GHSA-HCWQ-44XG-QQ4G
Vulnerability from github – Published: 2026-08-12 21:31 – Updated: 2026-08-12 21:31
VLAI
Details
The MongoDB BI Connector ODBC Driver converts floating point column values into text without checking that the result fits within the destination buffer. When an application reads a sufficiently large floating point value as text, the driver may write beyond the end of that buffer and corrupt adjacent memory. A user who can store data in a collection read through the BI Connector could use this to crash the application performing the read.
Severity
{
"affected": [],
"aliases": [
"CVE-2026-18888"
],
"database_specific": {
"cwe_ids": [
"CWE-787"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-08-12T21:17:37Z",
"severity": "HIGH"
},
"details": "The MongoDB BI Connector ODBC Driver converts floating point column values into text without checking that the result fits within the destination buffer. When an application reads a sufficiently large floating point value as text, the driver may write beyond the end of that buffer and corrupt adjacent memory. A user who can store data in a collection read through the BI Connector could use this to crash the application performing the read.",
"id": "GHSA-hcwq-44xg-qq4g",
"modified": "2026-08-12T21:31:44Z",
"published": "2026-08-12T21:31:44Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-18888"
},
{
"type": "WEB",
"url": "https://github.com/mongodb/mongo-bi-connector-odbc-driver/releases/tag/v1.4.9"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/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.
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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