Security Considerations

Tribuo is a library designed to be incorporated into larger programs. Therefore, we consider the trust boundary to live somewhere outside of Tribuo in the larger program. While we provide data loaders, we expect them to be used on trusted or cleaned data. We expect that the larger program will control access to Tribuo’s interfaces. Tribuo’s data loaders and other interfaces perform defensive copying and other standard procedures when user code crosses over into Tribuo’s internals. For performance reasons, however, this defensive behavior is not generally the case for calls within Tribuo (e.g., the linear algebra library exposes mutable state to reduce copying).

Serialized files

Tribuo models are stored as Java serialized objects. Due to the inherent issues with Java serialization, these object files should only be loaded and saved to trusted locations where third parties do not have access. We have provided a JEP 290 allowlist which will allow the deserialization of only the classes found in the Tribuo library. This allowlist should be enabled on the code paths which deserialize models or datasets. As Tribuo supports Java 8+, and JEP 290 is an addition to the Java 8 API from 8u121, the best way to use the allowlist for the main programs provided with Tribuo is by setting it as a process-wide flag.
Additionally, when running with a security manager, Tribuo will need access to the relevant filesystem locations to load or save model files. See the section on Configuration for more details.

Database access

Tribuo provides a SQL interface that can load data via a JDBC connection. As it’s frequently necessary to load data via a joined query from an unknown schema, Tribuo does not validate the input SQL. It is expected that the program developer will perform this validation since they know the schema from which they are loading. Tribuo supports connections via public key wallets via JDBC. To use this functionality, supply the wallet configuration to the JVM as a system property and use the constructors that accept a java.util.Properties instance with the appropriate configuration.

Native code

Tribuo uses several native libraries via JNI interfaces. Native code has different considerations as compared to pure Java code because native code can cause issues in the running JVM. We are active contributors to all the native ML libraries that Tribuo uses, and we fix issues upstream if we find them. Nevertheless, you should think carefully before running a model that requires native code inside an application container like a JavaEE or JakartaEE server. Multiple instances of Tribuo running inside separate containers may cause issues with JNI library loading due to ClassLoader security considerations.

Configuration

Tribuo uses OLCUT’s configuration and provenance systems, which use reflection to construct and inspect classes. Therefore, when running with a Java security manager, you need to give the OLCUT jar appropriate permissions. We have tested this set of permissions, which allows the configuration and provenance systems to work:

// OLCUT permissions
grant codeBase "file:/path/to/olcut/olcut-core-5.1.4.jar" {
        permission java.lang.RuntimePermission "accessDeclaredMembers";
        permission java.lang.reflect.ReflectPermission "suppressAccessChecks";
        permission java.util.logging.LoggingPermission "control";
        permission java.io.FilePermission "<<ALL FILES>>", "read";
        permission java.util.PropertyPermission "*", "read,write";
};

The read FilePermission can be restricted to the jars which contain configuration files, configuration files on disk, and the locations of serialised objects. The FilePermission in this example provides access to the complete filesystem because the necessary read locations are program specific. This scope should be narrowed based on your requirements. If you need to save an OLCUT configuration, you will also need to add write permissions for the save location.

Similar file read and write permissions are necessary for Tribuo to be able to load and save models; therefore, you’ll need to grant Tribuo those permissions using a similar snippet when running with a security manager.

Threat Model

As a library incorporated into other programs, Tribuo expects it’s inputs to be checked by the wider program; however, there are threats which are specific to ML systems that can result in model or data leakage.

Threat Description Exposed Assets Possible Mitigations
Model replication If an attacker can repeatedly query the model, where they either know or control the features, and they can observe the full prediction (e.g., the complete predicted probability distribution) for each query, then this can provide sufficient information for them to replicate the model. If the model is considered an important asset, allowing an attacker to copy it could be detrimental. The model parameters Only return a small number of predictions (i.e., the top n) or do not provide the probability distribution. This slows down the attack, but does not completely prevent it. Other mitigations such as employing rate limiting or preventing the attacker from controlling or observing the feature inputs are necessary to fully prevent this attack.
Training metadata leak The model file contains information about the training data such as the feature names, number of features, and number of examples. This information is potentially sensitive, as in the case of bigrams or trigrams from text. Training metadata Firstly, treat model files as confidential if the data itself is confidential. Secondly, use Tribuo’s methods for one-way hashing of the feature names. Hashing prevents attackers from trivially discovering the features without needing to complete the process of supplying the input text and testing if the model output changes. Thirdly, other information present in the model file, such as the number of examples, can be redacted by removing the provenance information before the model is deployed.
Training data leak If an attacker can repeatedly query the model, it’s possible for an attacker to find specific training data points that are part of the training data set. This attack is accomplished by measuring the confidence of the prediction (as training data points usually have a predicted confidence close to 1.0). Training data The most important mitigation is to treat model files as confidential if the training data is confidential. Once access to the model has been prevented, the mitigations for model replication apply. This attack is a variant of model replication that usually requires some foreknowledge of the identity of the training corpus.