CVE-2026-4229: Vanna-AI Vanna SQL Injection Vulnerability

CVE-2026-4229 Overview

A SQL injection vulnerability has been identified in vanna-ai vanna, an AI-powered SQL generation library, affecting versions up to 2.0.2. The flaw exists in the remove_training_data function within the file src/vanna/legacy/google/bigquery_vector.py. By manipulating the ID argument, an attacker can inject malicious SQL commands that execute against the underlying BigQuery database. This vulnerability can be exploited remotely without authentication, making it particularly dangerous for organizations using vanna for data analytics and AI-powered query generation.

Critical Impact

Remote attackers can exploit this SQL injection vulnerability to read, modify, or delete training data in BigQuery vector stores, potentially compromising AI model integrity and sensitive data.

Affected Products

• vanna-ai vanna versions up to 2.0.2
• BigQuery Vector integration module (bigquery_vector.py)
• Legacy Google BigQuery connectors in vanna

Discovery Timeline

• 2026-03-16 - CVE-2026-4229 published to NVD
• 2026-03-16 - Last updated in NVD database

Technical Details for CVE-2026-4229

Vulnerability Analysis

This vulnerability is classified as CWE-74 (Improper Neutralization of Special Elements in Output Used by a Downstream Component), commonly known as Injection. The vulnerable code path resides in the remove_training_data function, which accepts an ID parameter that is directly incorporated into SQL queries without proper sanitization or parameterized query handling.

The function is designed to remove training data entries from the BigQuery vector store based on a provided identifier. However, the implementation fails to properly validate or escape the ID input before constructing the database query. This allows an attacker to craft malicious input that breaks out of the intended query structure and executes arbitrary SQL commands.

Since vanna is commonly used to generate SQL queries from natural language, compromising the training data can have cascading effects on the quality and security of generated queries. An attacker could manipulate training data to influence the AI model's behavior or extract sensitive information stored in the vector database.

Root Cause

The root cause of this vulnerability is improper input validation in the remove_training_data function located in src/vanna/legacy/google/bigquery_vector.py. The ID parameter is directly concatenated or interpolated into SQL query strings rather than using parameterized queries or prepared statements. This fundamental violation of secure coding practices allows user-controlled input to be interpreted as SQL commands.

The legacy nature of this code path (indicated by the /legacy/ directory structure) suggests that older implementation patterns were used that do not incorporate modern security best practices for database interaction.

Attack Vector

The attack can be initiated remotely over the network without requiring authentication. An attacker needs to identify an application endpoint that exposes the remove_training_data functionality and accepts user-supplied ID values. By crafting a malicious ID parameter containing SQL metacharacters and injection payloads, the attacker can:

1. Extract sensitive data from the BigQuery database through UNION-based or blind SQL injection techniques
2. Modify or delete training data entries, corrupting the AI model's knowledge base
3. Potentially escalate access depending on the database permissions and BigQuery configuration

The proof-of-concept exploit has been publicly disclosed, as referenced in the GitHub PoC Repository, increasing the urgency of remediation.

Detection Methods for CVE-2026-4229

Indicators of Compromise

• Unusual or malformed ID parameters in requests to vanna training data management endpoints
• SQL syntax errors or unexpected query behavior in BigQuery logs
• Anomalous data modifications or deletions in vanna training datasets
• Increased query execution time indicative of time-based blind SQL injection attempts

Detection Strategies

• Monitor application logs for requests containing SQL metacharacters (single quotes, semicolons, comment sequences) in ID parameters
• Implement Web Application Firewall (WAF) rules to detect and block common SQL injection patterns
• Review BigQuery audit logs for unauthorized data access or manipulation queries
• Deploy runtime application self-protection (RASP) to detect injection attempts at the application layer

Monitoring Recommendations

• Enable detailed logging for all vanna training data management operations
• Configure alerts for failed or anomalous database queries originating from the BigQuery vector module
• Establish baseline behavior for training data management operations and alert on deviations
• Monitor for any public disclosure of additional exploitation techniques related to this vulnerability

How to Mitigate CVE-2026-4229

Immediate Actions Required

• Audit all instances of vanna deployments to identify usage of versions 2.0.2 or earlier
• Restrict network access to vanna training data management endpoints to trusted sources only
• Implement input validation at the application layer to reject ID parameters containing SQL metacharacters
• Review and revoke unnecessary BigQuery permissions to limit potential impact of exploitation

Patch Information

At the time of disclosure, the vendor (vanna-ai) was contacted but did not respond. Users should monitor the official vanna-ai repository for security updates and patches. Consider upgrading to the latest version when a fix becomes available.

For tracking this vulnerability, refer to VulDB #351152 and the VulDB CTI entry for updates.

Workarounds

• Implement a wrapper function that validates and sanitizes ID parameters before passing them to remove_training_data
• Replace direct function calls with parameterized query implementations at the application integration layer
• Deploy network-level controls such as IP allowlisting to restrict access to training data management functionality
• Consider temporarily disabling the BigQuery vector legacy module if not actively required

# Example input validation wrapper (conceptual)
# Validate ID contains only alphanumeric characters before processing
validate_id() {
    if [[ ! "$1" =~ ^[a-zA-Z0-9_-]+$ ]]; then
        echo "Invalid ID format detected - rejecting request"
        exit 1
    fi
}