CVE-2026-40978 Overview
A SQL injection vulnerability exists in Spring AI's CosmosDBVectorStore component that allows attackers to execute arbitrary SQL queries through crafted document IDs. This vulnerability affects applications using Spring AI's vector store functionality with Azure Cosmos DB as a backend, enabling malicious actors with low privileges to compromise data confidentiality, integrity, and availability.
Critical Impact
Attackers can exploit this SQL injection vulnerability to execute arbitrary database queries, potentially leading to unauthorized data access, data modification, or complete database compromise in applications using Spring AI with CosmosDB.
Affected Products
- VMware Spring AI 1.0.0 through 1.0.5 (fixed in 1.0.6)
- VMware Spring AI 1.1.0 through 1.1.4 (fixed in 1.1.5)
Discovery Timeline
- 2026-04-28 - CVE-2026-40978 published to NVD
- 2026-04-29 - Last updated in NVD database
Technical Details for CVE-2026-40978
Vulnerability Analysis
This SQL injection vulnerability (CWE-89) resides in the CosmosDBVectorStore component of Spring AI. The vulnerability occurs due to improper neutralization of special elements used in SQL commands when processing document IDs. When an application passes user-controlled document IDs to the vector store operations, these values are incorporated into SQL queries without adequate sanitization or parameterization.
The vulnerability is exploitable over the network and requires low privileges to execute. No user interaction is needed for successful exploitation. A successful attack can result in complete compromise of data confidentiality, integrity, and availability within the affected Cosmos DB instance.
Root Cause
The root cause of this vulnerability is insufficient input validation and improper construction of SQL queries in the CosmosDBVectorStore class. Document ID parameters are concatenated directly into SQL query strings rather than being properly parameterized, allowing attackers to inject malicious SQL fragments that alter the intended query logic.
Attack Vector
The attack vector involves supplying specially crafted document IDs to any Spring AI operation that interacts with the CosmosDBVectorStore. An attacker with the ability to influence document ID values can inject SQL syntax that escapes the intended query context, enabling them to:
- Extract sensitive data from the database through UNION-based or error-based injection techniques
- Modify or delete existing records by injecting UPDATE or DELETE statements
- Escalate privileges within the database context
- Potentially execute administrative operations depending on database permissions
The vulnerability is accessible over the network with low attack complexity. Authentication is required but only minimal privileges are needed to exploit this flaw.
Detection Methods for CVE-2026-40978
Indicators of Compromise
- Unusual SQL query patterns in Cosmos DB audit logs containing injection payloads such as single quotes, UNION statements, or SQL comments
- Unexpected database errors or exceptions related to malformed queries in application logs
- Abnormal data access patterns or bulk data retrieval from the vector store
- Application errors indicating SQL syntax violations in CosmosDBVectorStore operations
Detection Strategies
- Implement application-layer monitoring to detect SQL injection patterns in document ID parameters
- Enable and analyze Azure Cosmos DB diagnostic logs for anomalous query patterns
- Deploy Web Application Firewall (WAF) rules to detect common SQL injection signatures in API requests
- Monitor for unusual error rates in Spring AI vector store operations
Monitoring Recommendations
- Configure alerting on Azure Cosmos DB query performance metrics for unusual patterns
- Implement logging at the application layer to capture all document ID inputs before processing
- Establish baseline query patterns and alert on deviations
- Monitor authentication and authorization logs for attempts to access data outside normal scope
How to Mitigate CVE-2026-40978
Immediate Actions Required
- Upgrade Spring AI to version 1.0.6 or later (for 1.0.x branch) or 1.1.5 or later (for 1.1.x branch)
- Implement input validation to reject document IDs containing SQL metacharacters
- Review application code for any custom document ID handling that may expose this vulnerability
- Audit database access logs for evidence of past exploitation attempts
Patch Information
VMware has released patched versions that address this SQL injection vulnerability. Organizations should upgrade to Spring AI 1.0.6 or 1.1.5 depending on their current version branch. The patch implements proper parameterization of document ID values in SQL queries to prevent injection attacks.
For detailed patch information, refer to the Spring Security Advisory for CVE-2026-40978.
Workarounds
- Implement strict input validation on document IDs before passing them to CosmosDBVectorStore operations, rejecting any values containing SQL special characters
- Deploy a Web Application Firewall with SQL injection detection rules to filter malicious requests
- Apply principle of least privilege to database accounts used by Spring AI to limit potential impact
- Consider implementing a document ID whitelist or format validation layer as a defense-in-depth measure
# Example: Update Spring AI dependency in Maven pom.xml
# For 1.0.x branch users:
# Change spring-ai version from 1.0.x to 1.0.6
# For 1.1.x branch users:
# Change spring-ai version from 1.1.x to 1.1.5
# Verify installed version after update
mvn dependency:tree | grep spring-ai
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
