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CVE-2023-1177

CRITICAL
Published 2023-03-24T00:00:00.000Z
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CVSS Score

V3.1
9.3
/10
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:L/A:N
Base Score Metrics
Exploitability: N/A Impact: N/A

EPSS Score

v2025.03.14
0.932
probability
of exploitation in the wild

There is a 93.2% chance that this vulnerability will be exploited in the wild within the next 30 days.

Updated: 2025-06-25
Exploit Probability
Percentile: 0.998
Higher than 99.8% of all CVEs

Attack Vector Metrics

Attack Vector
NETWORK
Attack Complexity
LOW
Privileges Required
NONE
User Interaction
NONE
Scope
CHANGED

Impact Metrics

Confidentiality
HIGH
Integrity
LOW
Availability
NONE

Description

Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.2.1.

Available Exploits

Mlflow <2.2.1 - Local File Inclusion

Mlflow before 2.2.1 is susceptible to local file inclusion due to path traversal \..\filename in GitHub repository mlflow/mlflow. An attacker can potentially obtain sensitive information, modify data, and/or execute unauthorized administrative operations in the context of the affected site.

ID: CVE-2023-1177
Author: iamnoooobpdresearch Critical

Related News

No news articles found for this CVE.

Affected Products

GitHub Security Advisories

Community-driven vulnerability intelligence from GitHub

✓ GitHub Reviewed CRITICAL

mlflow is vulnerable to remote file access in `mlflow server` and `mlflow ui` CLIs

GHSA-xg73-94fp-g449

Advisory Details

### Impact Users of the MLflow Open Source Project who are hosting the MLflow Model Registry using the `mlflow server` or `mlflow ui` commands using an MLflow version older than MLflow 2.2.1 may be vulnerable to a remote file access exploit if they are not limiting who can query their server (for example, by using a cloud VPC, an IP allowlist for inbound requests, or authentication / authorization middleware). This issue only affects users and integrations that run the `mlflow server` and `mlflow ui` commands. Integrations that do not make use of `mlflow server` or `mlflow ui` are unaffected; for example, the Databricks Managed MLflow product and MLflow on Azure Machine Learning do not make use of these commands and are not impacted by these vulnerabilities in any way. The vulnerability detailed in https://nvd.nist.gov/vuln/detail/CVE-2023-1177 enables an actor to download arbitrary files unrelated to MLflow from the host server, including any files stored in remote locations to which the host server has access. ### Patches This vulnerability has been patched in MLflow 2.2.1, which was released to PyPI on March 2nd, 2023. If you are using `mlflow server` or `mlflow ui` with the MLflow Model Registry, we recommend upgrading to MLflow 2.2.1 as soon as possible. ### Workarounds If you are using the MLflow open source `mlflow server` or `mlflow ui` commands, we strongly recommend limiting who can access your MLflow Model Registry and MLflow Tracking servers using a cloud VPC, an IP allowlist for inbound requests, authentication / authorization middleware, or another access restriction mechanism of your choosing. If you are using the MLflow open source `mlflow server` or `mlflow ui` commands, we also strongly recommend limiting the remote files to which your MLflow Model Registry and MLflow Tracking servers have access. For example, if your MLflow Model Registry or MLflow Tracking server uses cloud-hosted blob storage for MLflow artifacts, make sure to restrict the scope of your server's cloud credentials such that it can only access files and directories related to MLflow. ### References More information about the vulnerability is available at https://nvd.nist.gov/vuln/detail/CVE-2023-1177.

Affected Packages

PyPI mlflow
ECOSYSTEM: ≥0 <2.2.1

CVSS Scoring

CVSS Score

9.0

CVSS Vector

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Advisory provided by GitHub Security Advisory Database. Published: March 24, 2023, Modified: October 1, 2024

References

Published: 2023-03-24T00:00:00.000Z
Last Modified: 2025-02-19T20:56:12.814Z
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