HighVulnerability

CVE-2026-2393: MLflow server-side request forgery through webhook URL creation

Source: NVD(opens in a new tab)
Identifier: CVE-2026-2393
Published: May 11, 2026
Record updated: October 9, 2026

Affected: mlflow < 3.9.0
Fixed in: 3.9.0
released January 29, 2026, 102 days before disclosure
Known exploitation: Not listed in the CISA Known Exploited Vulnerabilities catalog at the last check.
EPSS: 0.3%

Summary

CVE-2026-2393 is a Server-Side Request Forgery (SSRF) flaw in MLflow versions prior to 3.9.0. The `_create_webhook()` function in `mlflow/server/handlers.py` accepts a user-controlled `url` parameter without validation, and `_send_webhook_request()` in `mlflow/webhooks/delivery.py` sends HTTP POST requests to that URL. An authenticated attacker can force the MLflow backend to reach internal services, cloud metadata endpoints or external servers, potentially enabling cloud credential theft, internal network access and data exfiltration.

Mitigation

The source does not state a fix yet. Check the original advisory for updates.

Details

Scores and exploitation

Patch available: Yes
EPSS: 0.3%Estimated probability that this CVE is exploited in the next 30 days, from the FIRST Exploit Prediction Scoring System. Read on November 8, 2026.
Disclosure date: May 11, 2026

Weaknesses and techniques

Weakness (CWE): CWE-918
Attack pattern (CAPEC): CAPEC-664

Affected

Packages: mlflow@< 3.9.0 (fixed: 3.9.0)

Classification

Attack type: Other
Attack sophistication: Moderate
AI component targeted: Framework
Impact: Confidentiality,
Integrity
Labels: security

How this record was produced

Summaries, mitigations and labels on this page are generated automatically from the source. The linked source is authoritative. The source gives this advisory a description and no title, so the title above is a headline generated from the source text.

Original source: https://nvd.nist.gov/vuln/detail/CVE-2026-2393

First tracked: May 12, 2026 at 02:12 AM ET, from NVD/CVE Database

Classifier: claude-haiku-5-5, prompt v4

Classifier confidence: 90% (self-assessed)

Affected packages in the Exposure Registry

Matched by package name and ecosystem. Each entry shows whether the package delegates to a language model and how many tracked packages depend on it.

mlflowPyPILLM dependency since 2024-05-30 · 17 tracked dependents

Summary

Mitigation

Affected packages in the Exposure Registry

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