CVE-2026-105748 (GCVE-0-2026-105748)
Vulnerability from cvelistv5 – Published: 2026-10-05 21:34 – Updated: 2026-10-06 14:17
VLAI
EPSS
VEX
Title
Docling: Crafted DoclingDocument JSON embeds local image files into converted output
Summary
Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. From 2.16.0 until 2.131.0, the InputFormat.JSON_DOCLING backend in docling/backend/json/docling_json_backend.py validates serialized DoclingDocument input without rejecting picture image references that contain local paths or file URIs. When the document is enriched or exported with ImageRefMode.EMBEDDED, the DoclingDocument._with_embedded_pictures and ImageRef.pil_image methods can open those references and place readable image bytes in Markdown or HTML output. Disclosure is limited to files Pillow can decode as images, while differing decode behavior can also reveal whether a path exists. Direct untrusted loading through docling-core is outside this Docling fix. This issue is fixed in 2.131.0.
Severity
4.3 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-10-06 14:16 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/4417 | 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 | CPE status | |
|---|---|---|---|---|
| docling-project | docling |
Affected:
>= 2.16.0, < 2.131.0
|
guessed | |
| docling-project | docling-slim |
Affected:
>= 2.16.0, < 2.131.0
|
guessed |
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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.
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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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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