CVE-2026-47748
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:N/I:N/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-2026-47748 is a medium-severity Out-of-bounds Read (CWE-125) vulnerability in Leejet Stable-Diffusion.Cpp. Its CVSS base score is 5.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Privilege Escalation (T1068); ranked at the 6th 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-8 (Security and Privacy Engineering Principles) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-37137
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. Versions prior to master-584-0a7ae07 are vulnerable to an out-of-bounds reads error through PyTorch checkpoint pickle opcode parsing. The pickle .ckpt…
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parser in src/model.cpp did not consistently check that enough input remained before reading opcode arguments or advancing the parser buffer with a crafted or truncated .ckpt file. Throughout the pickle parser, opcode handlers advanced the parser position with expressions such as buffer += N without first checking that buffer + N <= buffer_end. A truncated file could therefore cause reads past the end of the metadata buffer. LibFuzzer found crashes in under one second using malformed checkpoint inputs. Any application using affected stable-diffusion.cpp releases to load untrusted .ckpt model files could be vulnerable. 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. This issue has been fixed in version master-584-0a7ae07. If developers are unable to immediately update their applications, they can work around this issue by ensuring they do not load .ckpt checkpoint files from untrusted sources. They should 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, pytorch
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 directly finds out-of-bounds read flaws through static analysis, fuzzing, and dynamic bounds checks.
Secure engineering principles require bounds checking and memory-safe constructs that stop out-of-bounds reads from being introduced.
Process isolation confines the effects of an out-of-bounds read to the compromised process.
Input validation rejects malformed indices or lengths that would otherwise cause reads outside buffer bounds.
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 such as bounds checking and memory-safe languages directly prevent out-of-bounds reads.
Vulnerability scanning and recording can discover instances of out-of-bounds reads after code is deployed.
Routine patching replaces vulnerable code containing out-of-bounds read flaws.
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 includes fuzzing and static analysis that detect out-of-bounds read defects before release.
Logging can record evidence of an out-of-bounds read but does not prevent the weakness itself.
Secure development life cycle mandates input validation and bounds checking that directly prevent out-of-bounds reads.
Application security requirements include explicit bounds and memory-safety specifications that mitigate buffer over-reads.
Secure system architecture and engineering principles require memory-safe design patterns and runtime protections against out-of-bounds access.
Secure coding standards explicitly forbid unsafe pointer arithmetic and mandate bounds-checked reads, eliminating CWE-125.