CVE-2026-94107 (GCVE-0-2026-94107)
Vulnerability from cvelistv5 – Published: 2026-09-20 11:09 – Updated: 2026-09-20 11:09
VLAI
EPSS
VEX
Title
NivoCart through 2.4.0 Predictable Administrator Password Reset Token
Summary
NivoCart through 2.4.0 contains a predictable password reset token vulnerability in the forgotten.php endpoint that generates recovery codes using substr(md5(mt_rand()), 0, 10). Attackers who know an administrator's email address can request a password reset and predict the token to gain administrative account access without rate limiting or expiration.
Severity
8.1 (High)
CWE
- CWE-338 - Use of Cryptographically Weak Pseudo-Random Number Generator (PRNG)
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://github.com/nivocart/nivocart/issues/25 | issue-tracking |
| https://github.com/nivocart/nivocart/blob/6ea6600… | technical-description |
| https://github.com/nivocart/nivocart/blob/6ea6600… | technical-description |
| https://github.com/nivocart/nivocart | product |
| https://www.vulncheck.com/advisories/nivocart-thr… | third-party-advisory |
Impacted products
Date Public
2026-09-19 00:00
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"packageURL": "pkg:composer/nivocart/nivocart",
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}
],
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"name": "GitHub Issue #25",
"tags": [
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],
"url": "https://github.com/nivocart/nivocart/issues/25"
},
{
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"tags": [
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"url": "https://github.com/nivocart/nivocart/blob/6ea6600cda1263ae70c0aab0a70d31091a32c46b/upload/admin/controller/common/forgotten.php#L20-L34"
},
{
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"url": "https://github.com/nivocart/nivocart/blob/6ea6600cda1263ae70c0aab0a70d31091a32c46b/upload/admin/model/user/user.php#L119-L123"
},
{
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"url": "https://github.com/nivocart/nivocart"
},
{
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"tags": [
"third-party-advisory"
],
"url": "https://www.vulncheck.com/advisories/nivocart-through-2.4.0-predictable-administrator-password-reset-token"
}
],
"title": "NivoCart through 2.4.0 Predictable Administrator Password Reset Token",
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"datePublished": "2026-09-20T11:09:40.891Z",
"dateReserved": "2026-09-20T10:56:43.733Z",
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"state": "PUBLISHED"
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}
],
"id": "CVE-2026-94107",
"lastModified": "2026-09-20T12:17:06.277",
"metrics": {
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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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