CVE-2026-47750 (GCVE-0-2026-47750)
Vulnerability from cvelistv5 – Published: 2026-06-16 18:17 – Updated: 2026-06-17 13:58
VLAI
EPSS
VEX
Title
stable-diffusion.cpp: Heap buffer overflow in GLOBAL opcode parsing for PyTorch checkpoint files
Summary
stable-diffusion.cpp is a pure C/C++ library for running diffusion model (Stable Diffusion, Flux, Wan, Qwen Image, Z-Image, and more) inference. In versions prior to master-584-0a7ae07, the pickle .ckpt parser in src/model.cpp contained a heap buffer overflow vulnerability in the GLOBAL opcode handler. The issue was caused by missing validation when searching for newline-delimited fields. A crafted .ckpt file without the expected newline could cause the parser to use -1 as a copy length, resulting in immediate heap corruption. The attack requires the victim or application to load a .ckpt file from an untrusted source, such as a downloaded model from a model sharing site. The issue has been resolved in version master-584-0a7ae07. If developers are unable to immediately update their applications they can work around this issue by following these instructions: do not load .ckpt checkpoint files from untrusted sources, and prefer trusted model sources and safer formats such as .safetensors where possible.
Severity
7.8 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-17 13:55 UTC
CWE
- CWE-787 - Out-of-bounds Write
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/leejet/stable-diffusion.cpp/se… | x_refsource_CONFIRM |
| https://github.com/leejet/stable-diffusion.cpp/pu… | x_refsource_MISC |
| https://github.com/leejet/stable-diffusion.cpp/co… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| leejet | stable-diffusion.cpp |
Affected:
< master-584-0a7ae07
|
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.
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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