CVE-2026-0596
Command Injection in mlflow Model Serving Enables Privilege Escalation
Publication date: 2026-03-31
Last updated on: 2026-04-14
Assigner: huntr.dev
Description
Description
CVSS Scores
EPSS Scores
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Meta Information
Affected Vendors & Products
| Vendor | Product | Version / Range |
|---|---|---|
| lfprojects | mlflow | * |
Helpful Resources
Exploitability
| CWE ID | Description |
|---|---|
| CWE-78 | The product constructs all or part of an OS command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended OS command when it is sent to a downstream component. |
Attack-Flow Graph
AI Powered Q&A
Can you explain this vulnerability to me?
This vulnerability is a command injection issue in mlflow/mlflow when serving a model with the option `enable_mlserver=True`. The problem arises because the `model_uri` is directly embedded into a shell command executed via `bash -c` without proper sanitization. If the `model_uri` contains shell metacharacters like `$()` or backticks, it can lead to command substitution, allowing an attacker to execute arbitrary commands.
This means that an attacker can inject malicious commands through the `model_uri` parameter, potentially gaining unauthorized control over the system.
How can this vulnerability impact me? :
This vulnerability can have severe impacts including privilege escalation. If a higher-privileged service serves models from a directory writable by lower-privileged users, an attacker can exploit this flaw to execute commands with elevated privileges.
The CVSS score of 9.6 indicates a critical severity, meaning it can lead to complete compromise of confidentiality, integrity, and availability of the affected system.
What immediate steps should I take to mitigate this vulnerability?
To mitigate this vulnerability, avoid serving models with the `enable_mlserver=True` option until a fix is applied.
Ensure that the `model_uri` does not contain shell metacharacters such as `$()` or backticks to prevent command injection.
Restrict write permissions on directories from which models are served, especially preventing lower-privileged users from writing to directories used by higher-privileged services.
Monitor for updates or patches from the mlflow/mlflow project and apply them promptly.