CVE-2026-47750
Memory Safety in Leejet Stable-Diffusion.Cpp ≤ master-584-0a7ae07
Raw vector
CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2026-47750 is a high-severity Out-of-bounds Write (CWE-787) vulnerability in Leejet Stable-Diffusion.Cpp. Its CVSS base score is 7.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Privilege Escalation (T1068); ranked at the 8th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as Computer Vision; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SA-15 (Development Process, Standards, and Tools) — see the control section below for these in your framework.
EU & UK References
No EU or UK CSIRT advisories indexed for this CVE.
Vulnerability Data
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…
more
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.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Computer Vision
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: stable diffusion
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation (including fuzzing and bounds checks) finds out-of-bounds write flaws before deployment.
Requiring documented secure-development standards and tools can mandate bounds-checked coding practices that avoid the weakness.
Input validation can structurally reject or sanitize data that would otherwise trigger an out-of-bounds write.
Memory-protection mechanisms limit the exploitability and blast radius of a successful out-of-bounds write.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure-development practices (static analysis, bounds checking, code review) are the primary means of preventing out-of-bounds writes.
Vulnerability scanning and recording can discover out-of-bounds write flaws so they can be remediated.
Patching or replacing vulnerable software directly eliminates known instances of this coding weakness.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing in development and acceptance can detect and prevent out-of-bounds write defects.
Secure development life cycle mandates practices that prevent out-of-bounds writes.
Application security requirements can specify bounds-checking and safe memory handling.
Secure architecture and engineering principles reduce the likelihood of buffer overflows.
Secure coding directly addresses out-of-bounds writes through language choice and coding standards.
Change management can enforce review gates that catch unsafe memory operations before deployment.