MLflow: server-side request forgery flaw under attack

What is affected

MLflow is mostly run by companies, schools and public bodies, not on home computers. You are unlikely to have it yourself, but organisations that hold your data may. Breaches that start with flaws like this one are how personal data ends up leaked.

MLflow contains a server-side request forgery vulnerability that can allow attackers to reach internal or cloud metadata services and receive response_status and response_body.

What an attacker can do

This is a server-side request forgery flaw. It lets an attacker make the server send requests on their behalf and reach internal systems that are not exposed to the internet.

How it is reached

Over the internet or network

Access the attacker needs

No account needed

Does the victim have to do something?

No, works without the victim doing anything

What to do

1. At home: nothing to install. Keep your own devices updated and use a different password for every service, so that a breach at one organisation does not open your other accounts.
2. If you administer MLflow at work: apply the vendor's fix or mitigation now and review logs for signs of compromise, because the flaw was being exploited before it was listed.
3. If the product is past its support date and no fix exists, take it off the internet or retire it.

Apply mitigations in accordance with vendor instructions, ensuring compliance with CISA’s BOD 26-04 Prioritizing Security Updates Based on Risk (see URL in Notes) guidance and CISA’s “Forensics Triage Requirements” (see URL in Notes). Follow applicable BOD 26-04 guidance for cloud services or discontinue use of the product if mitigations are unavailable. Stakeholders are responsible for evaluating each asset's internet exposure and ensuring adherence to BOD 26-04 patching guidelines.

Vendor advisories and fixes

• github.comgithub.com/mlflow/mlflow/pull/24258
• github.comgithub.com/mlflow/mlflow/issues/24179

How urgent is it

CISA added this vulnerability to its Known Exploited Vulnerabilities catalogue on August 19, 2026, which means there is reliable evidence of attacks in the wild. US federal agencies must fix it by September 2, 2026, a deadline that has already passed. That deadline does not bind anyone else, but it shows how seriously the agency rates it.

The EPSS model estimates a 9.8% probability that this flaw will be exploited somewhere in the next 30 days. That is higher than 95% of all scored vulnerabilities.

Its CVSS severity score is 9.3 out of 10 (critical), as recorded in the US National Vulnerability Database.

Technical description

MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. Starting in 3.3.0 and prior to 3.15.0, the unauthenticated POST /api/2.0/mlflow/webhooks/{id}/test endpoint calls _validate_webhook_url() in mlflow/utils/validation.py only for the original URL while mlflow/webhooks/delivery.py follows redirects and re-resolves the hostname without pinning the validated address, allowing attackers to reach internal or cloud metadata services and receive response_status and response_body. This issue is fixed in version 3.15.0.