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CVE-2023-6977
MLflow Local File Disclosure Vulnerability
Summary
MLflow Local File Disclosure Vulnerability
AI summary snake-internal / snake-template-v1
A weakness called CVE-2023-6977 was discovered in MLflow Local File Disclosure.
Severity is Info. Low severity or not yet rated.
What you should do: update the affected software to the latest version. If unsure, ask your IT team or search the vendor's site for "MLflow Local File Disclosure CVE-2023-6977".
CVE-2023-6977 (MLflow Local File Disclosure). Severity: Info. Category: CWE-29.
Response plan:
1. Check the vendor advisory for affected versions and the patched release.
2. If a vulnerable version is running in production, schedule maintenance (urgency from KEV/CVSS).
3. If no patch yet, mitigate via WAF rule, disabling the affected feature, etc.
4. Monitor logs / SIEM for known IOC and PoC signatures of this CVE.
PoCs and fix commits: see the 'References' section, MITRE, and NVD.
❓ What is the problem
A weakness (CVE-2023-6977) in MLflow Local File Disclosure. A serious software flaw has been identified.
📍 Affected scope
Target versions of MLflow Local File Disclosure (see vendor advisory). If running in production, identify exposure immediately.
🔥 Severity
Severity: Info. Low severity or not yet rated.
🔧 How to fix
Update to the patched release as listed in the vendor advisory. (Typical mitigation pattern for CWE-29)
🛡️ Workaround
If a patch is not yet available, consider disabling the affected feature, applying WAF rules, blocking via network ACLs, or isolating the vulnerable version.
🔍 Detection
Check version information, scan dependencies via SBOM, and monitor SIEM for IOC and PoC signatures related to this CVE.
Affected packages
Bitnami
mlflow
[{"type":"SEMVER","events":[{"introduced":"1.0.0"},{"fixed":"2.9.2"}]}]
PyPI
mlflow
[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.9.2"}]}]
References
- advisory https://nvd.nist.gov/vuln/detail/CVE-2023-6977
- advisory https://github.com/advisories/GHSA-qg8p-32gr-gh6x
- package https://github.com/mlflow/mlflow
- package https://pypi.org/project/mlflow
- web https://github.com/mlflow/mlflow/commit/4bd7f27c810ba7487d53ed5ef1038fca0f8dc28c
- web https://huntr.com/bounties/fe53bf71-3687-4711-90df-c26172880aaf