CVE-2026-22742 is a Server-Side Request Forgery (SSRF) vulnerability identified in the spring-ai-bedrock-converse module of Spring AI, specifically within the BedrockProxyChatModel component. This vulnerability occurs when the system processes multimodal messages containing user-supplied media URLs without adequate validation or sanitization. Due to this insufficient validation, an attacker can craft malicious URLs that cause the server to initiate HTTP requests to arbitrary destinations, including internal network resources or external systems that should not be accessible. This SSRF flaw can be exploited remotely without requiring authentication or user interaction, increasing its risk profile. The affected versions include Spring AI from 1.0.0 up to but not including 1.0.5, and from 1.1.0 up to but not including 1.1.4. The vulnerability’s CVSS v3.1 base score is 8.6, reflecting high severity, with an attack vector of network (AV:N), low attack complexity (AC:L), no privileges required (PR:N), no user interaction (UI:N), and a scope change (S:C). The impact primarily affects confidentiality by potentially exposing internal services or sensitive data accessible only from the server’s network context. Integrity and availability impacts are not indicated. No public exploit code or active exploitation has been reported to date. The vulnerability was reserved in January 2026 and published in March 2026. The lack of patch links suggests that fixes may be pending or available through vendor advisories. This vulnerability is critical for organizations deploying Spring AI in environments where internal network segmentation and access control are vital, as SSRF can be a stepping stone for further internal reconnaissance or attacks.

The SSRF vulnerability in Spring AI can have significant impacts on organizations worldwide. By exploiting this flaw, attackers can coerce the vulnerable server to send HTTP requests to internal systems that are otherwise inaccessible externally, potentially exposing sensitive internal services, metadata endpoints, or administrative interfaces. This can lead to unauthorized data disclosure, internal network reconnaissance, and could serve as a pivot point for more advanced attacks such as lateral movement or privilege escalation within the network. Since the vulnerability does not require authentication or user interaction, it can be exploited remotely by unauthenticated attackers, increasing the attack surface. Organizations relying on Spring AI for AI-driven applications, especially those processing user-generated content with media URLs, are at risk. The confidentiality of internal resources is the primary concern, while integrity and availability impacts are minimal or not directly affected. The vulnerability’s exploitation could also undermine trust in AI services and cause operational disruptions if internal systems are compromised or exposed. Given the widespread adoption of Spring frameworks and AI services, the potential impact is broad, affecting sectors such as technology, finance, healthcare, and government agencies that deploy Spring AI components.

To mitigate CVE-2026-22742, organizations should first apply vendor-provided patches or updates as soon as they become available for Spring AI versions prior to 1.0.5 and 1.1.4. In the absence of patches, implement strict input validation and sanitization on all user-supplied URLs within multimodal messages to ensure they do not point to internal IP ranges, localhost addresses, or other sensitive network segments. Employ allowlisting of permitted external domains and reject or block requests to private IP ranges (e.g., 10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16) and localhost (127.0.0.1). Network-level controls such as egress filtering and firewall rules should restrict the server’s ability to initiate outbound HTTP requests to only trusted destinations. Deploy Web Application Firewalls (WAFs) with custom rules to detect and block SSRF attack patterns targeting the affected endpoints. Monitor logs for unusual outbound HTTP requests initiated by the Spring AI service, especially those targeting internal IP addresses or unexpected external hosts. Conduct regular security assessments and penetration testing focusing on SSRF vectors in AI and web application components. Finally, educate developers and administrators about SSRF risks and secure coding practices to prevent similar vulnerabilities in future releases.