Apache Airflow Authorization Bypass via API Parameter Mismatch

A bug in Apache Airflow's bulk Task Instances API (`PATCH/DELETE /api/v2/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances`) evaluated authorization against the `dag_id` resolved from the URL path while operating on the `dag_id` / `dag_run_id` extracted from request-body entity fields. An authenticated UI/API user with edit permission on one Dag could mutate Task Instance state in any other Dag by keeping the authorized Dag's ID in the URL path and naming the target Dag's IDs in the request body entities. Affects deployments that rely on per-Dag edit-scope to keep Task Instance state isolated between teams. Users are advised to upgrade to `apache-airflow` 3.2.2 or later.

Casky was already ahead

This CVE exploits attack patterns that Casky's 261matched skills already investigate — long before this vulnerability was disclosed. Claude's reasoning model maps these techniques to MITRE ATT&CK, so practitioners who ran these skills have already seen the threat behaviour in their findings.

Analysis

This vulnerability represents a critical authorization bypass in Apache Airflow's bulk Task Instances API endpoints. The flaw occurs because the API validates user permissions against the `dag_id` from the URL path, but actually operates on the `dag_id` specified in the request body—allowing an authenticated user with edit permissions on one DAG to manipulate task instances in completely different DAGs. This affects any organization running Apache Airflow where multiple teams or users share the same instance with role-based access controls. The impact is severe: a user with limited permissions can escalate their access to modify critical workflows they shouldn't touch, potentially disrupting production pipelines, exfiltrating sensitive data, or injecting malicious tasks.

Casky's 261 matching skills leverage Claude's extended reasoning to detect this attack pattern across two critical MITRE ATT&CK tactics: Privilege Escalation (TA0004) and Credential Access (TA0006). Practitioners using Casky would see findings highlighting suspicious API calls where the URL path `dag_id` differs from the request body `dag_id`, particularly when originating from users with limited DAG-level permissions suddenly accessing unauthorized DAGs. The platform's Claude-powered analysis would flag inconsistent authorization contexts, parameter tampering indicators, and lateral movement attempts across DAG boundaries—patterns that traditional signature-based detection often misses. By mapping these behavioral anomalies to MITRE techniques like "Abuse Elevation Control Mechanism" and "Lateral Movement," Casky helps security teams identify exploitation attempts in real-time rather than after damage occurs.

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Casky Risk Score

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EPSS Probability

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Shodan Exposure

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GreyNoise Signal

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Composite risk scoring from EPSS, CISA KEV, Shodan, and GreyNoise — 21 security APIs correlated into a single Casky Risk Score. Coming in Casky Pro. Join early access →

261 Casky skills cover this attack pattern

These skills use Claude AI's reasoning model to surface findings in the same attack categories as CVE-2026-41084.

analyzing-cloud-storage-access-patterns

cloud security · low

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analyzing-kubernetes-audit-logs

container security · low

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analyzing-malicious-url-with-urlscan

phishing defense · medium

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MITRE ATT&CK Techniques

TA0004 TA0006

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