CVE-2026-55191 (GCVE-0-2026-55191)
Vulnerability from cvelistv5 – Published: 2026-08-19 17:44 – Updated: 2026-08-19 18:17
VLAI
EPSS
VEX
Title
FreeRDP: Heap-buffer-overflow write in AVC444 YUV buffer allocation
Summary
FreeRDP is a free implementation of the Remote Desktop Protocol. Prior to 3.27.0, FreeRDP clients that negotiate RDPGFX AVC444 with an H.264 decoder backend calculate the intermediate YUV444 allocation size in libfreerdp/codec/h264.c with 32-bit multiplication in avc444_ensure_buffer. A malicious RDP server can supply surface dimensions for which piDstStride multiplied by padDstHeight wraps to a small nonzero value, causing winpr_aligned_recalloc to allocate an undersized buffer before YUV420CombineToYUV444 writes using the actual stride and rectangle dimensions. This can cause a client crash and may permit code execution through attacker-influenced heap corruption. This issue is fixed in version 3.27.0.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-19 18:15 UTC
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/FreeRDP/FreeRDP/security/advis… | x_refsource_CONFIRM |
| https://github.com/FreeRDP/FreeRDP/pull/12873 | x_refsource_MISC |
| https://github.com/FreeRDP/FreeRDP/commit/97f40b9… | x_refsource_MISC |
| https://github.com/FreeRDP/FreeRDP/releases/tag/3.27.0 | x_refsource_MISC |
Impacted products
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},
{
"name": "https://github.com/FreeRDP/FreeRDP/releases/tag/3.27.0",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/FreeRDP/FreeRDP/releases/tag/3.27.0"
}
],
"source": {
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"discovery": "UNKNOWN"
},
"title": "FreeRDP: Heap-buffer-overflow write in AVC444 YUV buffer allocation"
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"id": "CVE-2026-55191",
"lastModified": "2026-09-09T21:19:49.197",
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"Recovery": "NOT_DEFINED",
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"exploitMaturity": "NOT_DEFINED",
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"modifiedAttackComplexity": "NOT_DEFINED",
"modifiedAttackRequirements": "NOT_DEFINED",
"modifiedAttackVector": "NOT_DEFINED",
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"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2026-55191.json",
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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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