A command injection vulnerability exists in MLflow's model serving container initialization code, specifically in the `_install_model_dependencies_to_env()` function. When deploying a model with `env_manager=LOCAL`, MLflow reads dependency specifications from the model artifact's `python_env.yaml` file and directly interpolates them into a shell command without sanitization. This allows an attacker to supply a malicious model artifact and achieve arbitrary command execution on systems that deploy the model. The vulnerability affects versions 3.8.0 and is fixed in version 3.8.2.
| Métrique | Valeur | Interprétation |
|---|---|---|
|
AV (Vecteur d'Attaque)
Attack Vector
|
Network (N)
|
Exploit possible à distance via réseau (HTTP, API, etc.) |
|
AC (Complexité d'Attaque)
Attack Complexity
|
Low (L)
|
Peu de conditions requises pour exploiter |
|
PR (Privilèges Requis)
Privileges Required
|
None (N)
|
Aucune authentification requise |
|
UI (Interaction Utilisateur)
User Interaction
|
None (N)
|
Aucune interaction utilisateur nécessaire |
|
S (Périmètre)
Scope
|
Changed (C)
|
Impact s'étend au-delà du composant compromis |
|
C (Impact Confidentialité)
Confidentiality Impact
|
High (H)
|
Divulgation totale d'informations |
|
I (Impact Intégrité)
Integrity Impact
|
High (H)
|
Modification totale des données possible |
|
A (Impact Disponibilité)
Availability Impact
|
High (H)
|
Interruption totale du service possible |
MLflow Command Injection vulnerability