# Using TensorFlow Securely

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 models are programs

TensorFlow's runtime system interprets and executes programs. What machine
learning practitioners term
[**models**](https://developers.google.com/machine-learning/glossary/#model) are
expressed as programs that TensorFlow executes.  TensorFlow programs are encoded
as computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
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.**

## Running untrusted models

As a general rule: **Always** execute untrusted models inside a sandbox (e.g.,
[nsjail](https://github.com/google/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.

## Accepting untrusted Inputs

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](https://www.cvedetails.com/cve/CVE-2017-12852/)).

## Running a TensorFlow server

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](https://cloud.google.com/compute/docs/networks-and-firewalls) and
[AWS](https://aws.amazon.com/vpc/)) 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.

## Vulnerabilities in TensorFlow

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.

### What is a vulnerability?

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.

### Reporting vulnerabilities

Please email reports about any security related issues you find to
`security@tensorflow.org`. This mail is delivered to a small security team. For
critical problems, you may encrypt your report (see below).

Please use a descriptive subject line for your report email. 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.
*   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.

If you believe that an existing (public) issue is security-related, please send
an email to `security@tensorflow.org`. The email should include the issue ID and
a short description of why it should be handled according to this security
policy.

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 discuss@tensorflow.org, and the advisory is published.

Note that we mostly do patch releases for security reasons and each version of
TensorFlow is supported for only 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.

#### Encryption key for `security@tensorflow.org`

If your disclosure is extremely sensitive, you may choose to encrypt your
report using the key below. Please only use this for critical security
reports.

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### Known Vulnerabilities

For a list of known vulnerabilities and security advisories for TensorFlow,
[click here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/README.md).