GHSA-c732-xvv8-g94c BIT-airflow-2023-22884 CVE-2023-22884 PYSEC-2026-268 PYSEC-2026-279
Command Injection in Apache Airflow and Apache Airflow MySQL Provider
- Severity
- 9.8 / 10
- Critical · CVSS 3.1
- Exploit chance (EPSS)
- 11.91%
- Chance of exploitation in the next 30 days. Higher than 96.11% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-77 Improper Neutralization of Special Elements used in a Command ('Command Injection')
- Fix
- Available
- 2 packages have a fixed version
Description
Improper Neutralization of Special Elements used in a Command ('Command Injection') vulnerability in Apache Software Foundation Apache Airflow, Apache Software Foundation Apache Airflow MySQL Provider.This issue affects Apache Airflow: before 2.5.1; Apache Airflow MySQL Provider: before 4.0.0.
Affected versions
PyPI
| Package | Vulnerable | Fixed in |
|---|---|---|
| apache-airflow | < 2.5.1 |
2.5.1 |
| apache-airflow-providers-mysql | < 4.0.0 |
4.0.0 |
How to fix
Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.
apache-airflow PyPI → 2.5.1
- pip
pip install --upgrade "apache-airflow>=2.5.1"- poetry
poetry add "apache-airflow>=2.5.1"- uv
uv add "apache-airflow>=2.5.1"
apache-airflow-providers-mysql PyPI → 4.0.0
- pip
pip install --upgrade "apache-airflow-providers-mysql>=4.0.0"- poetry
poetry add "apache-airflow-providers-mysql>=4.0.0"- uv
uv add "apache-airflow-providers-mysql>=4.0.0"
Severity breakdown
CVSS 3.1 9.8
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
- Attack vector
- Network
- Attack complexity
- Low
- Privileges required
- None
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- High
- Integrity
- High
- Availability
- High
Weakness
CWE-77: Improper Neutralization of Special Elements used in a Command ('Command Injection')
The product constructs all or part of a command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended command when it is sent to a downstream component.
How to prevent it
- Architecture and Design: If at all possible, use library calls rather than external processes to recreate the desired functionality.
- Implementation: If possible, ensure that all external commands called from the program are statically created.
- Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
- Operation: Run time: Run time policy enforcement may be used in an allowlist fashion to prevent use of any non-sanctioned commands.
- System Configuration: Assign permissions that prevent the user from accessing/opening privileged files.
References
Advisories
Packages
Other links
- github.com/apache/airflow/pull/28811
- github.com/pypa/advisory-database/tree/main/vulns/apache-airflow-providers-mysq…
- github.com/pypa/advisory-database/tree/main/vulns/apache-airflow/PYSEC-2026-268…
- lists.apache.org/thread/0l0j3nt0t7fzrcjl2ch0jgj6c58kxs5h
- pypi.org/project/apache-airflow
- pypi.org/project/apache-airflow-providers-mysql
Timeline
- Published
- Last updated by the source
- Latest EPSS score
Related advisories
| Advisory | Severity | Why it's related |
|---|---|---|
| CVE-2020-13927 Authentication bypass in Apache Airflow | Critical Known exploited | Also affects apache-airflow |
| CVE-2020-11978 Remote code execution (RCE) in Apache Airflow | High Known exploited | Also affects apache-airflow |
| PYSEC-2026-3990 Apache Airflow: the Core API logout endpoint revokes only a session token presented as the _token cookie. | Critical | Also affects apache-airflow |
| PYSEC-2026-3989 When a request to the Airflow core API carries both a session cookie and an explicit `Authorization: Bearer` token, Airflow resolves the caller from the cookie… | Medium | Also affects apache-airflow |
| PYSEC-2026-3988 Apache Airflow's `/assets/events` API returned asset events for every Dag in the deployment, with no filter restricting them to the Dags the caller is… | Medium | Also affects apache-airflow |
| GHSA-5247-m8w9-2v4m Apache Airflow missing team context permits cross-team Dag actions and XCom reads | Medium | Also affects apache-airflow |
| GHSA-f3c2-j7g8-vpp8 Apache Airflow Task SDK fails to mask list-shaped JSON Variables | Medium | Also affects apache-airflow |
| GHSA-cjv4-7hp2-2x9h Apache Airflow bulk endpoints log Variable and Connection secrets in cleartext | Medium | Also affects apache-airflow |
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