Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:NSummary
CVE-2026-22743 is a high-severity SQL Injection (CWE-89) vulnerability in Vmware Spring Ai. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 17th 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 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-2026-22743 is a Cypher injection vulnerability in the Neo4jVectorFilterExpressionConverter of Spring AI's spring-ai-neo4j-store module. The vulnerability arises when a user-controlled string is passed as a filter expression key; the doKey() method embeds this key into a backtick-delimited Cypher property accessor in the form node.`metadata.<key>`, stripping only double quotes without escaping embedded backticks. This flaw affects Spring AI versions from 1.0.0 before 1.0.5 and from 1.1.0 before 1.1.4.
An unauthenticated remote attacker can exploit this vulnerability over the network with low attack complexity and no user interaction required, as reflected in its CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N). By supplying a malicious filter expression key containing crafted backticks, the attacker can inject arbitrary Cypher queries, resulting in high-impact confidentiality loss through unauthorized access to sensitive data in the Neo4j database.
The official Spring security advisory at https://spring.io/security/cve-2026-22743 details mitigation steps, recommending upgrades to patched versions: Spring AI 1.0.5 or later for the 1.0.x series, and 1.1.4 or later for the 1.1.x series. This issue is classified under CWE-89 (Improper Neutralization of Special Elements used in an SQL Command).
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-16539
Vulnerability Data
Spring AI's spring-ai-neo4j-store contains a Cypher injection vulnerability in Neo4jVectorFilterExpressionConverter. When a user-controlled string is passed as a filter expression key in Neo4jVectorFilterExpressionConverter of spring-ai-neo4j-store, doKey() embeds the key into a backtick-delimited Cypher property accessor (node.`metadata.`) after stripping only double…
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quotes, without escaping embedded backticks.This issue affects Spring AI: from 1.0.0 before 1.0.5, from 1.1.0 before 1.1.4.
- 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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V6.2.5
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover SQLi flaws before deployment but does not stop their introduction.
Input validation directly stops untrusted data from reaching SQL query construction without neutralization.
Secure engineering principles require parameterized queries and input sanitization that structurally eliminate SQLi.
System monitoring can identify attempted SQLi exploitation via anomalous queries after the weakness exists.
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 target injection flaws during coding and review so largely prevent CWE-89 introduction, yet the single broad outcome leaves residual risk from incomplete neutralization techniques or missed edge cases.
Training raises developer awareness of SQLi risks and can reduce introduction likelihood (partial) but removes none of the actual coding flaw's risk by itself since technical neutralization is still required.
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.
The same secure-coding and static-analysis activities surface missing neutralization of SQL metacharacters before the system is accepted.
Early warnings and shared best-practice information help organizations apply the latest remediation techniques against SQL-injection vulnerabilities.
Threat-intelligence feeds that surface new SQL-injection campaigns enable rapid updates to query-construction defenses and detection signatures before exploitation occurs.
Secure-coding rules and security testing phases mandate the use of parameterized queries or equivalent escaping, preventing the construction of dynamic SQL statements from untrusted input.
Language-specific secure coding rules, peer review and SAST together prevent the construction of SQL statements from untrusted data without proper parameterization or escaping.