[Python-Dev] doc for new restricted execution design for Python
brett at python.org
Thu Jun 22 02:33:38 CEST 2006
I have been working on a design doc for restricted execution of Python
as part of my dissertation for getting Python into Firefox to replace
messing that up can be costly, I am sending it to the list for any
I have already run the ideas past Neal, Guido, Jeremy, and Alex and
everyone seemed to think the design was sound (thanks to them and Will
for attending my meeting on it and giving me feedback that helped to
shape this doc), so hopefully there are no major issues with the
design itself. There are a couple of places (denoted with XXX) where
there is an open issue still. Feedback on those would be great.
Anyway, here it is. I am going to be offline most of tomorrow so I
probably won't get back to comments until Friday.
And just in case people are wondering, I plan on doing the
implementation in the open on a branch within Python's repository so
if this design works out it will end up in the core (as for when that
would land, I don't know, but hopefully for 2.6).
Restricted Execution for Python
About This Document
This document is meant to lay out the general design for re-introducing a
restriced execution model for Python. This document should provide one with
enough information to understand the goals for restricted execution, what
considerations were made for the design, and the actual design itself. Design
decisions should be clear and explain not only why they were chosen but
possible drawbacks from taking that approach.
A good restricted execution model provides enough protection to prevent
malicious harm to come to the system, and no more. Barriers should be
minimized so as to allow most code that does not do anything that would be
regarded as harmful to run unmodified.
An important point to take into consideration when reading this document is to
realize it is part of my (Brett Cannon's) Ph.D. dissertation. This means it is
heavily geared toward the restricted execution when the interpreter is working
with Python code embedded in a web page. While great strides have been taken
to keep the design general enough so as to allow all previous uses of the
'rexec' module [#rexec]_ to be able to use the new design, it is not the
focused goal. This means if a design decision must be made for the embedded
use case compared to sandboxing Python code in a Python application, the former
will win out.
Throughout this document, the term "resource" is to represent anything that
deserves possible protection. This includes things that have a physical
representation (e.g., memory) to things that are more abstract and specific to
the interpreter (e.g., sys.path).
When referring to the state of an interpreter, it is either "trusted" or
"untrusted". A trusted interpreter has no restrictions imposed upon any
resource. An untrusted interpreter has at least one, possibly more, resource
with a restriction placed upon it.
All use cases are based on how many untrusted or trusted interpreters are
running in a single process.
When the Interpreter Is Embedded
Single Untrusted Interpreter
This use case is when an application embeds the interpreter and never has more
than one interpreter running.
The main security issue to watch out for is not having default abilities be
provided to the interpreter by accident. There must also be protection from
leaking resources that the interpreter needs for general use underneath the
covers into the untrusted interpreter.
Multiple Untrusted Interpreters
When multiple interpreters, all untrusted at varying levels, need to be running
within a single application. This is the key use case that this proposed
design is targetted for.
On top of the security issues from a single untrusted interpreter, there is one
additional worry. Resources cannot end up being leaked into other interpreters
where they are given escalated rights.
When someone has written a Python program that wants to execute Python code in
an untrusted interpreter(s). This is the use case that 'rexec' attempted to
The added security issues for this use case (on top of the ones for the other
use cases) is preventing something from the trusted interpreter leaking into an
untrusted interpreter and having elevated permissions. With the multiple
untrusted interpreters one did not have to worry about preventing actions from
occurring that are disallowed for all untrusted interpreters. With this use
case you do have to worry about the binary distinction between trusted and
untrusted interpreters running in the same process.
Resources to Protect
The most obvious facet of a filesystem to protect is reading from it. One does
not want what is stored in ``/etc/passwd`` to get out. And one also does not
want writing to the disk unless explicitly allowed for basically the same
reason; if someone can write ``/etc/passwd`` then they can set the password for
the root account.
But one must also protect information about the filesystem. This includes both
the filesystem layout and permissions on files. This means pathnames need to
be properly hidden from an untrusted interpreter.
Memory should be protected. It is a limited resource on the system that can
have an impact on other running programs if it is exhausted. Being able to
restrict the use of memory would help alleviate issues from denial-of-service
Networking is somewhat like the filesystem in terms of wanting similar
protections. You do not want to let untrusted code make tons of socket
connections or accept them to do possibly nefarious things (e.g., acting as a
You also want to prevent finding out information about the network you are
connected to. This includes doing DNS resolution since that allows one to find
out what addresses your intranet has or what subnets you use.
One must make sure that the interpreter is not harmed in any way. There are
several ways to possibly do this. One is generating hostile bytecode. Another
is some buffer overflow. In general any ability to crash the interpreter is
There is also the issue of taking it over. If one is able to gain control of
the overall process through the interpreter than heightened abilities could be
Types of Security
As with most things, there are multiple approaches one can take to tackle a
problem. Security is no exception. In general there seem to be two approaches
to protecting resources.
By never giving code a chance to access a resource, you prevent it from be
(ab)used. This is the idea behind resource hiding. This can help minimize
security checks by only checking if someone should be given a resource. By
having possession of a resource be what determines if one should be allowed to
use it you minimize the checks to only when a resource is handed out.
This can be viewed as a passive system for security. Once a resource has been
given to code there are no more checks to make sure the security model is being
The most common implementation of resource hiding is capabilities. In this
type of system a resource's reference acts as a ticket that represents the right
to use the resource. Once code has a reference it is considered to have full
use of that resource it represents and no further security checks are
To allow customizable restrictions one can pass references to wrappers of
resources. This allows one to provide custom security to resources instead of
requiring an all-or-nothing approach.
The problem with capabilities is that it requires a way to control access to
references. In languages such as Java that use a capability-based security
system, namespaces provide the protection. By having private attributes and
compartmentalized namespaces, references cannot be reached without explicit
For instance, Java has a ClassLoader class that one can call to have return a
reference that is desired. The class does a security check to make sure the
code should be allowed to access the resource, and then returns a reference as
appropriate. And with private attributes in objects and packages not providing
global attributes you can effectively hide references to prevent security
To use an analogy, imagine you are providing security for your home. With
capabilities, security came from not having any way to know where your house is
without being told where it was; a reference to its location. You might be
able to ask a guard (e.g., Java's ClassLoader) for a map, but if they refuse
there is no way for you to guess its location without being told. But once you
knew where it was, you had complete use of the house.
And that complete access is an issue with a capability system. If someone
played a little loose with a reference for a resource then you run the risk of
it getting out. Once a reference leaves your hands it becomes difficult to
revoke the right to use that resource. A capability system can be designed to
do a check every time a reference is handed to a new object, but that can be
difficult to do properly when grafting a new way to handle resources on to an
existing system such as Python since the check is no longer at a point for
requesting a reference but also at plain assignment time.
Another approach to security is to provide constant, proactive security
checking of rights to use a resource. One can have a resource perform a
security check every time someone tries to use a method on that resource. This
pushes the security check to a lower level; from a reference level to the
By performing the security check every time a resource's method is called the
worry of a resource's reference leaking out to insecure code is alleviated
since the resource cannot be used without authorizing it regardless of whether
even having the reference was granted. This does add extra overhead, though,
by having to do so many security checks.
FreeBSD's jail system provides a system similar to this. Various system calls
allow for basic usage, but knowing of the system call is not enough to grant
usage. Every call of a system call requires checking that the proper rights
have been granted to the use in order to allow for the system call to perform
An even better example in FreeBSD's jail system is its protection of sockets.
One can only bind a single IP address to a jail. Any attempt to do more or
perform uses with the one IP address that is granted is prevented. The check
is performed at every call involving the one granted IP address.
Using our home analogy, everyone in the world can know where your home is. But
to access any door in your home, you have to pass a security check. The
overhead is higher and slows down your movement in your home, but not caring if
perfect strangers know where your home is prevents the worry of your address
leaking out to the world.
The 'rexec' Module
The 'rexec' module [#rexec]_ was based on the design used by Safe-Tcl
[#safe-tcl]_. The design was essentially a capability system. Safe-Tcl
allowed you to launch a separate interpreter where its global functions were
specified at creation time. This prevented one from having any abilities that
were not explicitly provided.
For 'rexec', the Safe-Tcl model was tweaked to better match Python's situation.
An RExec object represented a restricted environment. Imports were checked
against a whitelist of modules. You could also restrict the type of modules to
import based on whether they were Python source, bytecode, or C extensions.
Built-ins were allowed except for a blacklist of built-ins to not provide.
Several other protections were provided; see documentation for the complete
With an RExec object created, one could pass in strings of code to be executed
and have the result returned. One could execute code based on whether stdin,
stdout, and stderr were provided or not.
The ultimate undoing of the 'rexec' module was how access to objects that in
normal Python require no direct action to reach was handled. Importing modules
requires a direct action, and thus can be protected against directly in the
import machinery. But for built-ins, they are accessible by default and
require no direct action to access in normal Python; you just use their name
since they are provided in all namespaces.
For instance, in a restricted interpreter, one only had to do
``del __builtins__`` to gain access to the full set of built-ins. Another way
is through using the gc module:
``gc.get_referrers(''.__class__.__bases__)['file']``. While both of
these could be fixed (the former a bug in 'rexec' and the latter not allowing
gc to be imported), they are examples of things that do not require proactive
actions on the part of the programmer in normal Python to gain access to
tends to leak out. An unfortunate side-effect of having all of that wonderful
reflection in Python.
There is also the issue that 'rexec' was written in Python which provides its
Much has been learned since 'rexec' was written about how Python tends to be
used and where security issues tend to appear. Essentially Python's dynamic
nature does not lend itself very well to passive security measures since the
reflection abilities in the language lend themselves to getting around
non-proactive security checks.
The Proposed Approach
In light of where 'rexec' succeeded and failed along with what is known about
the two main types of security and how Python tends to operate, the following
is a proposal on how to secure Python for restricted execution.
First, security will be provided at the C level. By taking advantage of the
language barrier of accessing C code from Python without explicit allowance
(i.e., ignoring ctypes [#ctypes]_), direct manipulation of the various security
checks can be substantially reduced and controlled.
Second, all proactive actions that code can do to gain access to resources will
be protected through resource hiding. By having to go through Python to get to
something (e.g., modules), a security check can be put in place to deny access
as appropriate (this also ties into the separation between interpreters,
Third, any resource that is usually accessible by default will use resource
crippling. Instead of worrying about hiding a resource that is available by
default (e.g., 'file' type), security checks within the resource will prevent
misuse. Crippling can also be used for resources where an object could be
desired, but not at its full capacity (e.g., sockets).
Performance should not be too much of an issue for resource crippling. It's
main use if for I/O types; files and sockets. Since operations on these types
are I/O bound and not CPU bound, the overhead for doing the security check
should be a wash overall.
Fourth, the restrictions separating multiple interpreters within a single
process will be utilized. This helps prevent the leaking of objects into
different interpreters with escalated privileges. Python source code
modules are reloaded for each interpreter, preventing an object that does not
have resource crippling from being leaked into another interpreter unless
explicitly allowed. C extension modules are shared by not reloading them
between interpreters, but this is considered in the security design.
Fifth, Python source code is always trusted. Damage to a system is considered
to be done from either hostile bytecode or at the C level. Thus protecting the
interpreter and extension modules is the great worry, not Python source code.
Python bytecode files, on the other hand, are considered inherently unsafe and
will never be imported directly.
Attempts to perform an action that is not allowed by the security policy will
raise an XXX exception (or subclass thereof) as appropriate.
XXX prefix/module name; Restrict, Secure, Sandbox? Different tense?
XXX C APIs use abstract names (e.g., string, integer) since have not decided if
Python objects or C types (e.g., PyStringObject vs. char *) will be used
Support for untrusted interpreters will be a compilation flag. This allows the
more common case of people not caring about protections to not have a
performance hindrance when not desired. And even when Python is compiled for
untrusted interpreter restrictions, when the running interpreter *is* trusted,
there will be no accidental triggers of protections. This means that
developers should be liberal with the security protections without worrying
about there being issues for interpreters that do not need/want the protection.
At the Python level, the __restricted__ built-in will be set based on whether
the interpreter is untrusted or not. This will be set for *all* interpreters,
regardless of whether untrusted interpreter support was compiled in or not.
For setting what is to be protected, the XXX<pointer to interpreter> for the
untrusted interpreter must be passed in. This makes the protection very
explicit and helps make sure you set protections for the exact interpreter you
The functions for checking for permissions are actually macros that take
in at least an error return value for the function calling the macro. This
allows the macro to return for the caller if the check failed and cause the XXX
exception to be propagated. This helps eliminate any coding errors from
incorrectly checking a return value on a rights-checking function call. For
the rare case where this functionality is disliked, just make the check in a
utility function and check that function's return value (but this is strongly
* interpreter PyXXX_NewInterpreter()
Return a new interpreter that is considered untrusted. There is no
corresponding PyXXX_EndInterpreter() as Py_EndInterpreter() will be taught
how to handle untrusted interpreters.
Macro that has the caller return with 'error_return' if the interpreter is
not a trusted one.
An memory cap will be allowed.
Modification to pymalloc will be needed to properly keep track of the
allocation and freeing of memory. Same goes for the macros around the system
malloc/free system calls. This provides a platform-independent system for
protection instead of relying on the operating system providing a service for
capping memory usage of a process. Also allows the protection to be at the
interpreter level instead of at the process level.
Protecting excessive memory usage allows one to make sure that a DoS attack
against the system's memory is prevented.
Possible Security Flaws
If code makes direct calls to malloc/free instead of using the proper PyMem_*()
macros then the security check will be circumvented. But C code is *supposed*
to use the proper macros or pymalloc and thus this issue is not with the
security model but with code not following Python coding standards.
* int PyXXX_SetMemoryCap(interpreter, integer)
Set the memory cap for an untrusted interpreter. If the interpreter is not
running an untrusted interpreter, return NULL.
* PyXXX_MemoryAlloc(integer, error_return)
Macro to increase the amount of memory that is reported that the running
untrusted interpreter is running. If the increase puts the total count
passed the set limit, raise an XXX exception and cause the calling function
to return with the value of error_return. For trusted interpreters or
untrusted interpreters where a cap has not been set, the macro does
* int PyXXX_MemoryFree(integer)
Decrease the current running interpreter's allocated memory. If this puts
the memory returned to below 0, raise an XXX exception and return NULL.
For trusted interpreters or untrusted interpreters where there is no memory
cap, the macro does nothing.
XXX Needed? Difficult to get right for all platforms. Would have to be very
The 'file' type will be resource crippled. The user may specify files or
directories that are acceptable to be opened for reading/writing, or both.
All operations that either read, write, or provide info on a file will require
a security check to make sure that it is allowed for the file that the 'file'
object represents. This includes the 'file' type's constructor not raising an
IOError stating a file does not exist but XXX instead so that information about
the filesystem is not improperly provided.
The security check will be done for all 'file' objects regardless of where the
'file' object originated. This prevents issues if the 'file' type or an
instance of it was accidentally made available to an untrusted interpreter.
Allowing anyone to be able to arbitrarily read, write, or learn about the
layout of your filesystem is extremely dangerous. It can lead to loss of data
or data being exposed to people whom should not have access.
Possible Security Flaws
Assuming that the method-level checks are correct and control of what
files/directories is not exposed, 'file' object protection is secure, even when
a 'file' object is leaked from a trusted interpreter to an untrusted one.
* int PyXXX_AllowFile(interpreter, path, mode)
Add a file that is allowed to be opened in 'mode' by the 'file' object. If
the interpreter is not untrusted then return NULL.
* int PyXXX_AllowDirectory(interpreter, path, mode)
Add a directory that is allowed to have files opened in 'mode' by the
'file' object. This includes both pre-existing files and any new files
created by the 'file' object.
XXX allow for creating/reading subdirectories?
* PyXXX_CheckPath(path, mode, error_return)
Macro that causes the caller to return with 'error_return' and XXX as the
exception if the specified path with 'mode' is not allowed. For trusted
interpreters, the macro does nothing.
Extension Module Importation
A whitelist of extension modules that may be imported must be provided. A
default set is given for stdlib modules known to be safe.
A check in the import machinery will check that a specified module name is
allowed based on the type of module (Python source, Python bytecode, or
extension module). Python bytecode files are never directly imported because
of the possibility of hostile bytecode being present. Python source is always
trusted based on the assumption that all resource harm is eventually done at
the C level, thus Python code directly cannot cause harm. Thus only C
extension modules need to be checked against the whitelist.
The requested extension module name is checked in order to make sure that it
is on the whitelist if it is a C extension module. If the name is not correct
an XXX exception is raised. Otherwise the import is allowed.
Even if a Python source code module imports a C extension module in a trusted
interpreter it is not a problem since the Python source code module is reloaded
in the untrusted interpreter. When that Python source module is freshly
imported the normal import check will be triggered to prevent the C extension
module from becoming available to the untrusted interpreter.
For the 'os' module, a special restricted version will be used if the proper
C extension module providing the correct abilities is not allowed. This will
default to '/' as the path separator and provide as much reasonable abilities
as possible from a pure Python module.
The 'sys' module is specially addressed in
`Changing the Behaviour of the Interpreter`_.
By default, the whitelisted modules are:
* XXX work off of rexec whitelist?
Because C code is considered unsafe, its use should be regulated. By using a
whitelist it allows one to explicitly decide that a C extension module should
be considered safe.
Possible Security Flaws
If a trusted C extension module imports an untrusted C extension module and
make it an attribute of the trust module there will be a breach in security.
Luckily this a rarity in extension modules.
There is also the issue of a C extension module calling the C API of an
untrusted C extension module.
Lastly, if a trusted C extension module is loaded in a trusted interpreter and
then loaded into an untrusted interpreter then there is no possible checks
during module initialization for possible security issues for resources opened
during initialization of the module if such checks exist in the init*()
All of these issues can be handled by never blindly whitelisting a C extension
module. Added support for dealing with C extension modules comes in the form
of `Extension Module Crippling`_.
* int PyXXX_AllowModule(interpreter, module_name)
Allow the untrusted interpreter to import 'module_name'. If the
interpreter is not untrusted, return NULL.
XXX sub-modules in packages allowed implicitly? Or have to list all
* int PyXXX_BlockModule(interpreter, module_name)
Remove the specified module from the whitelist. Used to remove modules
that are allowed by default. If called on a trusted interpreter, returns
* PyXXX_CheckModule(module_Name, error_return)
Macro that causes the caller to return with 'error_return' and sets the
exception XXX if the specified module cannot be imported. For trusted
interpreters the macro does nothing.
Extension Module Crippling
By providing a C API for checking for allowed abilities, modules that have some
useful functionality can do proper security checks for those functions that
could provide insecure abilities while allowing safe code to be used (and thus
not fully deny importation).
Consider a module that provides a string processing ability. If that module
provides a single convenience function that reads its input string from a file
(with a specified path), the whole module should not be blocked from being
used, just that convenience function. By whitelisting the module but having a
security check on the one problem function, the user can still gain access to
the safe functions. Even better, the unsafe function can be allowed if the
security checks pass.
Possible Security Flaws
If a C extension module developer incorrectly implements the security checks
for the unsafe functions it could lead to undesired abilities.
Use PyXXX_Trusted() to protect unsafe code from being executed.
The code object's constructor is not callable from Python. Importation of .pyc
and .pyo files is also prohibited.
Without implementing a bytecode verification tool, there is no way of making
sure that bytecode does not jump outside its bounds, thus possibly executing
malicious code. It also presents the possibility of crashing the interpreter.
Possible Security Flaws
Changing the Behaviour of the Interpreter
Only a subset of the 'sys' module will be made available to untrusted
interpreters. Things to allow from the sys module:
* stdin # See `Stdin, Stdout, and Stderr`_.
* __stdin__ # See `Stdin, Stdout, and Stderr`_ XXX Perhaps not needed?
Filesystem information must be removed. Any settings that could
possibly lead to a DoS attack (e.g., sys.setrecursionlimit()) or risk crashing
the interpreter must also be removed.
Possible Security Flaws
Exposing something that could lead to future security problems (e.g., a way to
crash the interpreter).
Allow sending and receiving data to/from specific IP addresses on specific
Allowing arbitrary sending of data over sockets can lead to DoS attacks on the
network and other machines. Limiting accepting data prevents your machine from
being attacked by accepting malicious network connections. It also allows you
to know exactly where communication is going to and coming from.
Possible Security Flaws
If someone managed to influence the used DNS server to influence what IP
addresses were used after a DNS lookup.
* int PyXXX_AllowIPAddress(interpreter, IP, port)
Allow the untrusted interpreter to send/receive to the specified IP
address on the specified port. If the interpreter is not untrusted,
* PyXXX_CheckIPAddress(IP, port, error_return)
Macro to verify that the specified IP address on the specified port is
allowed to be communicated with. If not, cause the caller to return with
'error_return' and XXX exception set. If the interpreter is trusted then
* PyXXX_AllowHost(interpreter, host, port)
Allow the untrusted interpreter to send/receive to the specified host on
the specified port. If the interpreter is not untrusted, return NULL.
XXX resolve to IP at call time to prevent DNS man-in-the-middle attacks?
* PyXXX_CheckHost(host, port, error_return)
Check that the specified host on the specified port is allowed to be
communicated with. If not, set an XXX exception and cause the caller to
return 'error_return'. If the interpreter is trusted then do nothing.
Limit what information can be gleaned about the network the system is running
on. This does not include restricting information on IP addresses and hosts
that are have been explicitly allowed for the untrusted interpreter to
With enough information from the network several things could occur. One is
that someone could possibly figure out where your machine is on the Internet.
Another is that enough information about the network you are connected to could
be used against it in an attack.
Possible Security Flaws
As long as usage is restricted to only what is needed to work with allowed
addresses, there are no security issues to speak of.
* int PyXXX_AllowNetworkInfo(interpreter)
Allow the untrusted interpreter to get network information regardless of
whether the IP or host address is explicitly allowed. If the interpreter
is not untrusted, return NULL.
Macro that will return 'error_return' for the caller and set XXX exception
if the untrusted interpreter does not allow checking for arbitrary network
information. For a trusted interpreter this does nothing.
Do not allow information about the filesystem layout from various parts of
Python to be exposed. This means blocking exposure at the Python level to:
* __file__ attribute on modules
* __path__ attribute on packages
* co_filename attribute on code objects
Exposing information about the filesystem is not allowed. You can figure out
what operating system one is on which can lead to vulnerabilities specific to
that operating system being exploited.
Possible Security Flaws
Not finding every single place where a file path is exposed.
* int PyXXX_AllowFilesystemInfo(interpreter)
Allow the untrusted interpreter to expose filesystem information. If the
passed-in interpreter is not untrusted, return NULL.
Macro that checks if exposing filesystem information is allowed. If it is
not, cause the caller to return with the value of 'error_return' and raise
Stdin, Stdout, and Stderr
By default, sys.__stdin__, sys.__stdout__, and sys.__stderr__ will be set to
instances of cStringIO. Allowing use of the normal stdin, stdout, and stderr
will be allowed.
XXX Or perhaps __stdin__ and friends should just be blocked and all you get is
sys.stdin and friends set to cStringIO.
Interference with stdin, stdout, or stderr should not be allowed unless
Possible Security Flaws
Unless cStringIO instances can be used maliciously, none to speak of.
XXX Use StringIO instances instead for even better security?
* int PyXXX_UseTrueStdin(interpreter)
Set the specific stream for the interpreter to the true version of the
stream and not to the default instance of cStringIO. If the interpreter is
not untrusted, return NULL.
Adding New Protections
Allow for extensibility in the security model by being able to add new types of
checks. This allows not only for Python to add new security protections in a
backwards-compatible fashion, but to also have extension modules add their own
An extension module can introduce a group for its various values to check, with
a type being a specific value within a group. The "Python" group is
specifically reserved for use by the Python core itself.
We are all human. There is the possibility that a need for a new type of
protection for the interpreter will present itself and thus need support. By
providing an extensible way to add new protections it helps to future-proof the
It also allows extension modules to present their own set of security
protections. That way one extension module can use the protection scheme
presented by another that it is dependent upon.
Possible Security Flaws
Poor definitions by extension module users of how their protections should be
used would allow for possible exploitation.
XXX Could also have PyXXXExtended prefix instead for the following functions
* int PyXXX_ExtendedSetTrue(interpreter, group, type)
Set a group-type to be true. Expected use is for when a binary
possibility of something is needed and that the default is to not allow
use of the resource (e.g., network information). Returns NULL if the
interpreter is not untrusted.
* PyXXX_ExtendedCheckTrue(group, type, error_return)
Macro that if the group-type is not set to true, cause the caller to
return with 'error_return' with XXX exception raised. For trusted
interpreters the check does nothing.
+ Numeric Range
* int PyXXX_ExtendedValueCap(interpreter, group, type, cap)
Set a group-type to a capped value, with the initial value set to 0.
Expected use is when a resource has a capped amount of use (e.g.,
memory). Returns NULL if the interpreter is not untrusted.
* PyXXX_ExtendedValueAlloc(increase, error_return)
Macro to raise the amount of a resource is used by 'increase'. If the
increase pushes the resource allocation past the set cap, then return
'error_return' and set XXX as the exception.
* PyXXX_ExtendedValueFree(decrease, error_return)
Macro to lower the amount a resource is used by 'decrease'. If the
decrease pushes the allotment to below 0 then have the caller return
'error_return' and set XXX as the exception.
* int PyXXX_ExtendedAddMembership(interpreter, group, type, string)
Add a string to be considered a member of a group-type (e.g., allowed
file paths). If the interpreter is not an untrusted interpreter,
* PyXXX_ExtendedCheckMembership(group, type, string, error_return)
Macro that checks 'string' is a member of the values set for the
group-type. If it is not, then have the caller return 'error_return'
and set an exception for XXX. For trusted interpreters the call does
+ Specific Value
* int PyXXX_ExtendedSetValue(interpreter, group, type, string)
Set a group-type to a specific string. If the interpreter is not
untrusted, return NULL.
* PyXXX_ExtendedCheckValue(group, type, string, error_return)
Macro to check that the group-type is set to 'string'. If it is not,
then have the caller return 'error_return' and set an exception for
XXX. If the interpreter is trusted then nothing is done.
.. [#rexec] The 'rexec' module
.. [#safe-tcl] The Safe-Tcl Security Model
.. [#ctypes] 'ctypes' module
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