Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:NCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2026-2614 is a high-severity Path Traversal (CWE-22) 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 14% 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 Platforms; in the Privacy and Disclosure risk domain.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-29180
Vulnerability Data
A vulnerability in the `_create_model_version()` handler of `mlflow/server/handlers.py` in mlflow/mlflow versions 3.9.0 and earlier allows an unauthenticated remote attacker to read arbitrary files from the server's filesystem. The issue arises when a `CreateModelVersion` request includes the tag `mlflow.prompt.is_prompt`, which bypasses…
more
source path validation. This enables an attacker to store an arbitrary local filesystem path as the model version source. The `get_model_version_artifact_handler()` function later uses this source to serve files without verifying the model version's prompt status, leading to a complete confidentiality compromise. This issue is fixed in version 3.10.0.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other Platforms
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: mlflow
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
—
—
—
V5.3.2
Mitigating Controls (NIST 800-53 r5) AI
Enforces the intended directory access authorizations that path traversal would otherwise bypass.
Input validation directly neutralizes special path elements before pathname construction occurs.
Least privilege reduces the impact of any unauthorized file access obtained via traversal.
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.
Patching/maintenance can remediate known path-traversal flaws in deployed software (partial prevention of exploitability) but does nothing to stop the coding defect from being introduced in the first place.
PR.AA-05 defines and reviews access policies but does not address code-level pathname neutralization, so neither direction prevents CWE-22.
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.
Security testing in development catches path traversal via static/dynamic analysis.
Secure SDLC mandates input validation and path sanitization that directly prevent path traversal.
Application security requirements include rules for safe file handling and canonicalization.
Secure architecture principles require least-privilege file access and directory isolation.
Secure coding standards explicitly forbid unsafe path construction and mandate safe APIs.
Information access restriction limits which files an application may read or write.