Security Digest · Vulnerability record
CVE-2026-0596: Mlflow: Command Injection when serving models with enable_mlserver=True
A command injection vulnerability exists in mlflow/mlflow when serving a model with enablemlserver=True.
CVSS 9.6 Critical EPSS 1.3% · p71
Should you act?
Critical, preconditions apply
Severe if exploited, but exploitation needs conditions that may not apply to you. This signal is computed from CISA KEV, EPSS and CVSS data; it is not an editorial verdict.
- Not in CISA KEV, but CISA’s SSVC assessment records a public proof-of-concept, so attack code exists.
- EPSS 1.3%, percentile 71: FIRST’s model estimates a 1.3% chance of exploitation activity in the next 30 days, higher than 71% of all scored CVEs (moderate). EPSS is a probability, not a verdict.
- Only applies if your dependency tree includes
mlflow(PyPI) < 3.9.0, fixed in 3.9.0. Check your lockfile, including transitive dependencies.
What it is
A command injection vulnerability exists in mlflow/mlflow when serving a model with enablemlserver=True. The modeluri is embedded directly into a shell command executed via bash -c without proper sanitization. If the modeluri contains shell metacharacters, such as $() or backticks, it allows for command substitution and execution of attacker-controlled commands. This vulnerability affects the latest version of mlflow/mlflow and can lead to privilege escalation if a higher-privileged service serves models from a directory writable by lower-privileged users.
From the GitHub advisory
A command injection vulnerability exists in Mlflow when serving a model with enablemlserver=True. The modeluri is embedded directly into a shell command executed via bash -c without proper sanitization. If the modeluri contains shell metacharacters, such as $() or backticks, it allows for command substitution and execution of attacker-controlled commands. This vulnerability affects the latest version of mlflow/mlflow and can lead to privilege escalation if a higher-privileged service serves models from a directory writable by lower-privileged users.
Weakness type: CWE-78.
Product: mlflow (PyPI) · Assigned by: @huntr_ai · Published: 2026-03-31
Affected and fixed versions
| Ecosystem | Package | Vulnerable | First fixed |
|---|---|---|---|
| PyPI | mlflow | < 3.9.0 | 3.9.0 |
| Vendor | Product | Affected versions (per CVE record) |
|---|---|---|
| mlflow | mlflow/mlflow | unspecified <= latest |
Severity scores
| Scored by | CVSS | Score | Vector |
|---|---|---|---|
| @huntr_ai | 3.0 | 9.6 Critical | CVSS:3.0/AV:A/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H |
| GitHub | 3.1 | 9.6 | CVSS:3.0/AV:A/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H |
- Attack vector: adjacent network only
- Attack complexity: low
- Privileges required: none (no login)
- User interaction: none
- Scope: changed (can affect other components)
CVSS measures how bad a flaw is if exploited, not how likely exploitation is. Read it next to the exploitation data below.
Exploitation and urgency
- CISA KEV: not listed (no confirmed in-the-wild exploitation known to CISA).
- CISA SSVC assessment: exploitation poc, automatable no, technical impact total.
- FIRST EPSS (model of 2026-10-21): 1.33% probability of exploitation activity in the next 30 days; percentile 71.
Timeline
- — CVE record published by @huntr_ai
- — GitHub-reviewed advisory GHSA-rvhj-8chj-8v3c published (critical)
- — CVE record last updated
Sources and references
Original sources first. Version and fix details should always be confirmed against the vendor or project advisory.
Vulnerability records
- GitHub Advisory GHSA-rvhj-8chj-8v3c github.com
- CVE.org record CVE-2026-0596 cve.org
- NVD entry CVE-2026-0596 nvd.nist.gov
Other references from the CVE record and advisory
- huntr.com huntr.com
- github.com github.com
- github.com github.com