Cyber Resilience

CVE-2023-6909

Lfprojects Mlflow ≤ 2.9.2

Public PoCHigh EPSS
Published
18 December 2023
Modified
21 November 2024
Patch / advisory
View advisory
CVSS Score v3.1 7.5
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N
EPSS Score 0.90 99.8th percentile
Risk Priority 83 floored blend · peak EPSS

Summary

CVE-2023-6909 is a high-severity Path Traversal: '\..\filename' (CWE-29) vulnerability in Lfprojects Mlflow. Its CVSS base score is 7.5 (High).

Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 0.2% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.

This vulnerability is AI-related — categorised as Other AI Platforms; in the Other ATLAS/OWASP Terms risk domain.

Deeper analysis AI-assisted summary

Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.

CVE-2023-6909 is a path traversal vulnerability, tracked as CWE-29, that affects the MLflow machine learning platform in the GitHub repository mlflow/mlflow prior to version 2.9.2. The flaw permits traversal sequences of the form '\..\filename' and carries a CVSS 3.1 base score of 7.5, reflecting network attack vector, low attack complexity, and no required privileges or user interaction, with high impact on confidentiality.

An unauthenticated attacker with network access can supply crafted path sequences to read arbitrary files on the server hosting the MLflow instance, exposing sensitive data without affecting integrity or availability.

The referenced GitHub commit 1da75dfcecd4d169e34809ade55748384e8af6c1 and the associated huntr.dev bounty report indicate that the issue is resolved by upgrading to MLflow 2.9.2 or later.

The vulnerability's EPSS score has reached a current value of 0.8571 with a recorded peak of 0.8741, indicating sustained exploitation interest after public disclosure; because MLflow is widely used in machine-learning workflows, the flaw is particularly relevant to AI/ML environments.

EU & UK References

Vulnerability Data

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

CWE(s)
CWE-29

AI Security AnalysisAI

AI Category
Other AI Platforms
Risk Domain
Other ATLAS/OWASP Terms
OWASP Top 10 for LLMs 2025
None mapped
Classification Reason
MLflow is an open-source platform for managing the end-to-end machine learning lifecycle, including experimentation, reproducibility, and deployment, fitting under 'Other Platforms' as it is not a specific framework, library, or other listed subcategory.

Related Threats

MITRE ATT&CK Enterprise Techniques

T1190 Exploit Public-Facing Application Initial Access
Adversaries may attempt to exploit a weakness in an Internet-facing host or system to initially access a network.
attack.mitre.org →
T1005 Data from Local System Collection
Adversaries may search local system sources, such as file systems, configuration files, local databases, virtual machine files, or process memory, to find files of interest and sensitive data prior to Exfiltration.
attack.mitre.org →
T1083 File and Directory Discovery Discovery
Adversaries may enumerate files and directories or may search in specific locations of a host or network share for certain information within a file system.
attack.mitre.org →
T1552 Unsecured Credentials Credential Access
Adversaries may search compromised systems to find and obtain insecurely stored credentials.
attack.mitre.org →
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2023-6977Same product: Lfprojects Mlflow
CVE-2023-2780Same product: Lfprojects Mlflow
CVE-2024-3573Same product: Lfprojects Mlflow
CVE-2024-8859Same product: Lfprojects Mlflow
CVE-2023-6831Same product: Lfprojects Mlflow
CVE-2024-2928Same product: Lfprojects Mlflow
CVE-2024-3848Same product: Lfprojects Mlflow
CVE-2023-6975Same product: Lfprojects Mlflow
CVE-2023-1177Same product: Lfprojects Mlflow
CVE-2025-15036Same product: Lfprojects Mlflow

Affected Assets

lfprojects
mlflow
≤ 2.9.2

Mitigating Controls

Mitigating Controls (NIST CSF 2.0) AI

Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.

PR.PS-06 mostly match
prevents

Secure SDLC practices directly require input validation and path sanitization that block this traversal vector.

Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI

Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.

A.8.29 Security testing in development and acceptance mostly match
finds

Security testing can discover path-traversal flaws but does not itself prevent them in production code.

A.8.26 Application security requirements partial match
prevents

Application security requirements can mandate input validation and path canonicalization to block traversal sequences.

A.8.27 Secure system architecture and engineering principles partial match
prevents

Secure architecture principles include directory sandboxing and safe file-access design that mitigate path traversal.

A.8.28 Secure coding partial match
prevents

Secure coding standards directly require neutralizing path traversal sequences such as '\..\filename'.

References