CVE-2025-47204 (GCVE-0-2025-47204)
Vulnerability from cvelistv5 – Published: 2025-05-13 00:00 – Updated: 2025-05-14 13:41Summary
An issue was discovered in post.php in bootstrap-multiselect (aka Bootstrap Multiselect) 1.1.2. A PHP script in the source code echoes arbitrary POST data. If a developer adopts this structure wholesale in a live application, it could create a Reflective Cross-Site Scripting (XSS) vulnerability exploitable through Cross-Site Request Forgery (CSRF).
Severity
6.1 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-05-14 13:39 UTC
CWE
- n/a
- CWE-352 - Cross-Site Request Forgery (CSRF)
Assigner
References
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: baf6ef6a-70dd-46d8-bad4-678a19522b5f
Exploited: Yes
Timestamps
First Seen: 2025-05-13
Asserted: 2025-05-13
Scope
Notes: An issue was discovered in post.php in bootstrap-multiselect (aka Bootstrap Multiselect) 1.1.2. A PHP script in the source code echoes arbitrary... | Affected: David Stutz / Bootstrap Multiselect | CVSS: 6.1 (MEDIUM) | EPSS: 0.00438 | 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 | An issue was discovered in post.php in bootstrap-multiselect (aka Bootstrap Multiselect) 1.1.2. A PHP script in the source code echoes arbitrary... |
| Cve Id | CVE-2025-47204 |
| Vendor | David Stutz |
| Ghsa Id | GHSA-GV5R-9GXR-V74W |
| Product | Bootstrap Multiselect |
| Added Date | 2025-05-13T00:00:00.000Z |
| Cvss Score | 6.1 |
| Epss Score | 0.00438 |
| Previous Ids | |
| Cvss Severity | MEDIUM |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.35777 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2025-47204 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-10-02 09:10 CEST
| Updated: 2026-10-02 09:10 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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