MLflow: Deserialization of Untrusted Data
CVE-2024-37057 is a high-severity deserialization of untrusted data vulnerability in MLflow, with a CVSS 3.1 base score of 8.8 out of 10. It can be exploited remotely over the network by an unauthenticated attacker, but only if a user takes part, for example by opening a link or file. Affected MLflow versions: 2.0.0rc0 and later. The record does not name a fixed version, so check the vendor advisory. There is no confirmed exploitation so far. EPSS estimates a 0.62% chance of exploitation within 30 days, higher than 50% of scored CVEs.
High severity with no evidence of exploitation so far.
What to do
- Check which version of MLflow you run against the affected versions below, and install the vendor's latest release.
- Read the advisory on hiddenlayer.com for workarounds and exact fixed builds.
- Until you can update, restrict network access to the affected service so it is not reachable from untrusted networks.
- Because the attack needs a user to take part, warn users about unexpected links and files related to this product until it is patched.
Affected products and versions
| Vendor | Product | Affected versions | Fixed in |
|---|---|---|---|
| MLflow | MLflow | 2.0.0rc0 and later | Not stated |
| Lfprojects | mlflow | 2.0.0rc0 and later | Not stated |
Version data comes from the organisation that assigned the CVE. Confirm against the vendor advisory before deciding you are unaffected.
How the severity score breaks down
- Attack vector
- NetworkCan be exploited remotely over a network, including the internet.
- Attack complexity
- LowNo special conditions are needed; the attack works reliably.
- Privileges required
- NoneNo account or login is needed.
- User interaction
- RequiredA user must take an action such as opening a file or link.
- Scope
- UnchangedImpact stays within the vulnerable component.
- Confidentiality
- HighAll data handled by the component can be read.
- Integrity
- HighData or code can be modified at will.
- Availability
- HighThe component can be taken down completely.
Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H. Scored by NVD.
| Scoring system | Score | Severity | Scored by |
|---|---|---|---|
| CVSS 3.1 | 8.8 | High | CNA |
| CVSS 3.1 | 8.8 | High | NVD |
Official description
Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.0.0rc0 or newer, enabling a maliciously uploaded Tensorflow model to run arbitrary code on an end user’s system when interacted with.
Source: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Deserialization of Untrusted Data
The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
How developers prevent this class of flaw
- If available, use the signing/sealing features of the programming language to assure that deserialized data has not been tainted. For example, a hash-based message authentication code (HMAC) could be used to ensure that data has not been modified.
- When deserializing data, populate a new object rather than just deserializing. The result is that the data flows through safe input validation and that the functions are safe.
- Explicitly define a final object() to prevent deserialization.
More Deserialization of Untrusted Data vulnerabilities (CWE-502)
Timeline
- CVE record published.
- CISA published its SSVC assessment (exploitation: poc, automatable: no, technical impact: total).
- Record last changed materially (score, affected products, status or references).
References
Advisories
- hiddenlayer.com/sai-security-advisory/mlflow-june2024Exploit, Third Party Advisory
Other vulnerabilities in MLflow
- 8.2 High severity
- 7.8 High severity
- 7.8 High severity
- 9.8 Critical severity
- 7.5 High severity
- 8.2 High severity
Recent Deserialization of Untrusted Data vulnerabilities
- 7.8 High severity
- 7.8 High severity
- 7.8 High severity
- 8.8 High severity
- 8.8 High severity
- 8.8 High severity
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