CVE-2026-105747 (GCVE-0-2026-105747)
Vulnerability from cvelistv5 – Published: 2026-10-05 21:32 – Updated: 2026-10-06 14:48
VLAI
EPSS
VEX
Title
Docling: METS-GBS archive member limit enforced after full member enumeration (memory exhaustion during format detection)
Summary
Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. From 2.45.0 until 2.131.0, METS-GBS format detection in docling/datamodel/document.py and the backend in docling/backend/mets_gbs_backend.py call tarfile.TarFile.getmembers() before enforcing the max_member_count limit, causing the full archive member list to be allocated before the limit can stop processing. A small gzip-compressed tar archive with a very large number of empty members can therefore consume memory proportional to the declared member count, including during format detection before the allowed_formats restriction is applied. This issue is a residual weakness in the member-count protection added for CVE-2026-44018. This issue is fixed in 2.131.0.
Severity
4.3 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-10-06 14:34 UTC
CWE
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/docling-project/docling/securi… | x_refsource_CONFIRM |
| https://github.com/docling-project/docling/pull/4412 | x_refsource_MISC |
| https://github.com/docling-project/docling/commit… | x_refsource_MISC |
| https://github.com/docling-project/docling/releas… | x_refsource_MISC |
Impacted products
2 products
| Vendor | Product | Version | |
|---|---|---|---|
| docling-project | docling |
Affected:
>= 2.45.0, < 2.131.0
|
|
| docling-project | docling-slim |
Affected:
>= 2.45.0, < 2.131.0
|
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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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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