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CVE-2025-65995 – CRITICAL DATA PIPELINE FAILURE: Airflow Flaw PRINTS ALL Your Cloud Secrets (AWS, Azure, DB Passwords) in Plain Sight.

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CyberDudeBivash Pvt Ltd | Data Pipeline Security | Apache Airflow

CVE-2025-65995 — Airflow Leak Can Expose Cloud Secrets in the UI: AWS Keys, Azure Tokens, DB Passwords (Urgent Patch Guide)

Author: CyberDudeBivash | Published: 13 Dec 2025 (IST) | Category: Credential Exposure / Information Disclosure

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Reality Check (Important Clarification)

You may see dramatic headlines claiming Airflow “prints ALL cloud secrets in plain sight.” The real issue is still serious, but it is more specific: CVE-2025-65995 is an information disclosure bug where Airflow UI tracebacks can include the full kwargs passed to operators when a DAG fails during parsing. If a developer put secrets inside those kwargs (API keys, tokens, passwords), those secrets could be exposed in the UI to authenticated users who can view the failing DAG.

In other words: the vulnerability does not magically extract every secret from every backend. It leaks what you accidentally passed in. That is enough to trigger real-world breaches, especially in large enterprises where many users have read access to Airflow UI.

TL;DR (What to do right now)

  • Affected: Apache Airflow 3.1.0 to 3.1.3 (3.1.x prior to 3.1.4).
  • Fixed: Upgrade to Airflow 3.1.4 immediately.
  • Risk: If DAG parsing fails, UI tracebacks may reveal full operator kwargs, potentially exposing secrets to users who can view the DAG.
  • Fast containment: Restrict UI access, review roles, and temporarily limit who can view “DAG parse error” details until patched.
  • Hunt: Review logs/UI history for any exposed secrets; rotate any credentials that could have been displayed.

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Table of Contents

  1. What is CVE-2025-65995?
  2. Why this matters for AWS/Azure/DB secrets
  3. Affected versions and fixed release
  4. Who is at risk (real-world target map)
  5. Urgent patch guide: upgrade to 3.1.4
  6. Temporary mitigations while patching
  7. Detection and secret-leak hunting checklist
  8. Credential rotation plan (AWS, Azure, DB)
  9. Hardening Airflow: zero-trust pipeline security
  10. FAQ
  11. References

1) What is CVE-2025-65995?

CVE-2025-65995 is a security vulnerability in Apache Airflow where, under certain failure conditions, secrets can be disclosed to the Airflow web UI. Specifically, advisories describe that when a DAG fails during parsing, the Airflow UI may render an error traceback that includes the full kwargs passed to operators. If those kwargs include sensitive values (tokens, passwords, cloud keys), the UI error view can expose them.

This is a classic “pipeline secrets exposure” problem, but with a modern enterprise twist: Airflow is commonly used by data engineering, analytics, platform engineering, ML teams, and DevOps. In many organizations, read-only or viewer-level roles are broadly granted to reduce operational friction. That creates a dangerous situation where a mistake made by one developer (placing secrets directly in kwargs) can become a credential exposure incident visible to many.

The severity is described in advisories as moderate, but the practical business impact can be high depending on what the leaked secret can access. A single leaked AWS access key that has wide permissions can cause data theft, cryptomining, or ransomware staging. A leaked database password can be enough for data exfiltration. A leaked Azure service principal secret can enable privilege escalation in cloud.

CyberDudeBivash security position: Treat this as a “credential exposure incident class” vulnerability. Even when labeled moderate, you must patch quickly and run a leak audit.

2) Why this matters for AWS, Azure, and database secrets

Data pipelines are credential-rich by design. Airflow orchestrates connections to data warehouses, cloud storage, message queues, APIs, SaaS tools, and CI/CD services. In high-CPC US/EU enterprise environments, Airflow often touches regulated datasets (PII, PCI, healthcare records, financial analytics, business intelligence) and therefore has credentials capable of high-impact actions.

Here is the uncomfortable truth: many Airflow environments end up with “convenience secrets” scattered across DAG code, variables, connections, environment variables, and operator arguments. Engineers are under time pressure, pipelines break, and secrets get pushed into places they do not belong. CVE-2025-65995 becomes dangerous because it can turn that engineering shortcut into a visible secret leak inside the UI.

Secret TypeTypical Blast RadiusWhat attackers do next
AWS Access Keys / Session TokensCloud-wide compromise if permissions are broadEnumerate IAM, access S3, spin cryptominers, exfil data
Azure App Secret / Service PrincipalTenant-level compromise if role assignments are highEnumerate subscriptions, modify resources, create persistence
Database Passwords (Postgres, MySQL, MSSQL)Data theft, deletion, ransomware stagingDump tables, create new admin users, pivot to app servers

The highest-risk scenario is not “someone sees a secret by accident.” The highest-risk scenario is when an attacker already has low-level access (a compromised employee account, contractor access, or a stolen session) and uses the UI leak as a privilege escalation into cloud and databases.

3) Affected versions and fixed release

Public advisories indicate this issue impacts Apache Airflow 3.1.0, 3.1.1, 3.1.2, and 3.1.3. The recommended fix is to upgrade to Airflow 3.1.4, which includes changes to reduce the risk of kwargs being exposed in UI error messages.

Action: If you run Airflow 3.1.x and your version is below 3.1.4, schedule an emergency upgrade. If you cannot patch immediately, apply the mitigations below until you complete the upgrade.

4) Who is at risk (High-risk target map)

In the real world, the most “at risk” organizations are not defined by industry labels. They are defined by three operational realities: (1) many users can view Airflow UI, (2) secrets are sometimes passed into operators or stored in variables loosely, and (3) pipelines touch high-value systems.

4.1 High-risk environments

  • Large teams: Many data engineers and analysts have viewer access to Airflow for troubleshooting.
  • Multi-tenant Airflow: Shared orchestration clusters with many DAG authors.
  • Regulated data pipelines: Finance, healthcare, payments, identity, telecom analytics.
  • Cloud-native orchestration: Airflow workers with broad IAM roles “for convenience.”
  • External contractors: Third-party access to UI for operations and support.

4.2 The “silent breach” pattern

The most dangerous attacks do not announce themselves. A stolen user session or compromised identity can quietly browse Airflow UI, wait for a DAG to fail, and harvest exposed credentials from tracebacks. That can be followed by cloud enumeration, stealth exfiltration, and persistence. If your organization does not centrally monitor UI access events, you may not detect the leak until damage occurs.

5) Urgent patch guide: upgrade to Airflow 3.1.4

The only complete remediation is to upgrade to a fixed version. For CVE-2025-65995, advisories recommend upgrading to Airflow 3.1.4. Treat this as a production emergency change if your Airflow is internet-reachable or if UI access is broad.

Safe upgrade workflow (defender-friendly):

  1. Identify all Airflow components: webserver, scheduler, workers, triggerer, API server (if applicable), and metadata DB.
  2. Backup metadata DB and snapshot current deployment artifacts (containers, Helm chart values, environment config).
  3. Upgrade to Airflow 3.1.4 using your standard deployment mechanism (Helm, Docker, Kubernetes, VM packages).
  4. Restart all components to ensure no old pods/containers remain on the cluster.
  5. Validate that DAG parsing and UI error rendering behave as expected after upgrade.
  6. Run a targeted secret exposure audit (see sections 7 and 8) and rotate any potentially exposed secrets.

Need help upgrading safely? CyberDudeBivash can help you plan upgrade windows, verify patches, and run post-upgrade leak hunting.

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6) Temporary mitigations while patching (Defense-in-depth)

If you cannot patch within hours, you must reduce exposure. These mitigations do not replace patching. They reduce the chance of secrets being visible to the wrong people during the patch window.

6.1 Restrict who can view parse errors

  • Review Airflow RBAC roles and minimize who can view DAG details and error tracebacks.
  • For shared environments, create a “least-privilege viewer role” that hides advanced error details during the emergency window.
  • Audit SSO groups mapped to Airflow access. Remove overly broad groups temporarily.

6.2 Reduce secret presence in kwargs immediately

  • Hotfix the highest-risk DAGs: remove raw secrets from operator kwargs.
  • Replace raw secrets with secret references from a secret backend (Vault, AWS Secrets Manager, Azure Key Vault) where possible.
  • If you must pass a value temporarily, ensure it is masked/redacted and does not contain production credentials.

6.3 Limit UI exposure surface

  • Ensure Airflow UI is not publicly accessible. Put it behind VPN, identity-aware proxy, or strict allowlists.
  • Enable MFA on the identity provider if not already enforced.
  • Increase session protections: shorter session TTL for high-risk groups during the patch window.

7) Detection and secret-leak hunting checklist

Because this is a “leak to UI” issue, hunting is about discovering whether secrets could have been exposed and who may have seen them. Treat this as a security event if you find any real credentials in a traceback.

7.1 What to search for (high-signal indicators)

  • Recent DAG parse failures that produced UI-visible tracebacks.
  • Traceback output containing patterns resembling keys/tokens/passwords (cloud key formats, JDBC URLs, bearer tokens).
  • Any user activity spikes around failing DAGs (unusual UI access events, new logins, off-hours access).
  • Downstream indicators: suspicious cloud API calls, unexpected database logins, new IAM keys, unusual outbound traffic.

7.2 Log sources to prioritize

  • Airflow webserver access logs (who accessed which pages and when).
  • Identity provider logs (SSO logins, MFA events, anomalous sessions).
  • Cloud audit logs (AWS CloudTrail, Azure Activity logs).
  • Metadata DB audit logs (if enabled) for stored rendered fields or error records.

CyberDudeBivash incident trigger: If you find any live credential in a traceback, immediately rotate it and treat exposure as confirmed until proven otherwise.

8) Credential rotation plan (AWS, Azure, DB passwords)

Rotation is not optional when secrets may have been exposed. A leaked secret is a compromised secret, even if you did not see evidence of misuse. The correct enterprise approach is to rotate quickly, then review logs for abuse attempts pre- and post-rotation.

CredentialRotate Steps (High-level)Post-rotation checks
AWS access keysCreate new key, update secret store, deploy, disable old key, then deleteCloudTrail review, IAM changes, unusual region usage, new resources
Azure service principal secretCreate new secret/cert, update Key Vault, redeploy, revoke old secretActivity log review, role assignment changes, new app registrations
Database passwordRotate user password, update connection/secret backend, redeployDB auth logs, failed logins, new users, unusual queries/exfil patterns

If the leaked credential had broad permissions, consider a containment escalation: revoke sessions, reduce IAM policy scope, and implement short-lived credentials (role-based access) instead of static keys.

9) Hardening Airflow: zero-trust pipeline security

CVE-2025-65995 should push organizations toward a stronger secrets model. Data pipelines must be treated as production systems, not “internal tooling.” In US/EU enterprise risk terms, Airflow often becomes a privileged orchestration plane. That means it needs zero-trust defenses: least privilege, segmentation, strong identity, and continuous monitoring.

9.1 Secrets best practices (non-negotiable)

  • Never hardcode secrets in DAG files, operator kwargs, or default_args.
  • Use a dedicated secrets backend (Vault, AWS Secrets Manager, Azure Key Vault) and reference secrets by name.
  • Use short-lived credentials and IAM roles whenever possible.
  • Mask secrets at every layer: logs, UI, rendered templates, error traces, and plugin outputs.

9.2 Access control and segmentation

  • Minimize who can view DAG details and operational errors in shared environments.
  • Put Airflow UI behind an identity-aware proxy with MFA and device posture checks if possible.
  • Segment workers from sensitive networks; allow only required outbound access.

9.3 Monitoring and alerting

  • Alert on access to sensitive UI views and parse-error pages, especially from unusual identities.
  • Correlate Airflow events with cloud audit logs to detect secret misuse quickly.
  • Run periodic “secret scanning” against DAG repos to stop exposure before deployment.

CyberDudeBivash Services CTA: We help enterprises lock down Airflow, build zero-trust pipeline security, and run credential exposure hunts.

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FAQ

Q1) Does this vulnerability expose all secrets stored in Airflow?

A) No. It can expose secrets that were passed into operator kwargs and then shown in UI tracebacks during DAG parsing failures. That is still dangerous because engineers sometimes pass real credentials in those kwargs.

Q2) Which Airflow versions are affected?

A) Advisories indicate Airflow 3.1.0 through 3.1.3 are affected. Upgrade to Airflow 3.1.4.

Q3) What is the fastest mitigation if we cannot patch today?

A) Restrict UI access to DAG error details, reduce who can view failing DAG tracebacks, and remove any secrets from kwargs in the highest-risk DAGs immediately.

Q4) Should we rotate credentials even if we have no proof of exposure?

A) If you find any real secrets in tracebacks or if you cannot confidently prove they were not exposed, rotate. Assume exposure is possible in shared UI environments.

Q5) Is this a remote code execution vulnerability?

A) No. CVE-2025-65995 is described as an information disclosure issue. The risk is credential exposure leading to downstream compromise.

References

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