CVE-2026-86465 (GCVE-0-2026-86465)
Vulnerability from cvelistv5 – Published: 2026-09-16 08:58 – Updated: 2026-09-17 19:31
VLAI
EPSS
VEX
Title
Apache Airflow Akeyless provider: Akeyless secrets backend: team-scope guard bypass via user-controlled key
Summary
Apache Airflow Akeyless provider: the Akeyless secrets backend's team-scope guard can be bypassed with a user-controlled key. In a multi-team deployment, a Dag author scoped to one team can supply a Variable key containing a path separator that causes the backend to resolve a secret belonging to a different team, because the lookup path is concatenated from an unvalidated key after the team-scoped lookup misses. The Execution API Variables route accepts a path-shaped key, so this is reachable from ordinary Dag code.
Affects multi-team deployments using the Akeyless secrets backend. Single-team deployments are not affected, as there is no cross-team boundary to cross. This is the same class as CVE-2026-68870, CVE-2026-68871 and CVE-2026-68872 in the Azure Key Vault, Yandex Lockbox and Amazon secrets backends.
Users of apache-airflow-providers-akeyless are recommended to upgrade to version 0.3.1 or later, which fixes the issue.
Severity
6.5 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-17 19:28 UTC
CWE
- CWE-639 - Authorization Bypass Through User-Controlled Key
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://github.com/apache/airflow/pull/72646 | patch |
| https://lists.apache.org/thread/vczt5xgqjv8ot8fpp… | vendor-advisory |
| https://www.cve.org/CVERecord?id=CVE-2026-68870 | related |
| https://www.cve.org/CVERecord?id=CVE-2026-68871 | related |
| https://www.cve.org/CVERecord?id=CVE-2026-68872 | related |
| https://lists.apache.org/thread/vczt5xgqjv8ot8fpp… | vendor-advisory |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Apache Software Foundation | Apache Airflow Akeyless provider |
Affected:
0 , < 0.3.1
(semver)
|
guessed |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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