type: Vendor Report

Signal: Successful Exploitation

Confidence: 80%

Source: cisa-kev

Details
Cwes CWE-441
Feed CISA Known Exploited Vulnerabilities Catalog
Product AuUploader
Due Date 2023-03-20
Date Added 2023-02-27
Vendorproject ZK Framework
Vulnerabilityname ZK Framework AuUploader Unspecified Vulnerability
Knownransomwarecampaignuse Known
References
Created: 2026-10-02 08:09 CEST | Updated: 2026-10-02 08:09 CEST
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: c77b7aec-5489-451a-89ed-905fc15d5c41

Vulnerability ID: CVE-2022-36537

Status: Confirmed

Status Updated: 2023-02-27 01:00 CET

Exploited: Yes

Timestamps
First Seen: 2023-02-27
Asserted: 2023-02-27
Scope
Notes: ZK Framework v9.6.1, 9.6.0.1, 9.5.1.3, 9.0.1.2 and 8.6.4.1 allows attackers to access sensitive information via a crafted POST request sent to the... | Affected: Potix / ZK Framework | CVSS: 7.5 (HIGH) | EPSS: 0.95397 | Used in malware: yes | Not yet in CISA KEV: False
Evidence

Type: Public Report

Signal: Confirmed Compromise

Confidence: 70%

Source: previdian

Details
Feed Previdian (previdian.com)
Title ZK Framework v9.6.1, 9.6.0.1, 9.5.1.3, 9.0.1.2 and 8.6.4.1 allows attackers to access sensitive information via a crafted POST request sent to the...
Cve Id CVE-2022-36537
Vendor Potix
Ghsa Id GHSA-6278-2Q4M-CMF3
Product ZK Framework
Added Date 2023-02-27T00:00:00.000Z
Cvss Score 7.5
Epss Score 0.95397
Previous Ids
Cvss Severity HIGH
Virtual Patch False
Cvss Estimated False
Epss Percentile 0.99868
Used In Malware yes
Vulnerability Id CVE-2022-36537
Ahead Of Cisa Kev None
Not Yet In Cisa Kev False
References
Created: 2026-10-02 09:11 CEST | Updated: 2026-10-02 09:11 CEST
Show details on NVD website
To clipboard 

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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

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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.
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  • 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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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.