FKIE_CVE-2026-59186
Vulnerability from fkie_nvd - Published: 2026-08-25 17:17 - Updated: 2026-08-25 19:16
Severity
Summary
OpenEXR is the reference implementation and specification for the EXR image format, widely used in the motion picture industry. In versions before 3.2.11, 3.3.0 through 3.3.12, and 3.4.0 through 3.4.13, a crafted tiled EXR can trigger a heap out-of-bounds write on 32-bit/ILP32 builds when read through the public TiledRgbaInputFile RGBA API. The file uses a small 40x40 dataWindow but a 65537x65537 tile size. On ILP32, the Array2D<Rgba> tile-conversion buffer size calculation overflows, allocates a much smaller heap buffer, and tile decode writes past that allocation. This issue is fixed in versions 3.2.11, 3.3.13, and 3.4.14.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "openexr",
"vendor": "AcademySoftwareFoundation",
"versions": [
{
"status": "affected",
"version": "\u003c 3.2.11"
},
{
"status": "affected",
"version": "\u003e= 3.3.0, \u003c 3.3.13"
},
{
"status": "affected",
"version": "\u003e= 3.4.0, \u003c 3.4.14"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "OpenEXR is the reference implementation and specification for the EXR image format, widely used in the motion picture industry. In versions before 3.2.11, 3.3.0 through 3.3.12, and 3.4.0 through 3.4.13, a crafted tiled EXR can trigger a heap out-of-bounds write on 32-bit/ILP32 builds when read through the public TiledRgbaInputFile RGBA API. The file uses a small 40x40 dataWindow but a 65537x65537 tile size. On ILP32, the Array2D\u003cRgba\u003e tile-conversion buffer size calculation overflows, allocates a much smaller heap buffer, and tile decode writes past that allocation. This issue is fixed in versions 3.2.11, 3.3.13, and 3.4.14."
}
],
"id": "CVE-2026-59186",
"lastModified": "2026-08-25T19:16:51.340",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.1,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:L/A:H",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 4.2,
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-59186",
"options": [
{
"exploitation": "poc"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-08-25T18:18:47.096879Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-08-25T17:17:36.787",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://github.com/AcademySoftwareFoundation/openexr/commit/71907b44ce9a1b05bf3934b8a7821752750731ab"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/AcademySoftwareFoundation/openexr/commit/904141d3a1f86327ad1e2b93fc92ce2dd5881d34"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/AcademySoftwareFoundation/openexr/commit/b1a5887372772d79328f4eb42b7f86352a38170b"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/AcademySoftwareFoundation/openexr/security/advisories/GHSA-f667-c4wm-c8gq"
},
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"url": "https://github.com/AcademySoftwareFoundation/openexr/security/advisories/GHSA-f667-c4wm-c8gq"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Received",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-122"
},
{
"lang": "en",
"value": "CWE-190"
},
{
"lang": "en",
"value": "CWE-787"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
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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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