CVE-2022-47615 (GCVE-0-2022-47615)
Vulnerability from cvelistv5 – Published: 2023-01-24 09:05 – Updated: 2026-04-28 16:07Title
WordPress LearnPress Plugin <= 4.1.7.3.2 is vulnerable to Local File Inclusion
Summary
Local File Inclusion vulnerability in LearnPress – WordPress LMS Plugin <= 4.1.7.3.2 versions.
Severity
9.3 (Critical)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-01-08 21:47 UTC
CWE
- Local File Inclusion
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://patchstack.com/database/vulnerability/lea… | vdb-entry |
| https://patchstack.com/articles/multiple-critical… | technical-description |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| ThimPress | LearnPress – WordPress LMS Plugin |
Affected:
n/a , ≤ 4.1.7.3.2
(custom)
|
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 2f35f1c4-95c8-4db3-9144-108e9ec0cd7d
Exploited: Yes
Timestamps
First Seen: 2023-01-24
Asserted: 2023-01-24
Scope
Notes: WordPress LearnPress Plugin <= 4.1.7.3.2 is vulnerable to Local File Inclusion | Affected: ThimPress / LearnPress – WordPress LMS Plugin | CVSS: 9.3 (CRITICAL) | EPSS: 0.05063 | 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 | WordPress LearnPress Plugin <= 4.1.7.3.2 is vulnerable to Local File Inclusion |
| Cve Id | CVE-2022-47615 |
| Vendor | ThimPress |
| Ghsa Id | None |
| Product | LearnPress – WordPress LMS Plugin |
| Added Date | 2023-01-24T09:05:26.000Z |
| Cvss Score | 9.3 |
| Epss Score | 0.05063 |
| Previous Ids | |
| Cvss Severity | CRITICAL |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.92052 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2022-47615 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-10-02 09:11 CEST
| Updated: 2026-10-02 09:11 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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