FKIE_CVE-2026-65822
Vulnerability from fkie_nvd - Published: 2026-08-17 21:16 - Updated: 2026-08-17 21:16
Severity
Summary
ERPNext is a free and open source Enterprise Resource Planning tool. Prior to 15.116.0 and 16.23.0, erpnext/selling/report/inactive_customers/inactive_customers.py accepts an unvalidated doctype filter and interpolates it into raw SQL in get_sales_details and get_last_sales_amt, allowing an authenticated user to extract sensitive information and manipulate database queries. This issue is fixed in versions 15.116.0 and 16.23.0.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "erpnext",
"vendor": "frappe",
"versions": [
{
"status": "affected",
"version": "\u003c 15.116.0"
},
{
"status": "affected",
"version": "\u003e= 16.0.0, \u003c 16.23.0"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "ERPNext is a free and open source Enterprise Resource Planning tool. Prior to 15.116.0 and 16.23.0, erpnext/selling/report/inactive_customers/inactive_customers.py accepts an unvalidated doctype filter and interpolates it into raw SQL in get_sales_details and get_last_sales_amt, allowing an authenticated user to extract sensitive information and manipulate database queries. This issue is fixed in versions 15.116.0 and 16.23.0."
}
],
"id": "CVE-2026-65822",
"lastModified": "2026-08-17T21:16:46.610",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "LOW",
"baseScore": 7.6,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "LOW",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:L",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 4.7,
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
},
"published": "2026-08-17T21:16:46.610",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://github.com/frappe/erpnext/commit/29dd6e6681d20bbacb69517d2d2c875aa929eb9e"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/frappe/erpnext/commit/f43af6624610e874e61ad3faf8701e5e6be6271a"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/frappe/erpnext/pull/55721"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/frappe/erpnext/releases/tag/v15.116.0"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/frappe/erpnext/releases/tag/v16.23.0"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/frappe/erpnext/security/advisories/GHSA-x35x-4mvx-h959"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Received",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-89"
}
],
"source": "security-advisories@github.com",
"type": "Primary"
}
]
}
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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.
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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