Client-side code execution via unescaped dataset schema
Published Feb 23, 2024 · Updated Aug 22, 2024
Cross-site scripting in MLflow 2.9.2 and earlier allows remote attackers to execute code in Jupyter via an untrusted dataset. The Recipes ingest step passes schema fields through BaseCard.render_table and inserts the result into the Data Schema template without Jinja escaping. A user must run and inspect a recipe that consumes the attacker-controlled dataset, after which injected markup executes in the Jupyter client context.
Summary
What happened
Cross-site scripting in MLflow 2.9.2 and earlier allows remote attackers to execute code in Jupyter via an untrusted dataset. The Recipes ingest step passes schema fields through BaseCard.render_table and inserts the result into the Data Schema template without Jinja escaping. A user must run and inspect a recipe that consumes the attacker-controlled dataset, after which injected markup executes in the Jupyter client context.
The record
- CVE
- CVE-2024-27133
- Published
- Feb 23, 2024
- Updated
- Aug 22, 2024
- Vendor
- MLflow Project
- Product
- MLflow
- Classifications
- CWE-79, T1204.002
- Attack vector
- network
- Privileges
- unauthenticated
Timeline
How it unfolded
- Feb 23, 2024CVE publishedPublication date reported by the CVE source.
- Aug 22, 2024Record updatedLatest update available in the CVE record.
Exploitability
Present is not the same as exploitable
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Is a vulnerable build present?
Compare these published version ranges with your installed build and any vendor patches.
- Affected versionversion=0 <=2.9.2
What conditions does exploitation require?
What is affected?
Attacks
What attackers are doing with it
Daily unique IPs observed by Shadowserver honeypots for known exploited vulnerabilities (KEVs). Missing observations do not establish an absence of attacks.
Weakness, pattern, technique
Public exploit references
- JFrog untrusted-dataset Recipe PoCproof of concept · demonstrated
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Technologies
Your stack
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Your stack
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