CVE-2025-1793: Llamaindex SQL Injection Vulnerability

CVE-2025-1793 Overview

CVE-2025-1793 is a critical SQL injection vulnerability affecting multiple vector store integrations in the run-llama/llama_index library version v0.12.21. This vulnerability allows attackers to read and write data using SQL commands, potentially leading to unauthorized access to data belonging to other users when the llama-index library is integrated into web applications.

Critical Impact

Attackers can exploit this SQL injection vulnerability to bypass authentication, extract sensitive data, modify database contents, and potentially gain unauthorized access to other users' data in applications built with LlamaIndex vector store integrations.

Affected Products

• LlamaIndex version v0.12.21 and earlier
• Multiple vector store integrations within the llama_index library
• Web applications utilizing vulnerable LlamaIndex vector store components

Discovery Timeline

• 2025-06-05 - CVE-2025-1793 published to NVD
• 2025-07-30 - Last updated in NVD database

Technical Details for CVE-2025-1793

Vulnerability Analysis

This SQL injection vulnerability (CWE-89) exists within multiple vector store integrations of the LlamaIndex library. The vulnerability stems from improper neutralization of special elements used in SQL commands. When user-supplied input is incorporated into SQL queries without adequate sanitization or parameterization, attackers can inject malicious SQL code that gets executed by the underlying database engine.

In the context of LlamaIndex, vector stores are critical components used for storing and retrieving embeddings in AI/ML applications. When these vector stores interface with SQL databases and fail to properly sanitize inputs, the entire data layer becomes vulnerable to injection attacks.

Root Cause

The root cause of CVE-2025-1793 is improper input validation and insufficient sanitization of user-supplied data before it is incorporated into SQL queries within the vector store integration components. The vulnerable code constructs SQL statements by directly concatenating or interpolating user input, rather than using parameterized queries or prepared statements that would treat all input as literal data values.

Attack Vector

The vulnerability is exploitable over the network without requiring authentication or user interaction. An attacker can craft malicious input containing SQL syntax that, when processed by vulnerable vector store integrations, executes arbitrary SQL commands against the backend database. This attack vector is particularly dangerous in web applications where LlamaIndex processes user-supplied queries or data, as it could allow:

• Extraction of sensitive data from the database
• Modification or deletion of existing records
• Bypassing application logic and access controls
• Potential lateral movement to access data belonging to other users

The patch in version 0.12.28 addresses these vulnerabilities, as shown in the following code changes:

"""Init file of LlamaIndex."""

-__version__ = "0.12.27"
+__version__ = "0.12.28"

import logging
from logging import NullHandler

Source: GitHub Commit Details

Detection Methods for CVE-2025-1793

Indicators of Compromise

• Unusual SQL error messages in application logs indicating syntax errors from malformed queries
• Database audit logs showing unexpected UNION SELECT, OR 1=1, or similar SQL injection patterns
• Abnormal data access patterns accessing records across user boundaries
• Unexpected database modifications or deletions without corresponding legitimate application activity

Detection Strategies

• Implement Web Application Firewall (WAF) rules to detect and block common SQL injection payloads targeting LlamaIndex endpoints
• Monitor application logs for SQL syntax errors or database exceptions that may indicate injection attempts
• Enable database query logging and analyze for anomalous query patterns or unauthorized data access
• Deploy runtime application self-protection (RASP) solutions to detect SQL injection attempts in real-time

Monitoring Recommendations

• Configure alerting on database audit logs for queries containing SQL injection signatures
• Monitor for unusual API request patterns to vector store endpoints, particularly those with special characters or SQL keywords
• Implement anomaly detection for database query execution times and result set sizes
• Track and alert on any authentication bypass attempts or unauthorized cross-user data access

How to Mitigate CVE-2025-1793

Immediate Actions Required

• Upgrade LlamaIndex to version 0.12.28 or later immediately
• Audit all applications using LlamaIndex vector store integrations for potential exposure
• Review database access logs for signs of exploitation prior to patching
• Implement input validation at the application layer as a defense-in-depth measure

Patch Information

LlamaIndex has released a security patch addressing this vulnerability in version 0.12.28. The fix is available in commit 0008041e8dde8e519621388e5d6f558bde6ef42e. Organizations should upgrade to the patched version immediately using their package manager:

pip install --upgrade llama-index>=0.12.28

For additional technical details, refer to the Huntr Bounty Listing and the GitHub Commit Details.

Workarounds

• Implement strict input validation and sanitization for all user-supplied data before passing to LlamaIndex components
• Deploy a Web Application Firewall configured to block SQL injection attack patterns
• Use database accounts with minimal required privileges to limit the impact of successful exploitation
• Consider network segmentation to isolate database servers from direct internet access

# Configuration example - Upgrade LlamaIndex to patched version
pip install --upgrade llama-index>=0.12.28

# Verify installed version
pip show llama-index | grep Version