ZenML: Deserialization of Untrusted Data
CVE-2026-68772 is a high-severity deserialization of untrusted data vulnerability in ZenML, with a CVSS 3.1 base score of 8.0 out of 10. It can be exploited remotely over the network by an attacker with a low-privileged account, but only if a user takes part, for example by opening a link or file. Affected ZenML versions: 0.94.6 and earlier. The record does not name a fixed version, so check the vendor advisory. There is no confirmed exploitation so far. EPSS estimates a 0.76% chance of exploitation within 30 days, higher than 56% of scored CVEs.
High severity with no evidence of exploitation so far.
What to do
- Check which version of ZenML you run against the affected versions below, and install the vendor's latest release.
- 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
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
- 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:L/UI:R/S:U/C:H/I:H/A:H. Scored by CNA (vulncheck.com).
| Scoring system | Score | Severity | Scored by |
|---|---|---|---|
| CVSS 4.0 | 8.5 | High | CNA (vulncheck.com) |
| CVSS 3.1 | 8.0 | High | CNA (vulncheck.com) |
Official description
ZenML 0.94.6 contains a remote code execution vulnerability in the CloudpickleMaterializer component that allows attackers with write access to a shared artifact store to execute arbitrary code by planting a malicious pickle file. Attackers can replace a stored artifact.pkl file with a crafted cloudpickle payload containing a malicious __reduce__ method, which executes arbitrary system commands when any user or pipeline materializes the artifact through the unsanitized cloudpickle.load() call in cloudpickle_materializer.py.
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
Patches and release notes
Other references
Other vulnerabilities in ZenML
- 7.8 High severity
- 7.5 High severity
- 8.8 High severity
Recent Deserialization of Untrusted Data vulnerabilities
- 7.5 High severity
- 9.8 Critical severity
- 9.8 Critical severity
- 9.0 Critical severity
- 7.6 High severity
- 8.8 High severity
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