CVE-2021-47975 (GCVE-0-2021-47975)
Vulnerability from cvelistv5 – Published: 2026-05-16 15:26 – Updated: 2026-05-26 11:51
VLAI
EPSS
VEX
Title
WordPress Plugin WP Learn Manager 1.1.2 Stored XSS
Summary
WP Learn Manager 1.1.2 contains a stored cross-site scripting vulnerability that allows unauthenticated attackers to inject malicious scripts through the `fieldtitle` parameter. Attackers can submit POST requests to the jslm_fieldordering page with XSS payloads in the fieldtitle field to execute arbitrary JavaScript when administrators view the field ordering interface.
Severity
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-18 19:55 UTC
CWE
- CWE-79 - Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://www.exploit-db.com/exploits/50086 | exploit |
| https://wplearnmanager.com/ | product |
| https://wordpress.org/plugins/learn-manager/ | product |
| https://www.vulncheck.com/advisories/wordpress-pl… | third-party-advisory |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Wplearnmanager | WP Learn Manager |
Affected:
1.1.2
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Date Public
2021-07-02 00:00
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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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