Description
Ormar is a async mini ORM for Python. In versions 0.9.9 through 0.22.0, when performing aggregate queries, Ormar ORM constructs SQL expressions by passing user-supplied column names directly into `sqlalchemy.text()` without any validation or sanitization. The `min()` and `max()` methods in the `QuerySet` class accept arbitrary string input as the column parameter. While `sum()` and `avg()` are partially protected by an `is_numeric` type check that rejects non-existent fields, `min()` and `max()` skip this validation entirely. As a result, an attacker-controlled string is embedded as raw SQL inside the aggregate function call. Any unauthorized user can exploit this vulnerability to read the entire database contents, including tables unrelated to the queried model, by injecting a subquery as the column parameter. Version 0.23.0 contains a patch.
EPSS Score:
0%
Comprehensive Technical Analysis of EUVD-2026-7409
1. Vulnerability Assessment and Severity Evaluation
The vulnerability identified in the EUVD entry EUVD-2026-7409 affects the Ormar async mini ORM for Python. Specifically, versions 0.9.9 through 0.22.0 are susceptible to SQL injection attacks due to insufficient validation and sanitization of user-supplied column names in aggregate queries. The min() and max() methods in the QuerySet class are particularly vulnerable because they accept arbitrary string input without proper validation.
Severity Evaluation:
- Base Score: 9.8
- Base Score Version: CVSS:3.1
- Base Score Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
The high base score of 9.8 indicates a critical vulnerability. The CVSS vector breakdown shows that the vulnerability can be exploited remotely (AV:N), requires low complexity (AC:L), does not need privileges (PR:N) or user interaction (UI:N), and has a high impact on confidentiality, integrity, and availability (C:H/I:H/A:H).
2. Potential Attack Vectors and Exploitation Methods
Attack Vectors:
- SQL Injection: An attacker can inject malicious SQL code by supplying crafted column names to the
min()andmax()methods. This can lead to unauthorized data access, modification, or deletion. - Data Exfiltration: By injecting subqueries, an attacker can read the entire database contents, including tables unrelated to the queried model.
Exploitation Methods:
- Direct SQL Injection: An attacker can craft a column name that includes SQL commands, which are then executed by the database.
- Subquery Injection: An attacker can inject subqueries to extract data from other tables or perform unauthorized operations.
3. Affected Systems and Software Versions
Affected Software:
- Ormar async mini ORM for Python
Affected Versions:
- 0.9.9 through 0.22.0
Patched Version:
- 0.23.0
4. Recommended Mitigation Strategies
Immediate Actions:
- Upgrade: Upgrade to Ormar version 0.23.0 or later, which includes a patch for this vulnerability.
- Input Validation: Implement additional input validation and sanitization for all user-supplied data, especially column names.
- Parameterized Queries: Use parameterized queries to prevent SQL injection.
Long-Term Strategies:
- Regular Audits: Conduct regular security audits and code reviews to identify and mitigate similar vulnerabilities.
- Security Training: Provide security training for developers to ensure they are aware of common vulnerabilities and best practices.
- Monitoring: Implement monitoring and logging to detect and respond to suspicious activities.
5. Impact on European Cybersecurity Landscape
The vulnerability in Ormar poses a significant risk to organizations using this ORM in their applications. Given the widespread use of Python and ORMs in web applications, this vulnerability could affect a broad range of industries, including finance, healthcare, and e-commerce. The potential for data breaches and unauthorized access highlights the need for robust security measures and continuous monitoring.
6. Technical Details for Security Professionals
Vulnerability Details:
- The
min()andmax()methods in theQuerySetclass accept arbitrary string input as the column parameter without proper validation. - The
sum()andavg()methods have partial protection with anis_numerictype check, but this is insufficient for comprehensive security. - The vulnerability allows an attacker to inject raw SQL into the aggregate function call, leading to SQL injection attacks.
Exploitation Example:
# Vulnerable code
result = QuerySet.min('user_input')
# Exploitation
user_input = "1; DROP TABLE users;"
result = QuerySet.min(user_input)
Mitigation Code Example:
# Patched code with input validation
def validate_column_name(column_name):
# Implement a robust validation mechanism
if not re.match(r'^[a-zA-Z_][a-zA-Z0-9_]*$', column_name):
raise ValueError("Invalid column name")
# Usage
column_name = 'user_input'
validate_column_name(column_name)
result = QuerySet.min(column_name)
References:
By addressing this vulnerability promptly and implementing robust security measures, organizations can mitigate the risk of SQL injection attacks and protect their data integrity.