GHSA-q2hg-643c-gw8h BIT-airflow-2025-54550 CVE-2025-54550 PYSEC-2026-2353
Apache Airflow: RCE by race condition in example_xcom dag
- Severity
- 8.1 / 10
- High · CVSS 3.1
- Exploit chance (EPSS)
- 0.58%
- Chance of exploitation in the next 30 days. Higher than 47.66% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-94 Improper Control of Generation of Code ('Code Injection')
- Fix
3.2.0- Upgrade apache-airflow to this version or later
Description
The example example_xcom that was included in airflow documentation implemented unsafe pattern of reading value from xcom in the way that could be exploited to allow UI user who had access to modify XComs to perform arbitrary execution of code on the worker. Since the UI users are already highly trusted, this is a Low severity vulnerability.
It does not affect Airflow release - example_dags are not supposed to be enabled in production environment, however users following the example could replicate the bad pattern. Documentation of Airflow 3.2.0 contains version of the example with improved resiliance for that case.
Users who followed that pattern are advised to adjust their implementations accordingly.
Affected versions
PyPI
| Package | Vulnerable | Fixed in |
|---|---|---|
| apache-airflow | < 3.2.0 |
3.2.0 |
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 → 3.2.0
- pip
pip install --upgrade "apache-airflow>=3.2.0"- poetry
poetry add "apache-airflow>=3.2.0"- uv
uv add "apache-airflow>=3.2.0"
Severity breakdown
CVSS 3.1 8.1
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N
- Attack vector
- Network
- Attack complexity
- Low
- Privileges required
- Low
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- High
- Integrity
- High
- Availability
- None
Weakness
CWE-94: Improper Control of Generation of Code ('Code Injection')
The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.
How to prevent it
- Architecture and Design: Refactor your program so that you do not have to dynamically generate code.
- Architecture and Design: Run your code in a "jail" or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which code can be executed by your product. Examples include the Unix chroot jail and AppArmor. In general, managed code may provide some protection. This may not be a feasible solution, and it only limits the impact to the operating system; the rest of your application may still be subject to compromise. Be careful to avoid CWE-243 and other weaknesses related to jails.
- 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. To reduce the likelihood of code injection, use stringent allowlists that limit which constructs are allowed. If you are dynamically constructing code that invokes a function, then verifying that the input is alphanumeric might be insufficient. An attacker might still be able to reference a dangerous function that you did not intend to allow, such as system(), exec(), or exit().
- Testing: Use dynamic tools and techniques that interact with the product using large test suites with many diverse inputs, such as fuzz testing (fuzzing), robustness testing, and fault injection. The product's operation may slow down, but it should not become unstable, crash, or generate incorrect results.
- Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
- Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
- Implementation: For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].
References
Advisories
Packages
Other links
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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