Security Resilience

CVE-2023-2356

Path Traversal in Lfprojects Mlflow ≤ 2.3.1

Public PoCPath Traversal
Published
28 April 2023
Modified
21 November 2024
Patch / 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 0.042 91th percentile
Risk Priority 83 floored blend · peak EPSS

CVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.

Summary

CVE-2023-2356 is a high-severity Relative Path Traversal (CWE-23) vulnerability in Lfprojects Mlflow. Its CVSS base score is 7.5 (High).

Operationally, exploitation aligns with the MITRE ATT&CK technique Data Obfuscation (T1001); ranked in the top 9% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.

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-2023-2356 is a relative path traversal vulnerability (CWE-23) affecting the MLflow open-source machine-learning platform in the mlflow/mlflow GitHub repository prior to version 2.3.1. The flaw received a CVSS 3.1 base score of 7.5, reflecting network attackability with no required credentials or user interaction and a high impact on confidentiality.

An unauthenticated remote attacker can supply crafted path sequences to read arbitrary files on the server hosting the MLflow instance, exposing sensitive configuration data, credentials, or model artifacts without affecting integrity or availability.

The referenced GitHub commit and corresponding huntr.dev report document the fix that was merged to close the traversal issue; practitioners should upgrade to MLflow 2.3.1 or later. The associated EPSS score remains elevated (current 0.8902, peak 0.9049), indicating sustained exploitation interest for this ML-specific component.

OWASP Top 10 for Web (2025)

EU & UK References

Vulnerability Data

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

CWE(s)

Related Threats

MITRE ATT&CK Enterprise Techniques

T1001 Data Obfuscation Command And Control
Adversaries may obfuscate command and control traffic to make it more difficult to detect.
T1001.003 Protocol or Service Impersonation Command And Control
Adversaries may impersonate legitimate protocols or web service traffic to disguise command and control activity and thwart analysis efforts.
T1003.002 Security Account Manager Credential Access
Adversaries may attempt to extract credential material from the Security Account Manager (SAM) database either through in-memory techniques or through the Windows Registry where the SAM database is stored.
T1003.005 Cached Domain Credentials Credential Access
Adversaries may attempt to access cached domain credentials used to allow authentication to occur in the event a domain controller is unavailable.
T1003.007 Proc Filesystem Credential Access
Adversaries may gather credentials from the proc filesystem or `/proc`.
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.
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2023-1177Same product: Lfprojects Mlflow
CVE-2023-6977Same product: Lfprojects Mlflow
CVE-2023-6975Same product: Lfprojects Mlflow
CVE-2023-2780Same product: Lfprojects Mlflow
CVE-2024-1594Same product: Lfprojects Mlflow
CVE-2023-1176Same product: Lfprojects Mlflow
CVE-2025-10279Same product: Lfprojects Mlflow
CVE-2023-43472Same product: Lfprojects Mlflow
CVE-2023-6014Same product: Lfprojects Mlflow
CVE-2026-13484Same product: Lfprojects Mlflow

Affected Assets

lfprojects
mlflow
≤ 2.3.1

Mitigating Controls

Control response

Prevent
Stop it (NIST 800-53)

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Detect
Catch it (NIST detect / respond)

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Harden
Shrink the surface (DISA STIG)

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Validate
Prove the fix (OWASP ASVS)
  • V5.3.2

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 partial match
blocks

Secure SDLC practices directly require input validation and path sanitization that prevent relative traversal.

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.

removes
removes
removes
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References