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Added new threat model.

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# Using TensorFlow Securely
This document discusses the TensorFlow security model. It describes how to
safely deal with untrusted programs (models or model parameters), and input
data. We also provide guidelines on what constitutes a vulnerability in
This document discusses the TensorFlow security model. It describes the security
risks to consider when using models, checkpoints or input data for training or
serving. We also provide guidelines on what constitutes a vulnerability in
TensorFlow and how to report them.
This document applies to other repositories in the TensorFlow organization,
@@ -16,272 +16,181 @@ use a term commonly used by machine learning practitioners) 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**.
Since models are practically programs that TensorFlow executes, using untrusted
models or graphs is equivalent to running untrusted code.
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.
If you need to run untrusted models, execute them inside a
[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions
in TensorFlow ops can be recognized as security issues only if they are
reachable and exploitable through production-grade, benign models.
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.
### Compilation
**TensorFlow models are programs, and need to be treated as such from a security
perspective.**
Compiling models via the recommended entry points described in
[XLA](https://www.tensorflow.org/xla) and
[JAX](https://jax.readthedocs.io/en/latest/jax-101/02-jitting.html)
documentation should be safe, while some of the testing and debugging tools that
come with the compiler are not designed to be used with untrusted data and
should be used with caution when working with untrusted models.
## Execution models of TensorFlow code
### Saved graphs and checkpoints
The TensorFlow library has a wide API which can be used in multiple scenarios.
The security requirements are also different depending on the usage.
When loading untrusted serialized computation graphs (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.
The API usage with the least security concerns is doing iterative exploration
via the Python interpreter or small Python scripts. Here, only some parts of the
API are exercised and eager execution is the default, meaning that each
operation executes immediately. This mode is useful for testing, including
fuzzing. For direct access to the C++ kernels, users of TensorFlow can directly
call `tf.raw_ops.xxx` APIs. This gives control over all the parameters that
would be sent to the kernel. Passing invalid combinations of parameters can
allow insecure behavior (see definition of a vulnerability in a section below).
However, these won’t always translate to actual vulnerabilities in TensorFlow.
This would be similar to directly dereferencing a null pointer in a C++ program:
not a vulnerability by itself but a coding error.
The risk of loading untrusted checkpoints depends on the code or graph that you
are working with. When loading untrusted checkpoints, the values of the traced
variables from your model are also going to be untrusted. That means that if
your code interacts with the filesystem, network, etc. and uses checkpointed
variables as part of those interactions (ex: using a string variable to build a
filesystem path), a maliciously created checkpoint might be able to change the
targets of those operations, which could result in arbitrary
read/write/executions.
The next 2 modes of using the TensorFlow API have the most security
implications. These relate to the actual building and use of machine learning
models. Both during training and inference, the TensorFlow runtime will build
and execute computation graphs from (usually Python) code written by a
practitioner (using compilation techniques to turn eager code into graph mode).
In both of these scenarios, a vulnerability can be exploited to cause
significant damage, hence the goal of the security team is to eliminate these
vulnerabilities or otherwise reduce their impact. This is essential, given that
both training and inference can run on accelerators (e.g. GPU, TPU) or in a
distributed manner.
Finally, the last mode of executing TensorFlow library code is as part of
additional tooling. For example, TensorFlow provides a `saved_model_cli` tool
which can be used to scan a `SavedModel` (the serialization format used by
TensorFlow for models) and describe it. These tools are usually run by a single
developer, on a single host, so the impact of a vulnerability in them is
somewhat reduced.
## 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` operation 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.
Similar considerations should apply if the model uses **custom ops** (C++ code
written outside of the TensorFlow tree and loaded as plugins).
## 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, however, 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
### 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.**
TensorFlow server (`tf.train.Server`). The TensorFlow server is intended for
internal communication only. It is not built for use in untrusted environments
or networks.
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`.
Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
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.
## Untrusted inputs during training and prediction
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**.
TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.
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.
These modifications and conversions are handled by a variety of libraries that
have different security properties and provide different levels of confidence
when dealing with untrusted data. Based on the security history of these
libraries we consider that it is safe to work with untrusted inputs for PNG,
BMP, GIF, WAV, RAW, RAW\_PADDED, CSV and PROTO formats. All other input formats,
including tensorflow-io should be sandboxed if used to process untrusted data.
## Multitenancy environments
For example, if an attacker were to upload a malicious video file, they could
potentially exploit a vulnerability in the TensorFlow code that handles videos,
which could allow them to execute arbitrary code on the system running
TensorFlow.
It is important to keep TensorFlow up to date with the latest security patches
and follow the sandboxing guideline above to protect against these types of
vulnerabilities.
## Security properties of execution modes
TensorFlow has several execution modes, with Eager-mode being the default in v2.
Eager mode lets users write imperative-style statements that can be easily
inspected and debugged and it is intended to be used during the development
phase.
As part of the differences that make Eager mode easier to debug, the [shape
inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.
The security impact of skipping those checks should be low, since the attack
scenario would require a malicious user to be able to control the model which as
stated above is already equivalent to code execution. In any case, the
recommendation is not to serve models using Eager mode since it also has
performance limitations.
## Multi-Tenant environments
It is possible to run multiple TensorFlow models in parallel. For example,
`ModelServer` collates all computation graphs exposed to it (from multiple
`SavedModel`) and executes them in parallel on available executors. A denial of
service caused by one model could bring down the entire server, but we don't
consider this as a high impact vulnerability, given that there exists solutions
to prevent this from happening (e.g., rate limits, ACLs, monitors to restart
broken servers).
`SavedModel`) and executes them in parallel on available executors. Running
TensorFlow in a multitenant design mixes the risks described above with the
inherent ones from multitenant configurations. The primary areas of concern are
tenant isolation, resource allocation, model sharing and hardware attacks.
However, it is a critical vulnerability if a model could be manipulated such
that it would output parameters of another model (or itself!) or data that
belongs to another model.
### Tenant isolation
Models that also run on accelerators could be abused to do hardware damage or to
leak data that exists on the accelerators from previous executions, if not
cleared.
Since any tenants or users providing models, graphs or checkpoints can execute
code in context of the TensorFlow service, it is important to design isolation
mechanisms that prevent unwanted access to the data from other tenants.
## Vulnerabilities in TensorFlow
Network isolation between different models is also important not only to prevent
unauthorized access to data or models, but also to prevent malicious users or
tenants sending graphs to execute under another tenant’s identity.
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.
The isolation mechanisms are the responsibility of the users to design and
implement, and therefore security issues deriving from their absence are not
considered a vulnerability in TensorFlow.
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.
### Resource allocation
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.
A denial of service caused by one model could bring down the entire server, but
we don't consider this as a vulnerability, given that models can exhaust
resources in many different ways and solutions exist to prevent this from
happening (e.g., rate limits, ACLs, monitors to restart broken servers).
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).
### Model sharing
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.
If the multitenant design allows sharing models, make sure that tenants and
users are aware of the security risks detailed here and that they are going to
be practically running code provided by other users. Currently there are no good
ways to detect malicious models/graphs/checkpoints, so the recommended way to
mitigate the risk in this scenario is to sandbox the model execution.
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.
### Hardware attacks
**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](https://bughunters.google.com/about/rules/6625378258649088/google-and-alphabet-vulnerability-reward-program-vrp-rules)
and [the Google OSS VRP
rules](https://bughunters.google.com/about/rules/6521337925468160/google-open-source-software-vulnerability-reward-program-rules)).
**Note**: Although the iterative exploration of TF API via fuzzing
`tf.raw_ops.xxx` symbols is the best way to uncover code weakness, please bear
in mind that this is not a typical use case that has security implications. It
is better to try to translate the vulnerability to something that can be
exploited during training or inference of a model (i.e., build a model that when
given a specific input would produce unwanted behavior). Alternatively, if the
TensorFlow API is only used in ancillary tooling, consider the environment where
the tool would run. For example, if `saved_model_cli` tool would crash on
parsing a `SavedModel` that is not considered a vulnerability but a bug (since
the user can use other ways to inspect the model if needed). However, it would
be a vulnerability if passing a `SavedModel` to `saved_model_cli` would result
in opening a new network connection, corrupting CPU state, or other forms of
unwanted behavior.
Physical GPUs or TPUs can also be the target of attacks. [Published
research](https://scholar.google.com/scholar?q=gpu+side+channel) shows that it
might be possible to use side channel attacks on the GPU to leak data from other
running models or processes in the same system. GPUs can also have
implementation bugs that might allow attackers to leave malicious code running
and leak or tamper with applications from other users. Please report
vulnerabilities to the vendor of the affected hardware accelerator.
## Reporting vulnerabilities
Please use [Google Bug Hunters reporting form](https://g.co/vulnz) to report security
related issues.
### Vulnerabilities in TensorFlow
Please use a descriptive title for your report.
This document covers different use cases for TensorFlow together with comments
whether these uses were recommended or considered safe, or where we recommend
some form of isolation when dealing with untrusted data. As a result, this
document also outlines what issues we consider as TensorFlow security
vulnerabilities.
In addition, please include the following information along with your report:
We recognize issues as vulnerabilities only when they occur in scenarios that we
outline as safe; issues that have a security impact only when TensorFlow is used
in a discouraged way (e.g. running untrusted models or checkpoints, data parsing
outside of the safe formats, etc.) are not treated as vulnerabilities..
* 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 vulnerability.
* 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,
### Reporting process
Please use [Google Bug Hunters reporting form](https://g.co/vulnz) to report
security vulnerabilities. Please include the following information along with
your report:
- A descriptive title
- Your name and affiliation (if any).
- A description of the technical details of the vulnerabilities.
- A minimal example of the vulnerability. It is very important to let us know
how we can reproduce your findings. For memory corruption triggerable in
TensorFlow models, please demonstrate an exploit against one of Alphabet's
models in <https://tfhub.dev/>
- An explanation of who can exploit this vulnerability, and what they gain
when doing so. Write an attack scenario that demonstrates how your issue
violates the use cases and security assumptions defined in the threat model.
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.
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.
TensorFlow uses the following disclosure process:
* When a report is received, we confirm the issue and determine its severity.
**Please try to maximize impact in the report**, going beyond just obtaining
unwanted behavior in a fuzzer.
* 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.
* We will publish a security advisory for all fixed vulnerabilities.
For each vulnerability, we try to ingress it as soon as possible, given the size
of the team and the number of reports. Vulnerabilities will, in general, be
batched to be fixed at the same time as a quarterly release.
Security advisories from 2018 to March 2023 are listed
[here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/README.md).
From TF 2.13 onwards, we have sunset this list and only use GitHub's Security
Advisory format, to simplify the post-vulnerability-fix process. In both
locations, we credit reporters for identifying security issues, although we keep
your name confidential if you request it.
We will try to fix the problems as soon as possible. Vulnerabilities will, in
general, be batched to be fixed at the same time as a quarterly release. We
credit reporters for identifying security issues, although we keep your name
confidential if you request it. Please see Google Bug Hunters program website
for more info.