Update docs with warning about pickle materializer insecurity (zenml-…

…io#2561)

* update docs with warning about pickle insecurity

* Apply suggestions from code review

Co-authored-by: Alex Strick van Linschoten <strickvl@users.noreply.github.com>

---------

Co-authored-by: Alex Strick van Linschoten <strickvl@users.noreply.github.com>

docs/book/user-guide/advanced-guide/data-management/artifact-versioning.md

@@ -32,6 +32,12 @@ By tracking the lineage of artifacts across environments and stacks, ZenML enabl
 
 Materializers are designed to be extensible and customizable, allowing you to define your own serialization and deserialization logic for specific data types or storage systems. By default, ZenML provides built-in materializers for common data types and uses `cloudpickle` to pickle objects where there is no default materializer. If you want direct control over how objects are serialized, you can easily create custom materializers by extending the `BaseMaterializer` class and implementing the required methods for your specific use case. Read more about materializers [here](handle-custom-data-types.md).
 
+{% hint style="warning" %}
+ZenML provides a built-in [CloudpickleMaterializer](https://sdkdocs.zenml.io/latest/core\_code\_docs/core-materializers/#zenml.materializers.cloudpickle\_materializer.CloudpickleMaterializer) that can handle any object by saving it with [cloudpickle](https://github.com/cloudpipe/cloudpickle). However, this is not production-ready because the resulting artifacts cannot be loaded when running with a different Python version. In such cases, you should consider building a [custom Materializer](handle-custom-data-types.md#custom-materializers) to save your objects in a more robust and efficient format.
+
+Moreover, using the `CloudpickleMaterializer` could allow users to upload of any kind of object. This could be exploited to upload a malicious file, which could execute arbitrary code on the vulnerable system.
+{% endhint %}
+
 When a pipeline runs, ZenML uses the appropriate materializers to save and load artifacts using the ZenML `fileio` system (built to work across multiple artifact stores). This not only simplifies the process of working with different data formats and storage systems but also enables artifact caching and lineage tracking. You can see an example of a default materializer (the `numpy` materializer) in action [here](https://github.com/zenml-io/zenml/blob/main/src/zenml/materializers/numpy\_materializer.py).
 
 <!-- For scarf -->

docs/book/user-guide/advanced-guide/data-management/handle-custom-data-types.md

@@ -14,8 +14,10 @@ ZenML already includes built-in materializers for many common data types. These
 
 <table data-full-width="true"><thead><tr><th>Materializer</th><th>Handled Data Types</th><th>Storage Format</th></tr></thead><tbody><tr><td><a href="https://sdkdocs.zenml.io/latest/core_code_docs/core-materializers/#zenml.materializers.built_in_materializer.BuiltInMaterializer">BuiltInMaterializer</a></td><td><code>bool</code>, <code>float</code>, <code>int</code>, <code>str</code>, <code>None</code></td><td><code>.json</code></td></tr><tr><td><a href="https://sdkdocs.zenml.io/latest/core_code_docs/core-materializers/#zenml.materializers.built_in_materializer.BytesMaterializer">BytesInMaterializer</a></td><td><code>bytes</code></td><td><code>.txt</code></td></tr><tr><td><a href="https://sdkdocs.zenml.io/latest/core_code_docs/core-materializers/#zenml.materializers.built_in_materializer.BuiltInContainerMaterializer">BuiltInContainerMaterializer</a></td><td><code>dict</code>, <code>list</code>, <code>set</code>, <code>tuple</code></td><td>Directory</td></tr><tr><td><a href="https://sdkdocs.zenml.io/latest/core_code_docs/core-materializers/#zenml.materializers.numpy_materializer.NumpyMaterializer">NumpyMaterializer</a></td><td><code>np.ndarray</code></td><td><code>.npy</code></td></tr><tr><td><a href="https://sdkdocs.zenml.io/latest/core_code_docs/core-materializers/#zenml.materializers.pandas_materializer.PandasMaterializer">PandasMaterializer</a></td><td><code>pd.DataFrame</code>, <code>pd.Series</code></td><td><code>.csv</code> (or <code>.gzip</code> if <code>parquet</code> is installed)</td></tr><tr><td><a href="https://sdkdocs.zenml.io/latest/core_code_docs/core-materializers/#zenml.materializers.pydantic_materializer.PydanticMaterializer">PydanticMaterializer</a></td><td><code>pydantic.BaseModel</code></td><td><code>.json</code></td></tr><tr><td><a href="https://sdkdocs.zenml.io/latest/core_code_docs/core-materializers/#zenml.materializers.service_materializer.ServiceMaterializer">ServiceMaterializer</a></td><td><code>zenml.services.service.BaseService</code></td><td><code>.json</code></td></tr><tr><td><a href="https://sdkdocs.zenml.io/latest/core_code_docs/core-materializers/#zenml.materializers.structured_string_materializer.StructuredStringMaterializer">StructuredStringMaterializer</a></td><td><code>zenml.types.CSVString</code>, <code>zenml.types.HTMLString</code>, <code>zenml.types.MarkdownString</code></td><td><code>.csv</code> / <code>.html</code> / <code>.md</code> (depending on type)</td></tr></tbody></table>
 
-{% hint style="info" %}
-ZenML also provides a built-in [CloudpickleMaterializer](https://sdkdocs.zenml.io/latest/core\_code\_docs/core-materializers/#zenml.materializers.cloudpickle\_materializer.CloudpickleMaterializer) that can handle any object by saving it with [cloudpickle](https://github.com/cloudpipe/cloudpickle). However, this is not production-ready because the resulting artifacts cannot be loaded when running with a different Python version. In such cases, you should consider building a [custom Materializer](handle-custom-data-types.md#custom-materializers) to save your objects in a more robust and efficient format.
+{% hint style="warning" %}
+ZenML provides a built-in [CloudpickleMaterializer](https://sdkdocs.zenml.io/latest/core\_code\_docs/core-materializers/#zenml.materializers.cloudpickle\_materializer.CloudpickleMaterializer) that can handle any object by saving it with [cloudpickle](https://github.com/cloudpipe/cloudpickle). However, this is not production-ready because the resulting artifacts cannot be loaded when running with a different Python version. In such cases, you should consider building a [custom Materializer](handle-custom-data-types.md#custom-materializers) to save your objects in a more robust and efficient format.
+
+Moreover, using the `CloudpickleMaterializer` could allow users to upload of any kind of object. This could be exploited to upload a malicious file, which could execute arbitrary code on the vulnerable system.
 {% endhint %}
 
 ## Integration Materializers

docs/book/user-guide/advanced-guide/pipelining-features/managing-steps.md

@@ -13,6 +13,12 @@ Your functions will work as ZenML steps even if you don't provide any type annot
 * **Type validation of your step inputs**: ZenML makes sure that your step functions receive an object of the correct type from the upstream steps in your pipeline.
 * **Better serialization**: Without type annotations, ZenML uses [Cloudpickle](https://github.com/cloudpipe/cloudpickle) to serialize your step outputs. When provided with type annotations, ZenML can choose a [materializer](../../../getting-started/core-concepts.md#materializers) that is best suited for the output. In case none of the builtin materializers work, you can even [write a custom materializer](../data-management/handle-custom-data-types.md).
 
+{% hint style="warning" %}
+ZenML provides a built-in [CloudpickleMaterializer](https://sdkdocs.zenml.io/latest/core\_code\_docs/core-materializers/#zenml.materializers.cloudpickle\_materializer.CloudpickleMaterializer) that can handle any object by saving it with [cloudpickle](https://github.com/cloudpipe/cloudpickle). However, this is not production-ready because the resulting artifacts cannot be loaded when running with a different Python version. In such cases, you should consider building a [custom Materializer](handle-custom-data-types.md#custom-materializers) to save your objects in a more robust and efficient format.
+
+Moreover, using the `CloudpickleMaterializer` could allow users to upload of any kind of object. This could be exploited to upload a malicious file, which could execute arbitrary code on the vulnerable system.
+{% endhint %}
+
 ```python
 from typing import Tuple
 from zenml import step