CVE-2026-16007 (GCVE-0-2026-16007)
Vulnerability from cvelistv5 – Published: 2026-08-15 05:21 – Updated: 2026-08-15 05:21
VLAI
EPSS
VEX
Title
Authenticated SQL Injection in AppFlowy
Summary
AppFlowy's qcuiknote feature is affected by a SQL injection vulnerability. Authenticated users with access to the feature can inject arbitrary SQL to exfiltrate data in the underlying SQL database.
Severity
CWE
- CWE-89 - Improper neutralization of special elements used in an SQL command ('SQL injection')
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://projectblack.io/blog/appflowy-authenticat… | exploitthird-party-advisory |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| AppFlowy-IO | AppFlowy-Cloud |
Affected:
0 , ≤ *
(custom)
|
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"providerMetadata": {
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],
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"source": {
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"datePublished": "2026-08-15T05:21:17.372Z",
"dateReserved": "2026-07-17T05:03:34.369Z",
"dateUpdated": "2026-08-15T05:21:17.372Z",
"state": "PUBLISHED"
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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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