Django SQLi via order_by alias before 6.0.2/5.2.11/4.2.28
CVE-2026-1312 Published on February 3, 2026

Potential SQL injection via QuerySet.order_by and FilteredRelation
An issue was discovered in 6.0 before 6.0.2, 5.2 before 5.2.11, and 4.2 before 4.2.28. `.QuerySet.order_by()` is subject to SQL injection in column aliases containing periods when the same alias is, using a suitably crafted dictionary, with dictionary expansion, used in `FilteredRelation`. Earlier, unsupported Django series (such as 5.0.x, 4.1.x, and 3.2.x) were not evaluated and may also be affected. Django would like to thank Solomon Kebede for reporting this issue.

Vendor Advisory Vendor Advisory NVD

Vulnerability Analysis

CVE-2026-1312 is exploitable with network access, and requires small amount of user privileges. This vulnerability is considered to have a low attack complexity. The potential impact of an exploit of this vulnerability is considered to have a small impact on confidentiality and integrity, and no impact on availability.

Attack Vector:
NETWORK
Attack Complexity:
LOW
Privileges Required:
LOW
User Interaction:
NONE
Scope:
UNCHANGED
Confidentiality Impact:
LOW
Integrity Impact:
LOW
Availability Impact:
NONE

Timeline

Initial report received.

Vulnerability confirmed. 14 days later.

Security release issued. 8 days later.

Weakness Type

What is a SQL Injection Vulnerability?

The software constructs all or part of an SQL command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended SQL command when it is sent to a downstream component.

CVE-2026-1312 has been classified to as a SQL Injection vulnerability or weakness.


Products Associated with CVE-2026-1312

stack.watch emails you whenever new vulnerabilities are published in Django Project Django or Canonical Ubuntu Linux. Just hit a watch button to start following.

 
 

Affected Versions

djangoproject Django:

Exploit Probability

EPSS
0.01%
Percentile
1.09%

EPSS (Exploit Prediction Scoring System) scores estimate the probability that a vulnerability will be exploited in the wild within the next 30 days. The percentile shows you how this score compares to all other vulnerabilities.