This document discusses how to safely deal with untrusted programs (models or model parameters), and input data. Below, we also provide guidelines on how to report vulnerabilities in TensorFlow.

TensorFlow's runtime system interprets and executes programs. What machine learning practitioners term models are expressed as programs that TensorFlow executes. TensorFlow programs are encoded as computation graphs. The model's parameters are often stored separately in checkpoints.

At runtime, TensorFlow executes the computation graph using the parameters provided. Note that the behavior of the computation graph may change depending on the parameters provided. TensorFlow itself is not a sandbox. When executing the computation graph, TensorFlow may read and write files, send and receive data over the network, and even spawn additional processes. All these tasks are performed with the permission of the TensorFlow process. Allowing for this flexibility makes for a powerful machine learning platform, but it has security implications.

The computation graph may also accept inputs. Those inputs are the data you supply to TensorFlow to train a model, or to use a model to run inference on the data.

TensorFlow models are programs, and need to be treated as such from a security perspective.

As a general rule: Always execute untrusted models inside a sandbox (e.g., nsjail).

There are several ways in which a model could become untrusted. Obviously, if an untrusted party supplies TensorFlow kernels, arbitrary code may be executed. The same is true if the untrusted party provides Python code, such as the Python code that generates TensorFlow graphs.

Even if the untrusted party only supplies the serialized computation graph (in form of a GraphDef, SavedModel, or equivalent on-disk format), the set of computation primitives available to TensorFlow is powerful enough that you should assume that the TensorFlow process effectively executes arbitrary code. One common solution is to allow only a few safe Ops. While this is possible in theory, we still recommend you sandbox the execution.

It depends on the computation graph whether a user provided checkpoint is safe. It is easily possible to create computation graphs in which malicious checkpoints can trigger unsafe behavior. For example, consider a graph that contains a tf.cond depending on the value of a tf.Variable. One branch of the tf.cond is harmless, but the other is unsafe. Since the tf.Variable is stored in the checkpoint, whoever provides the checkpoint now has the ability to trigger unsafe behavior, even though the graph is not under their control.

In other words, graphs can contain vulnerabilities of their own. To allow users to provide checkpoints to a model you run on their behalf (e.g., in order to compare model quality for a fixed model architecture), you must carefully audit your model, and we recommend you run the TensorFlow process in a sandbox.

It is possible to write models that are secure in the sense that they can safely process untrusted inputs assuming there are no bugs. There are two main reasons to not rely on this: First, it is easy to write models which must not be exposed to untrusted inputs, and second, there are bugs in any software system of sufficient complexity. Letting users control inputs could allow them to trigger bugs either in TensorFlow or in dependencies.

In general, it is good practice to isolate parts of any system which is exposed to untrusted (e.g., user-provided) inputs in a sandbox.

A useful analogy to how any TensorFlow graph is executed is any interpreted programming language, such as Python. While it is possible to write secure Python code which can be exposed to user supplied inputs (by, e.g., carefully quoting and sanitizing input strings, size-checking input blobs, etc.), it is very easy to write Python programs which are insecure. Even secure Python code could be rendered insecure by a bug in the Python interpreter, or in a bug in a Python library used (e.g., this one).

TensorFlow is a platform for distributed computing, and as such there is a TensorFlow server (tf.train.Server). The TensorFlow server is meant for internal communication only. It is not built for use in an untrusted network.

For performance reasons, the default TensorFlow server does not include any authorization protocol and sends messages unencrypted. It accepts connections from anywhere, and executes the graphs it is sent without performing any checks. Therefore, if you run a tf.train.Server in your network, anybody with access to the network can execute what you should consider arbitrary code with the privileges of the process running the tf.train.Server.

When running distributed TensorFlow, you must isolate the network in which the cluster lives. Cloud providers provide instructions for setting up isolated networks, which are sometimes branded as "virtual private cloud." Refer to the instructions for GCP and AWS) for details.

Note that tf.train.Server is different from the server created by tensorflow/serving (the default binary for which is called ModelServer). By default, ModelServer also has no built-in mechanism for authentication. Connecting it to an untrusted network allows anyone on this network to run the graphs known to the ModelServer. This means that an attacker may run graphs using untrusted inputs as described above, but they would not be able to execute arbitrary graphs. It is possible to safely expose a ModelServer directly to an untrusted network, but only if the graphs it is configured to use have been carefully audited to be safe.

Similar to best practices for other servers, we recommend running any ModelServer with appropriate privileges (i.e., using a separate user with reduced permissions). In the spirit of defense in depth, we recommend authenticating requests to any TensorFlow server connected to an untrusted network, as well as sandboxing the server to minimize the adverse effects of any breach.

TensorFlow is a large and complex system. It also depends on a large set of third party libraries (e.g., numpy, libjpeg-turbo, PNG parsers, protobuf). It is possible that TensorFlow or its dependencies may contain vulnerabilities that would allow triggering unexpected or dangerous behavior with specially crafted inputs.

Given TensorFlow's flexibility, it is possible to specify computation graphs which exhibit unexpected or unwanted behavior. The fact that TensorFlow models can perform arbitrary computations means that they may read and write files, communicate via the network, produce deadlocks and infinite loops, or run out of memory. It is only when these behaviors are outside the specifications of the operations involved that such behavior is a vulnerability.

A FileWriter writing a file is not unexpected behavior and therefore is not a vulnerability in TensorFlow. A MatMul allowing arbitrary binary code execution is a vulnerability.

This is more subtle from a system perspective. For example, it is easy to cause a TensorFlow process to try to allocate more memory than available by specifying a computation graph containing an ill-considered tf.tile operation. TensorFlow should exit cleanly in this case (it would raise an exception in Python, or return an error Status in C++). However, if the surrounding system is not expecting the possibility, such behavior could be used in a denial of service attack (or worse). Because TensorFlow behaves correctly, this is not a vulnerability in TensorFlow (although it would be a vulnerability of this hypothetical system).

As a general rule, it is incorrect behavior for TensorFlow to access memory it does not own, or to terminate in an unclean way. Bugs in TensorFlow that lead to such behaviors constitute a vulnerability.

One of the most critical parts of any system is input handling. If malicious input can trigger side effects or incorrect behavior, this is a bug, and likely a vulnerability.

Note: Assertion failures used to be considered a vulnerability in TensorFlow. If an assertion failure only leads to program termination and no other exploits, we will no longer consider assertion failures (e.g., CHECK-fails) as vulnerabilities. However, if the assertion failure occurs only in debug mode (e.g., DCHECK) and in production-optimized mode the issue turns into other code weakness(e.g., heap overflow, etc.), then we will consider this to be a vulnerability. We recommend reporters to try to maximize the impact of the vulnerability report (see also the Google VRP rules and the Google OSS VRP rules).

Please fill out this report form about any security related issues you find.

Please use a descriptive title for your report. After the initial reply to your report, the security team will endeavor to keep you informed of the progress being made towards a fix and announcement.

In addition, please include the following information along with your report:

• Your name and affiliation (if any).
• A description of the technical details of the vulnerabilities. It is very important to let us know how we can reproduce your findings.
• A minimal example of the vulnerabity.
• An explanation of who can exploit this vulnerability, and what they gain when doing so -- write an attack scenario. This will help us evaluate your report quickly, especially if the issue is complex.
• Whether this vulnerability is public or known to third parties. If it is, please provide details.

For each vulnerability, we try to ingress it as soon as possible, given the size of the team and the number of reports. If the vulnerability is not high impact, we will delay ingress during the period before a branch cut and the final release. For these cases, vulnerabilities will always be batched to be fixed at the same time as a quarterly release.

If a vulnerability is high impact, we will acknowledge reception and issue patches within an accelerated timeline and not wait for the patch release.

Once an issue is reported, TensorFlow uses the following disclosure process:

• When a report is received, we confirm the issue and determine its severity, according to the timeline listed above.
• If we know of specific third-party services or software based on TensorFlow that require mitigation before publication, those projects will be notified.
• An advisory is prepared (but not published) which details the problem and steps for mitigation.
• The vulnerability is fixed and potential workarounds are identified.
• Wherever possible, the fix is also prepared for the branches corresponding to all releases of TensorFlow at most one year old. We will attempt to commit these fixes as soon as possible, and as close together as possible.
• Patch releases are published for all fixed released versions, a notification is sent to [email protected], and the advisory is published.

We will make an attempt do patch releases for high impact security reasons up to 1 year after the release.

Past security advisories are listed below. We credit reporters for identifying security issues, although we keep your name confidential if you request it.

Since September 2022, you may also use the Google OSS VRP program to submit vulnerability reports.

For a list of known vulnerabilities and security advisories for TensorFlow, click here.