Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2026-33858 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Apache Airflow. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 45th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-33858 is a deserialization vulnerability (CWE-502) in Apache Airflow versions prior to 3.2.0, published on 2026-04-13. It allows Dag Authors, who typically lack permissions to execute code in the webserver context, to craft malicious XCom payloads that trigger arbitrary code execution on the webserver. Despite the high CVSS v3.1 score of 8.8 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H), the issue is rated low severity due to the high trust already placed in Dag Authors.
An attacker with Dag Author privileges can exploit this remotely over the network with low complexity and no user interaction required. By pushing a specially crafted XCom payload, they achieve full remote code execution on the Airflow webserver, potentially compromising confidentiality, integrity, and availability of the system.
Apache Airflow advisories recommend upgrading to version 3.2.0, which resolves the vulnerability. Relevant discussions and the fixing pull request are available at https://github.com/apache/airflow/pull/64148, https://lists.apache.org/thread/1npt3o2x81s0gw9tmfcv4n7p1z9hdmy0, and http://www.openwall.com/lists/oss-security/2026/04/13/7.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-21978
Vulnerability Data
Dag Authors, who normally should not be able to execute code in the webserver context could craft XCom payload causing the webserver to execute arbitrary code. Since Dag Authors are already highly trusted, severity of this issue is Low. Users…
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are recommended to upgrade to Apache Airflow 3.2.0, which resolves this issue.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Requiring vetted libraries, regular updates and SAST before release reduces the likelihood that deserialization logic will accept and act on attacker-controlled serialized objects.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.
Mandatory malware scanning of data received over networks or storage media intercepts malicious serialized payloads before they are deserialized by the target application.