# What PyPy can do for your objects

Thanks to the Object Space architecture, any feature that is based on proxying, extending, changing or otherwise controlling the behavior of all objects in a running program is easy to implement on top of PyPy.

Here is what we implemented so far, in historical order:

• Thunk Object Space: lazily computed objects, computing only when an operation is performed on them; lazy functions, computing their result only if and when needed; and a way to globally replace an object with another.
• Taint Object Space: a soft security system; your application cannot accidentally compute results based on tainted objects unless it explicitly untaints them first.
• Dump Object Space: dumps all operations performed on all the objects into a large log file. For debugging your applications.
• Transparent Proxies Extension: adds new proxy objects to the Standard Object Space that enable applications to control operations on application and builtin objects, e.g lists, dictionaries, tracebacks.

Which object space to use can be chosen with the :config:objspace.name option.

## The Thunk Object Space

This small object space, meant as a nice example, wraps another object space (e.g. the standard one) and adds two capabilities: lazily computed objects, computed only when an operation is performed on them, and "become", a more obscure feature which allows to completely and globally replaces an object with another.

Example usage of lazily computed objects:

$py.py -o thunk >>>> from __pypy__ import thunk >>>> def f(): .... print 'computing...' .... return 6*7 .... >>>> x = thunk(f) >>>> x computing... 42 >>>> x 42 >>>> y = thunk(f) >>>> type(y) computing... <type 'int'> Example of how one object can be instantly and globally replaced with another:$ py.py -o thunk
>>>> from __pypy__ import become
>>>> x = object()
>>>> lst = [1, 2, x, 4]
>>>> become(x, 3)
>>>> lst
[1, 2, 3, 4]

There is also a decorator for functions whose result can be computed lazily (the function appears to return a result, but it is not really invoked before the result is used, if at all):

$py.py -o thunk >>>> from __pypy__ import lazy >>>> @lazy .... def f(x): .... print 'computing...' .... return x * 100 .... >>>> lst = [f(i) for i in range(10)] >>>> del lst[1:9] >>>> lst computing... computing... [0, 900] ### Implementation The implementation is short (see pypy/objspace/thunk.py_). For the purpose of become(), it adds an internal field w_thunkalias to each object, which is either None (in the common case) or a reference to the object that this object was replaced with. When any space operation is invoked, the chain of w_thunkalias references is followed and the underlying object space really operates on the new objects instead of the old ones. For the laziness part, the function thunk() returns an instance of a new internal class W_Thunk which stores the user-supplied callable and arguments. When a space operation follows the w_thunkalias chains of objects, it special-cases W_Thunk: it invokes the stored callable if necessary to compute the real value and then stores it in the w_thunkalias field of the W_Thunk. This has the effect of replacing the latter with the real value. ### Interface In a PyPy running with (or translated with) the Thunk Object Space, the __pypy__ module exposes the following interface: • thunk(f, *args, **kwargs): returns something that behaves like the result of the call f(*args, **kwargs) but the call is done lazily. • is_thunk(obj): return True if obj is a thunk that is not computed yet. • become(obj1, obj2): globally replace obj1 with obj2. • lazy(callable): should be used as a function decorator - the decorated function behaves lazily: all calls to it return a thunk object. ## The Taint Object Space ### Motivation The Taint Object Space provides a form of security: "tainted objects", inspired by various sources, see [D12.1] for a more detailed discussion. The basic idea of this kind of security is not to protect against malicious code but to help with handling and boxing sensitive data. It covers two kinds of sensitive data: secret data which should not leak, and untrusted data coming from an external source and that must be validated before it is used. The idea is that, considering a large application that handles these kinds of sensitive data, there are typically only a small number of places that need to explicitly manipulate that sensitive data; all the other places merely pass it around, or do entirely unrelated things. Nevertheless, if a large application needs to be reviewed for security, it must be entirely carefully checked, because it is possible that a bug at some apparently unrelated place could lead to a leak of sensitive information in a way that an external attacker could exploit. For example, if any part of the application provides web services, an attacker might be able to issue unexpected requests with a regular web browser and deduce secret information from the details of the answers he gets. Another example is the common CGI attack where an attacker sends malformed inputs and causes the CGI script to do unintended things. An approach like that of the Taint Object Space allows the small parts of the program that manipulate sensitive data to be explicitly marked. The effect of this is that although these small parts still need a careful security review, the rest of the application no longer does, because even a bug would be unable to leak the information. We have implemented a simple two-level model: objects are either regular (untainted), or sensitive (tainted). Objects are marked as sensitive if they are secret or untrusted, and only declassified at carefully-checked positions (e.g. where the secret data is needed, or after the untrusted data has been fully validated). It would be simple to extend the code for more fine-grained scales of secrecy. For example it is typical in the literature to consider user-specified lattices of secrecy levels, corresponding to multiple "owners" that cannot access data belonging to another "owner" unless explicitly authorized to do so. ### Tainting and untainting Start a py.py with the Taint Object Space and try the following example:$ py.py -o taint
>>>> from __pypy__ import taint
>>>> x = taint(6)

# x is hidden from now on.  We can pass it around and
# even operate on it, but not inspect it.  Taintness
# is propagated to operation results.

>>>> x
TaintError

>>>> if x > 5: y = 2   # see below
TaintError

>>>> y = x + 5         # ok
>>>> lst = [x, y]
>>>> z = lst.pop()
>>>> t = type(z)       # type() works too, tainted answer
>>>> t
TaintError
>>>> u = t is int      # even 'is' works
>>>> u
TaintError

Notice that using a tainted boolean like x > 5 in an if statement is forbidden. This is because knowing which path is followed would give away a hint about x; in the example above, if the statement if x > 5: y = 2 was allowed to run, we would know something about the value of x by looking at the (untainted) value in the variable y.

Of course, there is a way to inspect tainted objects. The basic way is to explicitly "declassify" it with the untaint() function. In an application, the places that use untaint() are the places that need careful security review. To avoid unexpected objects showing up, the untaint() function must be called with the exact type of the object to declassify. It will raise TaintError if the type doesn't match:

>>>> from __pypy__ import taint
>>>> untaint(int, x)
6
>>>> untaint(int, z)
11
>>>> untaint(bool, x > 5)
True
>>>> untaint(int, x > 5)
TaintError

### Taint Bombs

In this area, a common problem is what to do about failing operations. If an operation raises an exception when manipulating a tainted object, then the very presence of the exception can leak information about the tainted object itself. Consider:

>>>> 5 / (x-6)

By checking if this raises ZeroDivisionError or not, we would know if x was equal to 6 or not. The solution to this problem in the Taint Object Space is to introduce Taint Bombs. They are a kind of tainted object that doesn't contain a real object, but a pending exception. Taint Bombs are indistinguishable from normal tainted objects to unprivileged code. See:

>>>> x = taint(6)
>>>> i = 5 / (x-6)     # no exception here
>>>> j = i + 1         # nor here
>>>> k = j + 5         # nor here
>>>> untaint(int, k)
TaintError

In the above example, all of i, j and k contain a Taint Bomb. Trying to untaint it raises an exception - a generic TaintError. What we win is that the exception gives little away, and most importantly it occurs at the point where untaint() is called, not where the operation failed. This means that all calls to untaint() - but not the rest of the code - must be carefully reviewed for what occurs if they receive a Taint Bomb; they might catch the TaintError and give the user a generic message that something went wrong, if we are reasonably careful that the message or even its presence doesn't give information away. This might be a problem by itself, but there is no satisfying general solution here: it must be considered on a case-by-case basis. Again, what the Taint Object Space approach achieves is not solving these problems, but localizing them to well-defined small parts of the application - namely, around calls to untaint().

The TaintError exception deliberately does not include any useful error messages, because they might give information away. Of course, this makes debugging quite a bit harder; a difficult problem to solve properly. So far we have implemented a way to peek in a Taint Box or Bomb, __pypy__._taint_look(x), and a "debug mode" that prints the exception as soon as a Bomb is created - both write information to the low-level stderr of the application, where we hope that it is unlikely to be seen by anyone but the application developer.

### Taint Atomic functions

Occasionally, a more complicated computation must be performed on a tainted object. This requires first untainting the object, performing the computations, and then carefully tainting the result again (including hiding all exceptions into Bombs).

There is a built-in decorator that does this for you:

>>>> @__pypy__.taint_atomic
>>>> def myop(x, y):
....     while x > 0:
....         x -= y
....     return x
....
>>>> myop(42, 10)
-8
>>>> z = myop(taint(42), 10)
>>>> z
TaintError
>>>> untaint(int, z)
-8

The decorator makes a whole function behave like a built-in operation. If no tainted argument is passed in, the function behaves normally. But if any of the arguments is tainted, it is automatically untainted - so the function body always sees untainted arguments - and the eventual result is tainted again (possibly in a Taint Bomb).

It is important for the function marked as taint_atomic to have no visible side effects, as these could cause information leakage. This is currently not enforced, which means that all taint_atomic functions have to be carefully reviewed for security (but not the callers of taint_atomic functions).

A possible future extension would be to forbid side-effects on non-tainted objects from all taint_atomic functions.

An example of usage: given a tainted object passwords_db that references a database of passwords, we can write a function that checks if a password is valid as follows:

@taint_atomic

It returns a tainted boolean answer, or a Taint Bomb if something went wrong. A caller can do:

ok = untaint(bool, ok)

This can give three outcomes: True, False, or a TaintError exception (with no information on it) if anything went wrong. If even this is considered giving too much information away, the False case can be made indistinguishable from the TaintError case (simply by raising an exception in validate() if the password is wrong).

In the above example, the security results achieved are the following: as long as validate() does not leak information, no other part of the code can obtain more information about a passwords database than a Yes/No answer to a precise query.

A possible extension of the taint_atomic decorator would be to check the argument types, as untaint() does, for the same reason: to prevent bugs where a function like validate() above is accidentally called with the wrong kind of tainted object, which would make it misbehave. For now, all taint_atomic functions should be conservative and carefully check all assumptions on their input arguments.

### Interface

The basic rule of the Tainted Object Space is that it introduces two new kinds of objects, Tainted Boxes and Tainted Bombs (which are not types in the Python sense). Each box internally contains a regular object; each bomb internally contains an exception object. An operation involving Tainted Boxes is performed on the objects contained in the boxes, and gives a Tainted Box or a Tainted Bomb as a result (such an operation does not let an exception be raised). An operation called with a Tainted Bomb argument immediately returns the same Tainted Bomb.

In a PyPy running with (or translated with) the Taint Object Space, the __pypy__ module exposes the following interface:

• taint(obj)

Return a new Tainted Box wrapping obj. Return obj itself if it is already tainted (a Box or a Bomb).

• is_tainted(obj)

Check if obj is tainted (a Box or a Bomb).

• untaint(type, obj)

Untaints obj if it is tainted. Raise TaintError if the type of the untainted object is not exactly type, or if obj is a Bomb.

• taint_atomic(func)

Return a wrapper function around the callable func. The wrapper behaves like a built-in operation with respect to untainting the arguments, tainting the result, and returning a Bomb.

• TaintError

Exception. On purpose, it provides no attribute or error message.

• _taint_debug(level)

Set the debugging level to level (0=off). At level 1 or above, all Taint Bombs print a diagnostic message to stderr when they are created.

• _taint_look(obj)

For debugging purposes: prints (to stderr) the type and address of the object in a Tainted Box, or prints the exception if obj is a Taint Bomb.

## The Dump Object Space

When PyPy is run with (or translated with) the Dump Object Space, all operations between objects are dumped to a file called pypy-space-dump. This should give a powerful way to debug applications, but so far the dump can only be inspected in a text editor; better browsing tools are needed before it becomes really useful.

Try:

$py.py -o dump >>>> 2+3 5 >>>> (exit py.py here)$ more pypy-space-dump

On my machine the add between 2 and 3 starts at line 3152 (!) and returns at line 3164. All the rest is start-up, printing, and shutdown.

## Transparent Proxies

PyPy's Transparent Proxies allow routing of operations on objects to a callable. Application level code can customize objects without interfering with the type system - type(proxied_list) is list holds true when 'proxied_list' is a proxied built-in list - while giving you full control on all operations that are performed on the proxied_list.

See [D12.1] for more context, motivation and usage of transparent proxies.

### Example of the core mechanism

The following example proxies a list and will return 42 on any add operation to the list:

\$ py.py --objspace-std-withtproxy
>>>> from __pypy__ import tproxy
>>>> def f(operation, *args, **kwargs):
>>>>         return 42
>>>>    raise AttributeError
>>>>
>>>> i = tproxy(list, f)
>>>> type(i)
list
>>>> i + 3
42

### Example of recording all operations on builtins

Suppose we want to have a list which stores all operations performed on it for later analysis. We can use the small tputil module to help with transparently proxying builtin instances:

from tputil import make_proxy

history = []
def recorder(operation):
history.append(operation)
return operation.delegate()

>>>> l = make_proxy(recorder, obj=[])
>>>> type(l)
list
>>>> l.append(3)
>>>> len(l)
1
>>>> len(history)
2

make_proxy(recorder, obj=[]) creates a transparent list proxy where we can delegate operations to in the recorder function. Calling type(l) does not lead to any operation being executed at all.

Note that append shows up as __getattribute__ and that type(lst) does not show up at all - the type is the only aspect of the instance which the controller cannot change.

### Transparent Proxy PyPy builtins and support

If you are using the --objspace-std-withtproxy option the __pypy__ module provides the following builtins:

• tproxy(type, controller): returns a proxy object representing the given type and forwarding all operations on this type to the controller. On each such operation controller(opname, *args, **kwargs) is invoked.
• get_tproxy_controller(obj): returns the responsible controller for a given object. For non-proxied objects None is returned.

### tputil help module

The tputil.py module provides:

• make_proxy(controller, type, obj): function which creates a transparent proxy controlled by the given 'controller' callable. The proxy will appear as a completely regular instance of the given type but all operations on it are send to the specified controller - which receives a ProxyOperation instance on each such operation. A non-specified type will default to type(obj) if obj was specified.

ProxyOperation instances have the following attributes:

proxyobj: the transparent proxy object of this operation.

opname: the operation name of this operation

args: positional arguments for this operation

kwargs: keyword arguments for this operation

obj: (if provided to make_proxy): a concrete object

If you have specified a concrete object instance obj to your make_proxy invocation, you may call proxyoperation.delegate() to delegate the operation to this object instance.

### Further points of interest

A lot of tasks could be performed using transparent proxies, including, but not limited to:

• Remote versions of objects, on which we can directly perform operations (think about transparent distribution)
• Access to persistent storage such as a database (imagine an SQL object mapper which looks like a real object)
• Access to external data structures, such as other languages, as normal objects (of course some operations could raise exceptions, but since they are purely done on application level, that is not real problem)

### Implementation Notes

PyPy's standard object space allows to internally have multiple implementations of a type and change the implementation at run time while application level code consistently sees the exact same type and object. Multiple performance optimizations using this features are already implemented: see the document about alternative object implementations. Transparent Proxies use the architecture to provide control back to application level code.

Transparent proxies are implemented on top of the standard object space, in proxy_helpers.py, proxyobject.py and transparent.py. To use them you will need to pass a --objspace-std-withtproxy option to py.py or translate.py. This registers implementations named W_TransparentXxx - which usually correspond to an appropriate W_XxxObject - and includes some interpreter hacks for objects that are too close to the interpreter to be implemented in the std objspace. The types of objects that can be proxied this way are user created classes & functions, lists, dicts, exceptions, tracebacks and frames.

 [D12.1] (1, 2) High-Level Backends and Interpreter Feature Prototypes, PyPy EU-Report, 2007, http://codespeak.net/pypy/extradoc/eu-report/D12.1_H-L-Backends_and_Feature_Prototypes-2007-03-22.pdf