CVE-2023-2356 — HIGH (CVSS 7.5) AI Security Vulnerability

CISO Take

Any internet-accessible MLflow tracking server running below 2.3.1 is fully exposed — no credentials required, single HTTP request. Patch immediately to 2.3.1+; if patching is delayed, isolate MLflow behind a VPN or internal network boundary. Audit whether your MLflow instances store cloud credentials, API keys, or model artifacts on the same filesystem.

Risk Assessment

Effective risk is HIGH to CRITICAL in practice despite the CVSS 7.5 score. The combination of no authentication, no user interaction, low complexity, and network access makes this trivially weaponizable. MLflow is often deployed with broad filesystem access on servers that also store cloud provider credentials, Hugging Face tokens, and training datasets. Organizations that expose MLflow tracking servers directly (dev shortcuts, misconfigured cloud SGs) face immediate data breach risk.

Affected Systems

Package	Ecosystem	Vulnerable Range	Patched
mlflow	pip	—	No patch
25.8K OpenSSF 4.7 624 dependents Pushed today 24% patched ~64d to patch Full package profile →

Do you use mlflow? You're affected.

Severity & Risk

CVSS 3.1

7.5 / 10

EPSS

90.5%

chance of exploitation in 30 days

Higher than 100% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Trivial

Exploitation Confidence

medium

○ CISA SSVC: Public PoC

○ Public PoC indexed (trickest/cve)

○ Nuclei detection template available

○ EPSS exploit prediction: 90%

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 2.3.1 or later immediately — this is the only complete fix.
NETWORK ISOLATION

If patching is delayed, restrict MLflow access to VPN/internal networks via firewall rules or security groups. Block public internet access.
LEAST PRIVILEGE

Run MLflow process with a dedicated service account with minimal filesystem permissions — no access to ~/.aws, ~/.ssh, or application secrets.
DETECTION

Search logs for requests containing '../', '%2e%2e', or URL-encoded path traversal sequences against MLflow endpoints. Alert on any request that resolves outside the MLflow artifact root.
AUDIT

Enumerate all MLflow deployments in your environment; check if any are publicly accessible via cloud provider asset inventory tools.

CISA SSVC Assessment

Decision Attend

Exploitation poc

Automatable Yes

Technical Impact total

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

Classification

Data Extraction Data Leakage Framework AML.T0007 - Discover AI Artifacts AML.T0025 - Exfiltration via Cyber Means AML.T0035 - AI Artifact Collection AML.T0049 - Exploit Public-Facing Application AML.T0055 - Unsecured Credentials

Compliance Impact

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.9.3 - Protection of AI system resources

NIST AI RMF

MS-2.5 - AI Risk Monitoring — System and Component Vulnerability Management

OWASP LLM Top 10

LLM05:2025 - Improper Output Handling / Insecure Design

Frequently Asked Questions

What is CVE-2023-2356?

Any internet-accessible MLflow tracking server running below 2.3.1 is fully exposed — no credentials required, single HTTP request. Patch immediately to 2.3.1+; if patching is delayed, isolate MLflow behind a VPN or internal network boundary. Audit whether your MLflow instances store cloud credentials, API keys, or model artifacts on the same filesystem.

Is CVE-2023-2356 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2023-2356, increasing the risk of exploitation.

How to fix CVE-2023-2356?

1. PATCH: Upgrade MLflow to 2.3.1 or later immediately — this is the only complete fix. 2. NETWORK ISOLATION: If patching is delayed, restrict MLflow access to VPN/internal networks via firewall rules or security groups. Block public internet access. 3. LEAST PRIVILEGE: Run MLflow process with a dedicated service account with minimal filesystem permissions — no access to ~/.aws, ~/.ssh, or application secrets. 4. DETECTION: Search logs for requests containing '../', '%2e%2e', or URL-encoded path traversal sequences against MLflow endpoints. Alert on any request that resolves outside the MLflow artifact root. 5. AUDIT: Enumerate all MLflow deployments in your environment; check if any are publicly accessible via cloud provider asset inventory tools.

What systems are affected by CVE-2023-2356?

This vulnerability affects the following AI/ML architecture patterns: MLOps platforms, model registries, training pipelines, experiment tracking systems, model serving.

What is the CVSS score for CVE-2023-2356?

CVE-2023-2356 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 90.45%.

Technical Details

NVD Description

Relative Path Traversal in GitHub repository mlflow/mlflow prior to 2.3.1.

Exploitation Scenario

An adversary scanning for exposed MLflow tracking servers (port 5000/5001, or via cloud metadata) identifies an unpatched instance. They craft a GET request to the MLflow artifact API with a relative path traversal payload (e.g., /api/2.0/mlflow/artifacts/get?path=../../.aws/credentials&run_id=<valid_run_id>). With no authentication enforced, the server returns the AWS credentials file. The attacker uses the harvested keys to access S3 buckets containing proprietary model weights, training datasets, or customer data. In a more targeted scenario, the attacker reads the MLflow database connection string or a mounted secrets file to pivot laterally into the ML infrastructure.