Security Bulletin
Summary
WML containers include scikit-learn. Scikit-learn includes joblib and pickle to cache and load models. Pickle (and joblib by extension), has some issues regarding maintainability and security. Because of this, usage of the joblib.load() function in scikit-learn must be done in a responsible manner.
Vulnerability Details
Refer to the security bulletin(s) listed in the Remediation/Fixes section
Affected Products and Versions
| Affected Product(s) | Version(s) |
| IBM Watson Machine Learning Community Edition | 1.6.2 |
| IBM Watson Machine Learning Community Edition | 1.7.0 |
Workarounds and Mitigations
- Never unpickle untrusted data as it could lead to malicious code being executed upon loading.
- While models saved using one version of scikit-learn might load in other versions, this is entirely unsupported and inadvisable. It should also be kept in mind that operations performed on such data could give different and unexpected results.
- The training data, e.g. a reference to an immutable snapshot
- The python source code used to generate the model
- The versions of scikit-learn and its dependencies
- The cross validation score obtained on the training data
This should make it possible to check that the cross-validation score is in the same range as before.
Since a model internal representation may be different on two different architectures, dumping a model on one architecture and loading it on another architecture is not supported.
Get Notified about Future Security Bulletins
References
Change History
08 Jun 2020: Initial Publication
*The CVSS Environment Score is customer environment specific and will ultimately impact the Overall CVSS Score. Customers can evaluate the impact of this vulnerability in their environments by accessing the links in the Reference section of this Security Bulletin.
Disclaimer
Review the IBM security bulletin disclaimer and definitions regarding your responsibilities for assessing potential impact of security vulnerabilities to your environment.
Document Location
Worldwide
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Document Information
Modified date:
20 July 2020
Initial Publish date:
08 June 2020
UID
ibm16250929