CVE-2022-1916 (GCVE-0-2022-1916)
Vulnerability from cvelistv5 – Published: 2022-06-27 08:58 – Updated: 2024-08-03 00:17Title
Active Products Tables for WooCommerce < 1.0.5 - Reflected Cross-Site-Scripting
Summary
The Active Products Tables for WooCommerce. Professional products tables for WooCommerce store WordPress plugin before 1.0.5 does not sanitise and escape a parameter before outputting it back in the response of an AJAX action (available to both unauthenticated and authenticated users), leading to a Reflected cross-Site Scripting
Severity
6.1 (Medium)
CWE
- CWE-79 - Cross-site Scripting (XSS)
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://wpscan.com/vulnerability/d16a0c3d-4318-4e… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Unknown | Active Products Tables for WooCommerce. Professional products tables for WooCommerce store |
Affected:
1.0.5 , < 1.0.5
(custom)
|
guessed |
Shadowserver
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 915bd704-9de2-49d4-9af7-34317474b37b
Exploited: Yes
Characteristics
Severity:
61.0
Timestamps
First Seen: 2023-10-23
Asserted: 2023-10-23
Last Seen: 2026-09-05
Scope
Asset Exposure: ['internet-facing']
Notes: Affected: Wordpress / Wordpress Active Products Tables for WooCommerce | Class: cms | Severity: Medium (CVSS 6.1) | IoT: no | In CISA KEV: no | Honeypot connections on 2026-09-05: 1
Evidence
Type: Honeypot
Signal: In The Wild Attempts
Confidence: 70%
Source: shadowserver
Details
| 1D | 1 |
|---|---|
| Iot | no |
| Feed | Shadowserver Foundation honeypot/exploited-vulnerabilities |
| Type | http-scan |
| Class | cms |
| 7D Avg | 0 |
| Vendor | Wordpress |
| 30D Avg | 0 |
| 90D Avg | 0 |
| Product | Wordpress Active Products Tables for WooCommerce |
| Cisa Kev | no |
| Connections | 1 |
| Observation Date | 2026-09-05 |
| Vulnerability Class | CVSS |
| Vulnerability Score | 6.1 |
| Vulnerability Severity | Medium |
References
Created: 2026-07-01 05:54 UTC
| Updated: 2026-09-07 01:01 UTC
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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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