CVE-2025-1793
📋 TL;DR
SQL injection vulnerabilities in multiple vector store integrations of run-llama/llama_index v0.12.21 allow attackers to execute arbitrary SQL commands. This can lead to unauthorized data access, modification, or deletion in applications using the vulnerable library. Web applications incorporating llama_index for AI/ML functionality are primarily affected.
💻 Affected Systems
- run-llama/llama_index
📦 What is this software?
Llamaindex by Llamaindex
⚠️ Risk & Real-World Impact
Worst Case
Complete compromise of backend databases, data exfiltration, privilege escalation, and potential remote code execution depending on database configuration.
Likely Case
Unauthorized access to sensitive vector store data, data manipulation, and potential exposure of user information in multi-tenant applications.
If Mitigated
Limited impact with proper input validation, parameterized queries, and database permission restrictions in place.
🎯 Exploit Status
SQL injection vulnerabilities are well-understood with many available exploitation tools. The huntr.com bounty references indicate active research.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: Versions after commit 0008041e8dde8e519621388e5d6f558bde6ef42e
Vendor Advisory: https://github.com/run-llama/llama_index/commit/0008041e8dde8e519621388e5d6f558bde6ef42e
Restart Required: No
Instructions:
1. Update llama_index to latest version. 2. Review commit 0008041e8dde8e519621388e5d6f558bde6ef42e for specific fixes. 3. Test vector store functionality after update.
🔧 Temporary Workarounds
Input Validation and Sanitization
allImplement strict input validation and sanitization for all user inputs passed to vector store functions.
Database Permission Restrictions
allLimit database user permissions to minimum required operations (read-only where possible).
🧯 If You Can't Patch
- Implement web application firewall (WAF) rules to detect and block SQL injection patterns
- Isolate vector store databases from other critical systems and implement network segmentation
🔍 How to Verify
Check if Vulnerable:
Check if your application uses llama_index version v0.12.21 or earlier and utilizes vector store integrations.Check Version:
pip show llama-index | grep VersionVerify Fix Applied:
Update to latest version and test vector store operations with malicious inputs to ensure SQL injection is prevented.📡 Detection & Monitoring
Log Indicators:
- Unusual SQL queries in database logs
- Multiple failed login attempts via vector store endpoints
- Unexpected database schema changes
Network Indicators:
- SQL syntax in HTTP parameters to vector store endpoints
- Unusual database connection patterns from application servers
SIEM Query:
source="database_logs" AND (query="UNION" OR query="SELECT *" OR query="DROP" OR query="INSERT") AND source_ip="application_server"