Security Considerations#
This document describes security considerations when reading Arrow
data from untrusted sources. It focuses specifically on data passed in a
standardized serialized form (such as a IPC stream), as opposed to an
implementation-specific native representation (such as arrow::Array in C++).
Note
Implementation-specific concerns, such as bad API usage, are out of scope for this document. Please refer to the implementation’s own documentation.
Who should read this#
You should read this document if you belong to either of these two categories:
users of Arrow: that is, developers of third-party libraries or applications that don’t directly implement the Arrow formats or protocols, but instead call language-specific APIs provided by an Arrow library (as defined below);
implementors of Arrow libraries: that is, libraries that provide APIs abstracting away from the details of the Arrow formats and protocols; such libraries include, but are not limited to, the official Arrow implementations documented on https://arrow.apache.org.
Columnar Format#
Invalid data#
The Arrow columnar format is an efficient binary representation with a focus on performance and efficiency. While the format does not store raw pointers, the contents of Arrow buffers are often combined and converted to pointers into the process’ address space. Invalid Arrow data may therefore cause invalid memory accesses (potentially crashing the process) or access to non-Arrow data (potentially allowing an attacker to exfiltrate confidential information).
For instance, to read a value from a Binary array, one needs to 1) read the values’ offsets from the array’s offsets buffer, and 2) read the range of bytes delimited by these offsets in the array’s data buffer. If the offsets are invalid (deliberately or not), then step 2) can access memory outside of the data buffer’s range.
Another instance of invalid data lies in the values themselves. For example, a String array is only allowed to contain valid UTF-8 data, but an untrusted source might have emitted invalid UTF-8 under the disguise of a String array. An unsuspecting algorithm that is only specified for valid UTF-8 inputs might lead to dangerous behavior (for example by reading memory out of bounds when looking for an UTF-8 character boundary).
Fortunately, knowing its schema, it is possible to validate Arrow data up front, so that reading this data will not pose any danger later on.
Advice for users#
Arrow implementations often assume inputs follow the specification to provide high speed processing. It is extremely recommended that your application explicitly validates any Arrow data it receives under serialized form from untrusted sources. Many Arrow implementations provide explicit APIs to perform such validation.
Advice for implementors#
It is recommended that you provide dedicated APIs to validate Arrow arrays and/or record batches. Users will be able to utilize those APIs to assert whether data coming from untrusted sources can be safely accessed.
A typical validation API must return a well-defined error, not crash, if the given Arrow data is invalid; it must always be safe to execute regardless of whether the data is valid or not.
Uninitialized data#
A less obvious pitfall is when some parts of an Arrow array are left uninitialized. For example, if an element of a primitive Arrow array is marked null through its validity bitmap, the corresponding value slot in the values buffer can be ignored for all purposes. It is therefore tempting, when creating an array with null values, to not initialize the corresponding value slots.
However, this then introduces a serious security risk if the Arrow data is serialized and published (e.g. using IPC or Flight) such that it can be accessed by untrusted users. Indeed, the uninitialized value slot can reveal data left by a previous memory allocation made in the same process. Depending on the application, this data could contain confidential information.
Advice for users and implementors#
When creating a Arrow array, it is recommended that you never leave any data uninitialized in a buffer if the array might be sent to, or read by, an untrusted third-party, even when the uninitialized data is logically irrelevant. The easiest way to do this is to zero-initialize any buffer that will not be populated in full.
If it is determined, through benchmarking, that zero-initialization imposes an excessive performance cost, a library or application may instead decide to use uninitialized memory internally as an optimization; but it should then ensure all such uninitialized values are cleared before passing the Arrow data to another system.
Note
Sending Arrow data out of the current process can happen indirectly, for example if you produce it over the C Data Interface and the consumer persists it using the IPC format on some public storage.
C Data Interface#
The C Data Interface contains raw pointers into the process’ address space. It is generally not possible to validate that those pointers are legitimate; read from such a pointer may crash or access unrelated or bogus data.
Advice for users#
You should never consume a C Data Interface structure from an untrusted producer, as it is by construction impossible to guard against dangerous behavior in this case.
Advice for implementors#
When consuming a C Data Interface structure, you can assume that it comes from a trusted producer, for the reason explained above. However, it is still recommended that you validate it for soundness (for example that the right number of buffers is passed for a given datatype), as a trusted producer can have bugs anyway.
IPC Format#
The IPC format is a serialization format for the columnar format with associated metadata. Reading an IPC stream or file from an untrusted source comes with similar caveats as reading the Arrow columnar format.
The additional signalisation and metadata in the IPC format come with their own risks. For example, buffer offsets and sizes encoded in IPC messages may be out of bounds for the IPC stream; Flatbuffers-encoded metadata payloads may carry incorrect offsets pointing outside of the designated metadata area.
Advice for users#
Arrow libraries will typically ensure IPC streams are structurally valid but may not also validate the underlying Array data. It is extremely recommended that you use the appropriate APIs to validate the Arrow data read from an untrusted IPC stream.
Advice for implementors#
It is extremely recommended to run dedicated validation checks when decoding the IPC format, to make sure that the decoding can not induce unwanted behavior. Failing those checks should return a well-known error to the caller, not crash.
Extension Types#
Extension types typically register a custom deserialization hook so that they can be automatically recreated when reading from an external source (for example using IPC). The deserialization hook has to decode the extension type’s parameters from a string or binary payload specific to the extension type. Typical examples use a bespoke JSON representation with object fields representing the various parameters.
When reading data from an untrusted source, any registered deserialization hook could be called with an arbitrary payload. It is therefore of primary importance that the hook be safe to call on invalid, potentially malicious, data. This mandates the use of a robust metadata serialization schema (such as JSON, but not Python’s pickle or R’s serialize(), for example).
Advice for users and implementors#
When designing an extension type, it is extremely recommended to choose a metadata serialization format that is robust against potentially malicious data.
When implementing an extension type, it is recommended to ensure that the deserialization hook is able to detect, and error out gracefully, if the serialized metadata payload is invalid.
Testing for robustness#
Advice for implementors#
For APIs that may process untrusted inputs, it is extremely recommended that your unit tests exercise your APIs against typical kinds of invalid data. For example, your validation APIs will have to be tested against invalid Binary or List offsets, invalid UTF-8 data in a String array, etc.
Testing against known regression files#
The arrow-testing repository contains regression files for various formats, such as the IPC format.
Two categories of files are especially noteworthy and can serve to exercise an Arrow implementation’s robustness:
gold integration files that are valid files to exercise compliance with Arrow IPC features;
fuzz regression files that have been automatically generated each time a fuzzer founds a bug triggered by a specific (usually invalid) input for a given format.
Fuzzing#
It is recommended that you go one step further and set up some kind of automated robustness testing against unforeseen inputs. One typical approach is though fuzzing, possibly coupled with a runtime instrumentation framework that detects dangerous behavior (such as Address Sanitizer in C++ or Rust).
A reasonable way of setting up fuzzing for Arrow is using the IPC format as a binary payload; the fuzz target should not only attempt to decode the IPC stream as Arrow data, but it should then validate the Arrow data. This will strengthen both the IPC decoder and the validation routines against invalid, potentially malicious data. Finally, if validation comes out successfully, the fuzz target may exercise some important core functionality, such as printing the data for human display; this will help ensure that the validation routine did not let through invalid data that may lead to dangerous behavior.
Non-Arrow formats and protocols#
Arrow data can also be sent or stored using third-party formats such as Apache Parquet. Those formats may or may not present the same security risks as listed above (for example, the precautions around uninitialized data may not apply in a format like Parquet that does not create any value slots for null elements). We suggest you refer to these projects’ own documentation for more concrete guidelines.