CVE-2026-17538 (GCVE-0-2026-17538)
Vulnerability from cvelistv5 – Published: 2026-10-07 23:27 – Updated: 2026-10-07 23:27
VLAI
EPSS
VEX
Title
Appointment Booking Plugin – LatePoint | Calendar & Scheduling for WordPress <= 5.6.9 - Authenticated (Subscriber+) Insecure Direct Object Reference to Arbitrary Customer PII Modification
Summary
The LatePoint - Appointment Booking & Reservation plugin for WordPress is vulnerable to Insecure Direct Object Reference in versions up to, and including, 5.6.9. This is due to the process_step_customer() function using is_user_logged_in() as the sole gate before merging POSTed customer data into an existing LatePoint customer, without any ownership checks. This makes it possible for authenticated attackers, with Subscriber-level access and above, to modify the personal information (first name, last name, email, phone, notes) of arbitrary LatePoint customers, and, when the contact_merge setting is 'phone', to overwrite the victim's email address and take over the account via a password reset.
Severity
5.4 (Medium)
CWE
- CWE-639 - Authorization Bypass Through User-Controlled Key
Assigner
References
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| latepoint | Appointment Booking Plugin – LatePoint | Calendar & Scheduling for WordPress |
Affected:
0 , ≤ 5.6.9
(semver)
|
guessed |
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}
],
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"providerMetadata": {
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"orgId": "b15e7b5b-3da4-40ae-a43c-f7aa60e62599",
"shortName": "Wordfence"
},
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},
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}
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"value": "Vendor Notified"
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"time": "2026-10-07T11:12:20.000Z",
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"title": "Appointment Booking Plugin \u2013 LatePoint | Calendar \u0026 Scheduling for WordPress \u003c= 5.6.9 - Authenticated (Subscriber+) Insecure Direct Object Reference to Arbitrary Customer PII Modification"
}
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"cveMetadata": {
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"dateReserved": "2026-07-27T09:35:22.512Z",
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"state": "PUBLISHED"
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"vulnerability-lookup:meta": {
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"date": "2026-10-08",
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"percentile": "0.03746"
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"nvd": {
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}
],
"id": "CVE-2026-17538",
"lastModified": "2026-10-08T17:24:31.040",
"metrics": {
"cvssMetricV31": [
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"cvssData": {
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"attackVector": "NETWORK",
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"confidentialityImpact": "LOW",
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}
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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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