CVE-2026-81728 (GCVE-0-2026-81728)
Vulnerability from cvelistv5 – Published: 2026-08-27 20:07 – Updated: 2026-08-27 20:07
VLAI
EPSS
VEX
Title
Dolibarr before 24.0.0 SQL Injection via the CSV and XLSX Import Update Keys
Summary
Dolibarr before 24.0.0 contains a SQL injection in its CSV and XLSX import wizard. The wizard reads its update keys with GETPOST('updatekeys', 'array') in htdocs/imports/import.php, which applies only the generic alphanohtml filter: that strips HTML but leaves SQL keywords, comment markers, parentheses, spaces and quotes intact. import_insert() in htdocs/core/modules/import/import_csv.modules.php then iterates the submitted values and builds a filter with $where[] = $key.' = '.$data[$key], having first applied preg_replace('/^.*\./i', '', $key), an alias strip that does nothing to a value containing no dot. The assembled string is executed through $this->db->query(). The injected SELECT resolves the row id that the import then assigns to $lastinsertid, which becomes the WHERE target of a subsequent UPDATE, so a UNION SELECT returning an attacker-chosen integer both exfiltrates arbitrary table content and redirects which row the import overwrites; for category link tables the raw filter array is spliced into that UPDATE directly. The interface offers a fixed list of legitimate column codes but the server never checks the submitted values against it. A user holding the import permission can exploit this. Release 23.0.4 does not carry the fix; the allow-list test was added in 24.0.0.
Severity
CWE
- CWE-89 - Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/Dolibarr/dolibarr/commit/24b1b… | patch |
| https://github.com/Dolibarr/dolibarr | product |
| https://github.com/Dolibarr/dolibarr/blob/23.0.4/… | technical-description |
| https://www.vulncheck.com/advisories/dolibarr-bef… | third-party-advisory |
Impacted products
Date Public
2026-07-09 00:00
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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.
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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