Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:N/A:NSummary
CVE-2026-22742 is a high-severity SSRF (CWE-918) 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 LLM/Generative AI Risks risk domain.
The strongest mitigations our analysis identified map to AC-4 (Information Flow Enforcement) 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-2026-22742 is a Server-Side Request Forgery (SSRF) vulnerability, classified under CWE-918, in the BedrockProxyChatModel component of Spring AI's spring-ai-bedrock-converse module. The flaw arises from insufficient validation of user-supplied media URLs in multimodal messages, enabling the server to issue HTTP requests to unintended internal or external destinations. It affects Spring AI versions from 1.0.0 before 1.0.5 and from 1.1.0 before 1.1.4, with a CVSS v3.1 base score of 8.6 (AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:N/A:N), indicating high severity due to its network accessibility, low complexity, lack of required privileges or user interaction, cross-scope impact, and high confidentiality consequences.
Remote, unauthenticated attackers can exploit this vulnerability by submitting multimodal messages containing malicious media URLs to a vulnerable Spring AI application. Successful exploitation induces the server to make unauthorized HTTP requests on the attacker's behalf, potentially allowing access to internal network resources, metadata services, or external endpoints that the server can reach but users cannot. While integrity and availability are not directly impacted, the high confidentiality score reflects risks such as data exfiltration from behind firewalls or cloud metadata exposure.
The official Spring security advisory at https://spring.io/security/cve-2026-22742 details the issue and recommends upgrading to Spring AI 1.0.5 or later for the 1.0.x branch, or 1.1.4 or later for the 1.1.x branch, as these versions include fixes for URL validation.
This vulnerability is relevant to AI/ML practitioners using Spring AI integrations with AWS Bedrock for conversational models, highlighting risks in proxying user-supplied content in multimodal AI workflows. No public reports of real-world exploitation were available as of the CVE publication on 2026-03-27.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-16537
Vulnerability Data
Spring AI's spring-ai-bedrock-converse contains a Server-Side Request Forgery (SSRF) vulnerability in BedrockProxyChatModel when processing multimodal messages that include user-supplied media URLs. Insufficient validation of those URLs allows an attacker to induce the server to issue HTTP requests to unintended internal…
more
or external destinations. 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
- 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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V1.3.6V1.5.3V5.3.2V10.4.7
Mitigating Controls (NIST 800-53 r5) AI
Information flow enforcement can restrict which destinations the server is allowed to contact on behalf of users.
Input validation directly stops untrusted URLs from being accepted and fetched without destination checks.
Boundary protection limits the network reach of server-initiated requests even if SSRF occurs.
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 directly include input validation and destination allow-listing that prevent SSRF.
Runtime monitoring of web applications and services can detect anomalous outbound requests indicative of SSRF.
Vulnerability identification processes can discover and record SSRF flaws in web applications.
Network segmentation and egress controls can limit the damage from successful SSRF requests.
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.
Operational threat data describing SSRF campaigns can be used to tighten outbound-request allow-lists and detection rules before attackers exploit them.