CVE-2026-22744
Vmware Spring Ai 1.0.0 – 1.0.5
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:NSummary
CVE-2026-22744 is a high-severity Injection (CWE-74) 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 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-2026-22744 is a vulnerability in the RedisFilterExpressionConverter component of spring-ai-redis-store, part of the Spring AI project. It occurs when a user-controlled string is passed as a filter value for a TAG field, as the stringValue() method inserts the value directly into the @field:{VALUE} RediSearch TAG block without escaping special characters. This flaw affects Spring AI versions from 1.0.0 before 1.0.5 and from 1.1.0 before 1.1.4. The issue has a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N), indicating high confidentiality impact with no requirements for privileges or user interaction.
Remote, unauthenticated attackers can exploit this vulnerability by supplying a malicious filter value for a TAG field in Redis queries processed through the affected component. Successful exploitation allows attackers to manipulate RediSearch TAG blocks, potentially leading to unauthorized disclosure of sensitive information stored in the Redis database.
The Spring security advisory at https://spring.io/security/cve-2026-22744 recommends upgrading to Spring AI version 1.0.5 or later for the 1.0.x branch, or 1.1.4 or later for the 1.1.x branch, where the input is properly escaped to prevent injection. No workarounds are specified in the available information.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-16541
Vulnerability Data
In RedisFilterExpressionConverter of spring-ai-redis-store, when a user-controlled string is passed as a filter value for a TAG field, stringValue() inserts the value directly into the @field:{VALUE} RediSearch TAG block without escaping characters.This issue affects Spring AI: from 1.0.0 before 1.0.5,…
more
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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V1.2.1V1.2.3V1.2.5V1.2.8
Mitigating Controls (NIST 800-53 r5) AI
SI-10 directly requires validation of information inputs to reject malformed or special-element content before it reaches downstream parsers.
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 validation and output encoding that 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 catches injection vulnerabilities before release.
Logging supports detection of injection attempts but does not prevent the weakness.
Monitoring activities can identify active injection attacks after they occur.
Secure development life cycle mandates input validation and output encoding that directly prevent injection flaws.
Application security requirements explicitly call for controls against injection attacks in software design.
Secure architecture principles reduce injection surfaces but do not prescribe specific neutralization techniques.