MLflow: Deserialization of Untrusted Data
CVE-2026-96804 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 attacker with a low-privileged account, without any action from a user. The record lists version 3.15.0 as the first without the flaw, so updating MLflow to that release or later removes the exposure. There is no confirmed exploitation so far. EPSS estimates a 0.75% chance of exploitation within 30 days, higher than 55% of scored CVEs.
High severity with no evidence of exploitation so far.
What to do
- Update MLflow to 3.15.0 or a later version.
Affected products and versions
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
- LowA basic user account is enough.
- User interaction
- NoneNo action by a victim is required.
- 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:L/UI:N/S:U/C:H/I:H/A:H. Scored by CISA (ADP).
Official description
MLflow's statsmodel flavor, versions 2.1.0 to 3.14.0, omits the MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False security control entirely in _load_model(), which allows a remote attacker to execute arbitrary code via a crafted MLmodel artifact.
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: none, automatable: no, technical impact: total).
- Record last changed materially (score, affected products, status or references).
References
Other references
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
- 8.8 High severity
- 8.8 High severity
- 9.8 Critical severity
- 9.8 Critical severity
- 8.8 High severity
- 8.8 High severity
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