CVE-2025-11682 (GCVE-0-2025-11682)
Vulnerability from cvelistv5 – Published: 2025-10-27 07:39 – Updated: 2025-10-27 17:25
VLAI
EPSS
VEX
Title
Stored Cross-Site Scripting in Perx Customer Engagement & Loyalty Platform
Summary
Stored cross-site scripting (XSS) vulnerability in the LMT Dashboard of the Perx Customer Engagement & Loyalty Platform allows an authenticated attacker to execute arbitrary JavaScript code in a victim's browser. The vulnerability is due to improper sanitization of SVG file uploads. An attacker can upload a malicious SVG file containing a script payload to a campaign. When another user views this image on the public LMT microsite, the script executes, which can lead to session hijacking, data theft, or other unauthorized actions.This issue affects Customer Engagement & Loyalty Platform before 4.617.4.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-10-27 17:24 UTC
CWE
Assigner
References
1 reference
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Perx Technologies | Customer Engagement & Loyalty Platform |
Affected:
0 , < 4.617.4
(custom)
|
guessed |
Date Public
2025-10-26 23:00
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Sightings
| Author | Source | Type | Date | Other |
|---|
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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
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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.
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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.