CVE-2023-6909 — HIGH (CVSS 7.5) AI Security Vulnerability

CISO Take

MLflow servers prior to 2.9.2 allow unauthenticated remote attackers to read arbitrary files via path traversal—no credentials needed. In MLOps environments this directly risks exposure of model artifacts, training data, API keys, and credentials stored on the MLflow host. Upgrade to 2.9.2+ immediately and restrict MLflow network exposure to trusted networks only.

Risk Assessment

HIGH. CVSS vector AV:N/AC:L/PR:N/UI:N makes this trivially exploitable by any attacker with network access. MLflow instances are frequently deployed with internet or broad internal exposure for team collaboration. The confidentiality impact is severe: MLflow servers routinely hold experiment configs, model weights, API keys in .env files, cloud credentials, and data paths. No active KEV listing, but the zero-barrier exploitation and sensitivity of MLflow server contents make this a priority patch for any MLOps team.

Affected Systems

Package	Ecosystem	Vulnerable Range	Patched
mlflow	pip	—	No patch
25.7K OpenSSF 4.5 624 dependents Pushed 7d ago 24% patched ~64d to patch Full package profile →

Do you use mlflow? You're affected.

Severity & Risk

CVSS 3.1

7.5 / 10

EPSS

85.7%

chance of exploitation in 30 days

Higher than 99% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Trivial

Exploitation Confidence

medium

○ Public PoC indexed (trickest/cve)

○ Nuclei detection template available

○ EPSS exploit prediction: 86%

Composite signal derived from CISA KEV, CISA SSVC, EPSS, trickest/cve, and Nuclei templates.

Attack Surface

AV Network

AC Low

PR None

UI None

S Unchanged

C High

I None

A None

Recommended Action

5 steps

PATCH

Upgrade MLflow to 2.9.2 or later immediately.
NETWORK CONTROLS

Restrict MLflow server access to trusted VPN/internal networks; remove any public internet exposure.
LEAST PRIVILEGE

Run MLflow with a dedicated service account with minimal filesystem permissions—limit readable directories to experiment artifacts only.
DETECTION

Search web/application logs for requests containing '../', '%2e%2e', or '..\\' patterns targeting MLflow endpoints.
AUDIT

Review MLflow server filesystem for sensitive files (credentials, SSH keys) and relocate them outside the accessible path.

Classification

Data Extraction Framework AML.T0025 - Exfiltration via Cyber Means AML.T0035 - AI Artifact Collection AML.T0037 - Data from Local System AML.T0049 - Exploit Public-Facing Application

Compliance Impact

This CVE is relevant to:

EU AI Act

Art. 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.8.1 - AI system operational and monitoring processes

NIST AI RMF

MANAGE-2.2 - Mechanisms to sustain risk treatment

OWASP LLM Top 10

LLM03:2025 - Supply Chain Vulnerabilities

Frequently Asked Questions

What is CVE-2023-6909?

MLflow servers prior to 2.9.2 allow unauthenticated remote attackers to read arbitrary files via path traversal—no credentials needed. In MLOps environments this directly risks exposure of model artifacts, training data, API keys, and credentials stored on the MLflow host. Upgrade to 2.9.2+ immediately and restrict MLflow network exposure to trusted networks only.

Is CVE-2023-6909 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2023-6909, increasing the risk of exploitation.

How to fix CVE-2023-6909?

1. PATCH: Upgrade MLflow to 2.9.2 or later immediately. 2. NETWORK CONTROLS: Restrict MLflow server access to trusted VPN/internal networks; remove any public internet exposure. 3. LEAST PRIVILEGE: Run MLflow with a dedicated service account with minimal filesystem permissions—limit readable directories to experiment artifacts only. 4. DETECTION: Search web/application logs for requests containing '../', '%2e%2e', or '..\\' patterns targeting MLflow endpoints. 5. AUDIT: Review MLflow server filesystem for sensitive files (credentials, SSH keys) and relocate them outside the accessible path.

What systems are affected by CVE-2023-6909?

This vulnerability affects the following AI/ML architecture patterns: ML experiment tracking, model registry, training pipelines, MLOps infrastructure, CI/CD for ML.

What is the CVSS score for CVE-2023-6909?

CVE-2023-6909 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 85.72%.

Technical Details

NVD Description

Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2.

Exploitation Scenario

An attacker scans for exposed MLflow instances (default port 5000) via Shodan or similar. With no authentication required, they craft a GET request with a path traversal payload (e.g., /get-artifact?path=../../../../etc/passwd or ~/.aws/credentials). In a typical MLOps pipeline, MLflow runs with access to training data storage, model registries, and CI/CD secrets. The attacker iterates through common credential paths to extract cloud provider keys, enabling lateral movement to S3/GCS buckets containing training data or production model artifacts—potentially poisoning future model versions.