CVE-2023-30172 — HIGH (CVSS 7.5) AI Security Vulnerability

CISO Take

Any MLflow instance running v2.0.1 or earlier is vulnerable to unauthenticated arbitrary file read — no credentials needed, no user interaction required. This puts model weights, training datasets, and cloud credentials (AWS/GCP keys routinely stored near artifact directories) at immediate risk. Patch to v2.1.0+ now and verify your MLflow server is not internet-exposed.

Risk Assessment

Effectively critical for any internet-facing deployment despite the 7.5 CVSS score. Zero authentication, network accessibility, and low attack complexity make this trivially exploitable by any attacker with HTTP access. MLflow servers routinely sit adjacent to cloud credential files, S3 bucket configurations, and database connection strings — amplifying blast radius far beyond a typical file disclosure. Internal-only deployments are still high risk given lateral movement potential after initial network foothold.

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

0.4%

chance of exploitation in 30 days

Higher than 64% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

No known exploitation

Sophistication

Trivial

Attack Surface

AV Network

AC Low

PR None

UI None

S Unchanged

C High

I None

A None

Recommended Action

1 step

1) Patch: Upgrade MLflow to v2.1.0 or later immediately. 2) Isolate: If patching is delayed, firewall MLflow to internal networks only — never expose tracking server to the internet. 3) Least privilege: Run MLflow under a dedicated service account with read access scoped only to artifact directories. 4) Credential hygiene: Ensure cloud credentials are not stored in paths accessible from the MLflow host; use IAM roles/instance profiles instead. 5) Detection: Audit HTTP access logs for path traversal patterns in artifact API calls — look for '../', '%2e%2e%2f', or absolute paths in the 'path' parameter. 6) Incident response: If exposure is suspected, rotate all cloud credentials accessible from the server and audit artifact bucket access logs.

CISA SSVC Assessment

Decision Track

Exploitation none

Automatable Yes

Technical Impact partial

Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.

Classification

Data Extraction Data Leakage Framework Training Data Model AML.T0010.001 - AI Software AML.T0025 - Exfiltration via Cyber Means AML.T0035 - AI Artifact Collection AML.T0037 - Data from Local System AML.T0049 - Exploit Public-Facing Application AML.T0055 - Unsecured Credentials

Compliance Impact

This CVE is relevant to:

EU AI Act

Art.15 - Accuracy, robustness and cybersecurity

ISO 42001

A.6.2.6 - Information security for AI systems A.8.2 - AI system access controls during operation

NIST AI RMF

GOVERN-6.1 - Policies for AI risk and impacts MANAGE-2.2 - Mechanisms to sustain AI risk management

OWASP LLM Top 10

LLM06 - Sensitive Information Disclosure

Frequently Asked Questions

What is CVE-2023-30172?

Any MLflow instance running v2.0.1 or earlier is vulnerable to unauthenticated arbitrary file read — no credentials needed, no user interaction required. This puts model weights, training datasets, and cloud credentials (AWS/GCP keys routinely stored near artifact directories) at immediate risk. Patch to v2.1.0+ now and verify your MLflow server is not internet-exposed.

Is CVE-2023-30172 actively exploited?

No confirmed active exploitation of CVE-2023-30172 has been reported, but organizations should still patch proactively.

How to fix CVE-2023-30172?

1) Patch: Upgrade MLflow to v2.1.0 or later immediately. 2) Isolate: If patching is delayed, firewall MLflow to internal networks only — never expose tracking server to the internet. 3) Least privilege: Run MLflow under a dedicated service account with read access scoped only to artifact directories. 4) Credential hygiene: Ensure cloud credentials are not stored in paths accessible from the MLflow host; use IAM roles/instance profiles instead. 5) Detection: Audit HTTP access logs for path traversal patterns in artifact API calls — look for '../', '%2e%2e%2f', or absolute paths in the 'path' parameter. 6) Incident response: If exposure is suspected, rotate all cloud credentials accessible from the server and audit artifact bucket access logs.

What systems are affected by CVE-2023-30172?

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

What is the CVSS score for CVE-2023-30172?

CVE-2023-30172 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.45%.

Technical Details

NVD Description

A directory traversal vulnerability in the /get-artifact API method of the mlflow platform up to v2.0.1 allows attackers to read arbitrary files on the server via the path parameter.

Exploitation Scenario

An attacker discovers an exposed MLflow tracking server via Shodan or a misconfigured security group. They send a single unauthenticated HTTP GET to /get-artifact?path=../../../../home/mlflow-svc/.aws/credentials and receive AWS access keys in the response body within seconds. With those keys they enumerate the S3 artifact bucket, exfiltrate the full model registry (IP theft), and — in a destructive variant — overwrite production model artifacts with poisoned replacements. The entire initial compromise requires one HTTP request and knowledge of standard Linux filesystem paths. No AI/ML expertise needed.