CVE-2026-68514 (GCVE-0-2026-68514)
Vulnerability from cvelistv5 – Published: 2026-08-25 19:40 – Updated: 2026-08-26 12:45
VLAI
EPSS
VEX
Title
OpenEXR: Heap buffer overflow in PyOpenEXR from literal/prefixed RGB channel name collision in deep images
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 contain a heap out-of-bounds write triggered when reading a crafted deep scanline EXR file. When a deep file declares a literal channel named left alongside layer-prefixed RGB channels left.R, left.G, and left.B, the wrapper processes the literal left channel first and allocates a scalar deep sample array for it, then reuses that same array as the coalesced destination for the prefixed RGB group. The deep reader registers sample slices with an RGB stride (three lanes) into storage that was allocated with scalar shape, so decoding the deep samples writes past the allocation. Opening such a file through the default public Python API, OpenEXR.File(path), causes a heap buffer overflow during normal deep sample decode, leading to memory corruption and a crash. This issue is fixed in versions 3.3.13 and 3.4.14.
Severity
5.5 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-26 12:44 UTC
Assigner
References
3 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 |
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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