CVE-2026-82834 (GCVE-0-2026-82834)
Vulnerability from cvelistv5 – Published: 2026-08-31 19:15 – Updated: 2026-08-31 19:15
VLAI
EPSS
VEX
Title
Doccano Open Source Annotation Tools for Machine Learning Practitioners Bulk-Delete Endpoint category-types LabelList access control
Summary
A security flaw has been discovered in Doccano Open Source Annotation Tools for Machine Learning Practitioners and Auto Labeling Pipeline Module to Annotate a Document Automatically up to 1.8.5. This affects the function LabelList of the file /v1/projects/1/category-types of the component Bulk-Delete Endpoint. Performing a manipulation results in improper access controls. Remote exploitation of the attack is possible. The exploit has been released to the public and may be used for attacks. The vendor was contacted early about this disclosure but did not respond in any way.
Severity
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://vuldb.com/vuln/397246 | vdb-entrytechnical-description |
| https://vuldb.com/vuln/397246/cti | signaturepermissions-required |
| https://vuldb.com/cve/CVE-2026-82834 | third-party-advisory |
| https://vuldb.com/submit/876639 | third-party-advisory |
| https://drive.google.com/file/d/1qRWtrUHfr-huJlr-… | exploit |
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Doccano | Open Source Annotation Tools for Machine Learning Practitioners |
Affected:
1.8.0
Affected: 1.8.1 Affected: 1.8.2 Affected: 1.8.3 Affected: 1.8.4 Affected: 1.8.5 cpe:2.3:a:doccano:open_source_annotation_tools_for_machine_learning_practitioners:*:*:*:*:*:*:*:* |
||
| Doccano | Auto Labeling Pipeline Module to Annotate a Document Automatically |
Affected:
1.8.0
Affected: 1.8.1 Affected: 1.8.2 Affected: 1.8.3 Affected: 1.8.4 Affected: 1.8.5 cpe:2.3:a:doccano:auto_labeling_pipeline_module_to_annotate_a_document_automatically:*:*:*:*:*:*:*:* |
{
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{
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}
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},
{
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"tags": [
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"value": "VulDB entry last update"
}
],
"title": "Doccano Open Source Annotation Tools for Machine Learning Practitioners Bulk-Delete Endpoint category-types LabelList access control",
"x_generator": [
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]
}
},
"cveMetadata": {
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"cveId": "CVE-2026-82834",
"datePublished": "2026-08-31T19:15:08.323Z",
"dateReserved": "2026-08-31T07:25:51.029Z",
"dateUpdated": "2026-08-31T19:15:08.323Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2",
"vulnerability-lookup:meta": {
"epss": {
"cve": "CVE-2026-82834",
"date": "2026-09-01",
"epss": "0.00221",
"percentile": "0.12487"
},
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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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