GHSA-99PQ-PXMQ-GF2Q
Vulnerability from github – Published: 2026-09-17 15:32 – Updated: 2026-09-17 15:32
VLAI
Details
admin3 through 3.0.0 stores account passwords using single-round MD5 with only the username as salt and no key derivation function. Attackers with database access can recover plaintext passwords through offline dictionary or brute-force attacks due to negligible computational effort.
Severity
4.9 (Medium)
{
"affected": [],
"aliases": [
"CVE-2026-92921"
],
"database_specific": {
"cwe_ids": [
"CWE-916"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-09-17T13:17:01Z",
"severity": "MODERATE"
},
"details": "admin3 through 3.0.0 stores account passwords using single-round MD5 with only the username as salt and no key derivation function. Attackers with database access can recover plaintext passwords through offline dictionary or brute-force attacks due to negligible computational effort.",
"id": "GHSA-99pq-pxmq-gf2q",
"modified": "2026-09-17T15:32:12Z",
"published": "2026-09-17T15:32:12Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-92921"
},
{
"type": "WEB",
"url": "https://github.com/LinYuanyi1/cve-request-poc/blob/master/admin3/C08_md5_credential_hashing.py"
},
{
"type": "WEB",
"url": "https://github.com/cjbi/admin3"
},
{
"type": "WEB",
"url": "https://github.com/cjbi/admin3/blob/3.0.0/admin3-server/src/main/java/tech/wetech/admin3/common/SecurityUtil.java"
},
{
"type": "WEB",
"url": "https://github.com/cjbi/admin3/blob/3.0.0/admin3-server/src/main/java/tech/wetech/admin3/sys/model/UserCredential.java"
},
{
"type": "WEB",
"url": "https://www.vulncheck.com/advisories/admin3-through-3.0.0-weak-password-hashing-via-single-round-md5"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:N/A:N",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:H/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
"type": "CVSS_V4"
}
]
}
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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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