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CVE-2023-43472

47
FAUCET Score

CVE-2023-43472 is a high-severity information disclosure vulnerability affecting MLFlow versions 2.8.1 and earlier, allowing remote attackers to obtain sensitive information through crafted REST API requests. With a CVSS score of 7.5, this vulnerability is easily exploitable over the network with no user interaction, leading to a high impact on confidentiality. While not currently in CISA's KEV catalog, public Nuclei templates exist for exploitation, and it has garnered significant community discussion and media coverage, indicating active awareness and potential for future exploitation.

Impacted Technologies

VendorProductVersion(s)CPE
<= 2.8.1CPE matchmatch criteria
cpe:2.3:a:lfprojects:mlflow:*:*:*:*:*:*:*:*

CVSS Data

CVSS version used by this source: 3.1

7.5HIGH

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

Attack Vector
NETWORK
Attack Complexity
LOW
Privileges Required
NONE
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
HIGH
Integrity Impact
NONE
Availability Impact
NONE
Exploitability Score
3.9
Impact Score
3.6
CvssVersion
3.1

Exploit Intelligence

EPSS Score
36.58%
Probability of exploitation in next 30 days
EPSS Percentile
98.3%
Percentile rank of EPSS score among Peer Group
As of 2026-07-27
Model: v2026.06.15
Nuclei: CVE-2023-43472 · Jun 7, 2024
This CVE's current EPSS score of 0.3658 is in the 97th percentile among its peer group of 51,506 CVEs.

Social Chatter

No social media mentions found for this CVE.

The average CVE in this peer group has 0.0 Twitter, 0.0 Reddit, 0.1 Bluesky, 0.1 Mastodon, and 0.4 GitHub mentions.

Media Mentions

The average CVE in this peer group has 0.1 InfoSec Media, 0.0 Vendor Blog, and 0.0 Security Researcher mentions.

Remediation

Patch Available

Vendor Patches (1)

pippatch availablevia ghsa
Product: mlflowFixed in: 2.9.0

Vendor Advisories (1)

pipGHSA-wqxf-447m-6f5fhigh

Information exposure in MLflow

Dec 5, 2023

References

contrastsecurity.com / security-influencers/discovering-mlflow-framework-zero-day-vulnerability-machine-language-model-security-contrast-security
ExploitPress/Media Coverage