CVE-2026-62986 (GCVE-0-2026-62986)
Vulnerability from cvelistv5 – Published: 2026-08-25 18:27 – Updated: 2026-08-25 19:20
VLAI
EPSS
VEX
Title
OpenEXR: PyOpenEXR deep prefixed RGB stale lane disclosure
Summary
OpenEXR is the reference implementation and specification for the EXR image file format, widely used in the motion picture industry. In versions 3.3.0 through 3.3.12 and 3.4.0 through 3.4.13, the PyOpenEXR Python bindings return stale heap data when reading a crafted deep scanline EXR that uses layer-prefixed RGB channels. With the default channel coalescing (separate_channels=False), the wrapper groups channels such as left.R, left.G, and left.B into a single RGB sample array, but the lane-offset calculation in PyPart::setDeepSliceData() only recognizes the exact unprefixed names G, B, and A. As a result, prefixed channels like left.G and left.B are decoded into lane 0 while lanes 1 and 2 are left uninitialized and returned to Python. A Python application that reads untrusted deep EXR files through the default OpenEXR.File API and then logs, serializes, previews, or otherwise processes the resulting NumPy sample arrays may expose uninitialized same-process heap contents, in addition to receiving incorrect green and blue channel data. This issue is fixed in versions 3.3.13 and 3.4.14.
Severity
4.3 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-25 19:19 UTC
CWE
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/AcademySoftwareFoundation/open… | x_refsource_CONFIRM |
| https://github.com/AcademySoftwareFoundation/open… | x_refsource_MISC |
| https://github.com/AcademySoftwareFoundation/open… | x_refsource_MISC |
| https://github.com/AcademySoftwareFoundation/open… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| AcademySoftwareFoundation | openexr |
Affected:
>= 3.3.0, < 3.3.13
Affected: >= 3.4.0, < 3.4.14 |
guessed |
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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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