CVE-2025-24793: Snowflake Connector Python SQLi Flaw

CVE-2025-24793 Overview

A SQL injection vulnerability exists in the Snowflake Connector for Python, specifically within a function in the snowflake.connector.pandas_tools module. This vulnerability allows attackers with local access to inject malicious SQL commands through the affected interface, potentially leading to unauthorized data access, modification, or execution of arbitrary SQL operations against connected Snowflake databases.

Critical Impact

Applications using the vulnerable pandas_tools module function may be susceptible to SQL injection attacks, enabling unauthorized database operations including data exfiltration, modification, or deletion within Snowflake environments.

Affected Products

• Snowflake Connector for Python versions 2.2.5 through 3.13.0
• Python applications utilizing the snowflake.connector.pandas_tools module
• Data pipelines and ETL processes leveraging the affected connector versions

Discovery Timeline

• January 29, 2025 - CVE-2025-24793 published to NVD
• August 25, 2025 - Last updated in NVD database

Technical Details for CVE-2025-24793

Vulnerability Analysis

This SQL injection vulnerability (CWE-89) exists within the snowflake.connector.pandas_tools module of the Snowflake Connector for Python. The flaw stems from improper neutralization of special elements used in SQL commands, allowing user-controlled input to be incorporated into SQL queries without adequate sanitization or parameterization.

The vulnerability requires local access and low privileges to exploit, though the attack complexity is high. Successful exploitation can result in complete compromise of confidentiality, integrity, and availability of data accessible through the affected Snowflake connection.

Root Cause

The root cause is improper input validation in the pandas_tools module, where user-supplied data is not properly sanitized before being incorporated into SQL statements. This allows specially crafted input to escape the intended SQL context and inject arbitrary SQL commands.

Attack Vector

The attack requires local access to the system running the vulnerable Python application. An attacker with low-level privileges can exploit this vulnerability by supplying malicious input to functions within the pandas_tools module that construct SQL queries. The injected SQL commands execute with the same database permissions as the application's Snowflake connection.

The security patch introduces a _force_qmark_paramstyle parameter to enforce parameterized queries:

         _skip_upload_on_content_match: bool = False,
         file_stream: IO[bytes] | None = None,
         num_statements: int | None = None,
+        _force_qmark_paramstyle: bool = False,
         _dataframe_ast: str | None = None,
     ) -> Self | dict[str, Any] | None:
         """Executes a command/query.

Source: GitHub Commit Change

Detection Methods for CVE-2025-24793

Indicators of Compromise

• Unusual or malformed SQL queries in Snowflake audit logs originating from Python connector sessions
• Unexpected data access patterns or bulk data extraction from Snowflake databases
• Error logs showing SQL syntax errors that may indicate failed injection attempts
• Anomalous database operations performed outside normal application behavior

Detection Strategies

• Monitor Snowflake query history for suspicious SQL patterns including UNION-based injections, stacked queries, or comment sequences
• Implement application-level logging to capture all inputs passed to pandas_tools functions
• Use SentinelOne Singularity to detect anomalous process behavior associated with Python applications accessing Snowflake
• Review dependency manifests to identify applications using vulnerable connector versions (2.2.5 through 3.13.0)

Monitoring Recommendations

• Enable comprehensive Snowflake query logging and regularly audit for injection patterns
• Configure alerts for database operations that exceed normal application scope or access unusual tables
• Monitor Python application processes for unexpected network connections or data transfers
• Implement real-time monitoring of Snowflake connector activity using SIEM integration

How to Mitigate CVE-2025-24793

Immediate Actions Required

• Upgrade Snowflake Connector for Python to version 3.13.1 or later immediately
• Audit all applications using the snowflake.connector.pandas_tools module to identify potential exposure
• Review Snowflake audit logs for any suspicious activity that may indicate prior exploitation
• Implement input validation at the application layer as an additional defense-in-depth measure

Patch Information

Snowflake has released version 3.13.1 which addresses this SQL injection vulnerability. The fix is available through the official GitHub Security Advisory. Organizations should update their Python dependencies immediately.

The patch commit is available at: GitHub Commit Change

Workarounds

• If immediate upgrade is not possible, implement strict input validation before passing data to pandas_tools functions
• Restrict database user permissions to minimum required privileges to limit potential impact
• Consider temporarily disabling functionality that relies on the vulnerable pandas_tools module until patching is complete
• Deploy web application firewalls or input filtering at the application boundary to detect SQL injection patterns

# Upgrade Snowflake Connector for Python to patched version
pip install --upgrade snowflake-connector-python>=3.13.1

# Verify installed version
pip show snowflake-connector-python | grep Version

# For requirements.txt, update the dependency
# snowflake-connector-python>=3.13.1