Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:L/A:LSummary
CVE-2026-41705 is a high-severity Expression Language Injection (CWE-917) vulnerability in Vmware Spring Ai. Its CVSS base score is 8.6 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 28th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as NLP and Transformers; in the Data-Related Vulnerabilities risk domain.
The strongest mitigations our analysis identified map to SI-10 (Information Input Validation) and SA-8 (Security and Privacy Engineering Principles) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-28875
Vulnerability Data
Spring AI's MilvusVectorStore#doDelete(List) implementation is vulnerable to filter-expression injection via unsanitized document IDs. Spring AI 1.0.x: affected from 1.0.0 through latest 1.0.x; upgrade to 1.0.7 or greater. Spring AI 1.1.x: affected from 1.1.0 through latest 1.1.x; upgrade to 1.1.6 or…
more
greater.
- CWE(s)
AI Security AnalysisAI
- AI Category
- NLP and Transformers
- Risk Domain
- Data-Related Vulnerabilities
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.1.2V1.3.2V1.3.5V4.3.1
Mitigating Controls (NIST 800-53 r5) AI
Input validation directly requires checking and neutralizing special elements in externally influenced data before it is used to build executable statements such as EL expressions.
Security engineering principles include requirements for safe construction and sanitization of dynamic statements, structurally preventing expression-language injection at design time.
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 SDLC practices directly require input neutralization and safe EL construction to prevent injection 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 can detect EL injection but does not itself implement the fix.
Secure SDLC mandates input validation and output encoding that directly prevent expression-language injection.
Application security requirements explicitly call for controls against injection flaws including EL injection.
Secure architecture principles reduce the attack surface but do not prescribe the specific neutralization techniques needed.
Secure coding standards require proper escaping and parameterization of expression-language statements.