CVE-2026-59981 (GCVE-0-2026-59981)
Vulnerability from cvelistv5 – Published: 2026-08-25 19:58 – Updated: 2026-08-28 22:35
VLAI
EPSS
VEX
Title
OpenEXR: Heap OOB read in SampleCountChannel row when using nonzero dataWindow
Summary
OpenEXR is the reference implementation and specification for the EXR image file format, widely used in the motion picture industry. In versions through 3.2.10, 3.3.0 through 3.3.12, and 3.4.0 through 3.4.13, the OpenEXRUtil library returns an out-of-bounds pointer from the SampleCountChannel::row() API when a deep image has a non-zero dataWindow origin. The row() accessor is documented as 0-based and computes its address from an internal base that is offset for absolute pixel coordinates, so the two coordinate models conflict whenever dataWindow.min is non-zero. For a deep image whose data window has a large negative vertical origin, row(0) points far outside the allocated sample-count buffer. An application that opens an attacker-controlled deep EXR file and accesses sample counts through row() performs an out-of-bounds read, which can crash the process or, under a controlled heap layout, return adjacent heap memory as sample-count values. This issue is fixed in versions 3.2.11, 3.3.13, and 3.4.14.
Severity
7.1 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-28 22:35 UTC
CWE
- CWE-125 - Out-of-bounds Read
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:
< 3.2.11
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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