Raw vector
CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2026-25990 is a high-severity Out-of-bounds Write (CWE-787) vulnerability in Python Pillow. Its CVSS base score is 8.6 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Privilege Escalation (T1068); ranked at the 30th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Computer Vision; in the Data-Related Vulnerabilities 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.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-25990 is an out-of-bounds write vulnerability (CWE-787) in Pillow, a Python imaging library. It affects versions from 10.3.0 up to but not including 12.1.1 and can be triggered by loading a specially crafted PSD image. The issue carries a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H), reflecting high availability impact with no confidentiality or integrity effects.
Any remote attacker can exploit this vulnerability without privileges or user interaction by supplying a malicious PSD image to a vulnerable application using Pillow for image processing. Exploitation leads to an out-of-bounds write, typically resulting in denial-of-service via application crash or memory corruption.
Pillow version 12.1.1 resolves the vulnerability through a specific commit at https://github.com/python-pillow/Pillow/commit/9000313cc5d4a31bdcdd6d7f0781101abab553aa. Further details appear in the project's GitHub security advisory at https://github.com/python-pillow/Pillow/security/advisories/GHSA-cfh3-3jmp-rvhc and an oss-security mailing list post at http://www.openwall.com/lists/oss-security/2026/02/12/1. Mitigation requires updating to Pillow 12.1.1 or later.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-6218
Vulnerability Data
Pillow is a Python imaging library. From 10.3.0 to before 12.1.1, an out-of-bounds write may be triggered when loading a specially crafted PSD image. This vulnerability is fixed in 12.1.1.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Computer Vision
- Risk Domain
- Data-Related Vulnerabilities
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: pillow
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.