Security readout for executives and security teams
Plain-English summary
MLflow 3.8.0 can run unintended operating-system commands when serving a malicious model artifact. The risk appears highest where teams accept or automatically deploy model artifacts from users, partners, CI pipelines, or shared registries. The source bundle says version 3.8.2 fixes the issue.
Executive priority
Treat this as urgent for ML platforms that deploy user-supplied or pipeline-generated MLflow artifacts. Prioritize patching and artifact trust controls because successful abuse could give an attacker command execution in serving infrastructure.
Technical view
The flaw is command injection in MLflow model serving container initialization. With env_manager=LOCAL, _install_model_dependencies_to_env() reads dependency entries from python_env.yaml inside a model artifact and interpolates them into a shell command without sanitization, enabling arbitrary command execution when the artifact is deployed.
Likely exposure
Organizations using mlflow/mlflow 3.8.0 for model serving are most exposed, especially if model artifacts can be supplied by less-trusted users or automated pipelines. Exposure depends on using env_manager=LOCAL and deploying artifacts whose python_env.yaml is attacker-controlled.
Exploitation context
The bundle does not show KEV listing or confirmed active exploitation. It describes a low-complexity, unauthenticated path in the CVSS vector, but practical exploitation requires getting a malicious model artifact deployed by a vulnerable MLflow serving workflow.
Researcher notes
Do not broaden affected versions beyond the bundle evidence. The described sink is shell command construction from python_env.yaml dependency data during local environment handling. Validate by code review, version checks, and deployment-path analysis rather than offensive reproduction.
Mitigation direction
- Upgrade affected MLflow deployments to version 3.8.2 or later.
- Avoid deploying untrusted model artifacts with env_manager=LOCAL.
- Restrict who can publish artifacts consumed by production serving systems.
- Review vendor and downstream distributor advisories for environment-specific guidance.
- Add artifact provenance checks before automated model deployment.
Validation and detection
- Inventory MLflow deployments and identify any running version 3.8.0.
- Find serving workflows that use env_manager=LOCAL.
- Review model artifact sources and recent python_env.yaml changes.
- Confirm deployed packages include the fixed MLflow release or vendor backport.
- Check logs for unexpected dependency installation behavior around model deployment.
Public sources used
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
Conservative CVE-to-ATT&CK context
These mappings and lookup hints may be relevant to the vulnerability behavior, CWE, affected product, or exposure path. Glexia-inferred context is not an official MITRE, ATT&CK, CWE, or CVE Program mapping.
ATT&CK lookup starting points
Use these exact CWE pages and searches to review the Glexia ATT&CK library from this CVE's weakness and description context.
CWE-77: Command execution behavior lookup
Command injection weaknesses can lead defenders to review execution techniques and command interpreter telemetry. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.
Open ATT&CK lookupCWE-78: Command execution behavior lookup
Command injection weaknesses can lead defenders to review execution techniques and command interpreter telemetry. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.
Open ATT&CK lookupExecution behavior lookup
The CVE wording references code or command execution, so execution technique review may help defensive triage. This is a Glexia inferred lookup path, not an official MITRE, ATT&CK, or CVE Program mapping.
Open ATT&CK lookupContainer behavior lookup
The affected technology mentions containers, so container-specific ATT&CK technique review may help. This is a Glexia inferred lookup path, not an official MITRE, ATT&CK, or CVE Program mapping.
Open ATT&CK lookupCVE-2025-15379 mapping review
Open the CVE-to-ATT&CK bridge for reviewed, inferred, or future official mappings tied to this CVE.
Open ATT&CK lookup- Severity
- Critical
- CVSS
- 10 (3.0)
- Known Exploited
- No
- Published
Vector: CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H
CNA and ADP enrichment extracted from CVE v5
These fields come from the CVE record and ADP containers, not from Glexia's Take. They preserve time-varying source decisions such as CISA SSVC, KEV status, CVSS metrics, and provider references.
SSVC decision data
CVSS vector scores
2 official scoresWe collect every scored CVSS vector available in the official CNA and ADP containers. When more than one version is present, the table keeps the source vectors side by side instead of collapsing them into the highest score.
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H3.96@huntr_aiCVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:H2.36redhat-SADPVulnerability scoring details
Base CVSS 3.1 score
9CriticalVector: CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:H
Base CVSS 3.0 score
10CriticalVector: CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H
Vulnerability timeline
Timeline events are normalized from CVE metadata, CNA source timelines, ADP timelines, and KEV metadata when present.
- CVE reservedCVE Program
The CVE ID was reserved by the assigning CNA.
- ADP timelineredhat-SADP
Made public.
- CVE publishedCVE Program
The CVE record was published.
- ADP timelineredhat-SADP
Reported to Red Hat.
- CVE updatedCVE Program
The CVE record metadata indicates this as the latest update time.
ADP provider summaries
- 2026-03-30T08:01:15.603Z: Reported to Red Hat.
- 2026-03-30T07:16:57.610Z: Made public.
Source materials
- CVE List V5 sourceCVE List V5
- https://huntr.com/bounties/dc9c1c20-7879-4050-87df-4d095fe5ca75CVE reference, @huntr_ai
- https://github.com/mlflow/mlflow/commit/361b6f620adf98385c6721e384fb5ef9a30bb05eCVE reference, @huntr_ai
- https://access.redhat.com/security/cve/CVE-2025-15379CVE reference, redhat-SADP · vdb-entry, x_refsource_REDHAT
- RHBZ#2452949CVE reference, redhat-SADP · issue-tracking, x_refsource_REDHAT
- https://security.access.redhat.com/data/csaf/v2/vex/2025/cve-2025-15379.jsonCVE reference, redhat-SADP · x_sadp-csaf-vex
Products and packages named in the record
CWE details
CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.
Improper Neutralization of Special Elements used in a Command ('Command Injection')
Improper Neutralization of Special Elements used in a Command ('Command Injection') represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.
Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')
Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection') represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.