CVE-2026-82833 (GCVE-0-2026-82833)
Vulnerability from cvelistv5 – Published: 2026-08-31 19:00 – Updated: 2026-09-01 13:59
VLAI
EPSS
VEX
Title
Doccano Open Source Annotation Tools for Machine Learning Practitioners Project Example Detail Endpoint examples ExampleDetail access control
Summary
A vulnerability was identified 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. Affected by this issue is the function ExampleDetail of the file /v1/projects/1/examples/ of the component Project Example Detail Endpoint. Such manipulation leads to improper access controls. The attack may be launched remotely. The exploit is publicly available and might be used. The vendor was contacted early about this disclosure but did not respond in any way.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-01 13:59 UTC
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://vuldb.com/vuln/397245 | vdb-entrytechnical-description |
| https://vuldb.com/vuln/397245/cti | signaturepermissions-required |
| https://vuldb.com/cve/CVE-2026-82833 | third-party-advisory |
| https://vuldb.com/submit/876617 | third-party-advisory |
| https://drive.google.com/file/d/1gqrFfbP7dSSp4Pf-… | 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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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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