Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:L/A:LSummary
CVE-2026-40967 is a high-severity Code Injection (CWE-94) vulnerability in Vmware Spring Ai. Its CVSS base score is 8.6 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked at the 32th 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 LLM/Generative AI Risks risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — 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-2026-40967 is a code injection vulnerability (CWE-94) in Spring AI's FilterExpressionConverter implementations, which accept filter expression objects and translate them into specific vector store query languages. In affected versions, keys and values are not properly escaped, enabling attackers to alter the resulting queries. The vulnerability impacts Spring AI versions 1.0.0 through 1.0.5 and 1.1.0 through 1.1.4, with a CVSS v3.1 base score of 8.6 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:L/A:L).
Unauthenticated attackers can exploit this vulnerability remotely with low complexity by submitting malicious filter expressions through application interfaces that use the affected converters. Successful exploitation allows query alteration in vector stores, potentially leading to high confidentiality impacts such as unauthorized data access, alongside low integrity and availability effects.
The Spring security advisory at https://spring.io/security/cve-2026-40967 details the issue and confirms fixes in Spring AI 1.0.6 and 1.1.5, recommending immediate upgrades for affected deployments.
This vulnerability is particularly relevant in AI/ML contexts, as Spring AI integrates with vector stores commonly used for embedding-based retrieval and generation workflows. No public evidence of real-world exploitation has been reported.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-25994
Vulnerability Data
In Spring AI, various FilterExpressionConverter implementations accept a filter expression object and translate them to specific vector store query languages. In several cases, keys and values are not properly escaped, leading to the ability to alter the query. Affected versions:…
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Spring AI: 1.0.0 - 1.0.5 (fixed in 1.0.6), 1.1.0 - 1.1.4 (fixed in 1.1.5)
- CWE(s)
AI Security AnalysisAI
- AI Category
- NLP and Transformers
- Risk Domain
- LLM/Generative AI Risks
- 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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V1.3.1
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation finds code paths that accept and execute externally influenced strings.
Input validation directly stops untrusted data from being used to construct executable code without neutralization.
Least privilege limits the damage an injected code fragment can perform once executed.
Requiring documented secure development standards and tools enforces use of safe code-generation APIs and escaping.
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.
PR.PS-06's SDLC practices directly target injection flaws via secure coding and testing (mostly), yet as a single broad outcome it leaves many code-generation specifics unaddressed (partial).
PR.DS-10 protects runtime data confidentiality/integrity but has no bearing on neutralizing externally influenced input during code generation, so neither direction shows any preventive effect.
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.
Banning unapproved code samples and unauthenticated web services, combined with secure-coding standards and SAST, prevents the dynamic generation or inclusion of attacker-supplied code.
Controls that restrict unauthorized or malicious code from being introduced via external networks or removable media limit opportunities for an attacker to inject and execute arbitrary code.