mirror of https://github.com/python/cpython.git
74 lines
3.6 KiB
TeX
74 lines
3.6 KiB
TeX
\chapter{Restricted Execution}
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In general, Python programs have complete access to the underlying
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operating system throug the various functions and classes, For
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example, a Python program can open any file for reading and writing by
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using the \code{open()} built-in function (provided the underlying OS
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gives you permission!). This is exactly what you want for most
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applications.
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There exists a class of applications for which this ``openness'' is
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inappropriate. Take Grail: a web browser that accepts ``applets'',
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snippets of Python code, from anywhere on the Internet for execution
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on the local system. This can be used to improve the user interface
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of forms, for instance. Since the originator of the code is unknown,
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it is obvious that it cannot be trusted with the full resources of the
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local machine.
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\emph{Restricted execution} is the basic framework in Python that allows
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for the segregation of trusted and untrusted code. It is based on the
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notion that trusted Python code (a \emph{supervisor}) can create a
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``padded cell' (or environment) with limited permissions, and run the
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untrusted code within this cell. The untrusted code cannot break out
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of its cell, and can only interact with sensitive system resources
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through interfaces defined and managed by the trusted code. The term
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``restricted execution'' is favored over ``safe-Python''
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since true safety is hard to define, and is determined by the way the
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restricted environment is created. Note that the restricted
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environments can be nested, with inner cells creating subcells of
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lesser, but never greater, privilege.
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An interesting aspect of Python's restricted execution model is that
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the interfaces presented to untrusted code usually have the same names
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as those presented to trusted code. Therefore no special interfaces
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need to be learned to write code designed to run in a restricted
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environment. And because the exact nature of the padded cell is
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determined by the supervisor, different restrictions can be imposed,
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depending on the application. For example, it might be deemed
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``safe'' for untrusted code to read any file within a specified
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directory, but never to write a file. In this case, the supervisor
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may redefine the built-in
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\code{open()} function so that it raises an exception whenever the
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\var{mode} parameter is \code{'w'}. It might also perform a
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\code{chroot()}-like operation on the \var{filename} parameter, such
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that root is always relative to some safe ``sandbox'' area of the
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filesystem. In this case, the untrusted code would still see an
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built-in \code{open()} function in its environment, with the same
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calling interface. The semantics would be identical too, with
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\code{IOError}s being raised when the supervisor determined that an
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unallowable parameter is being used.
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The Python run-time determines whether a particular code block is
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executing in restricted execution mode based on the identity of the
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\code{__builtins__} object in its global variables: if this is (the
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dictionary of) the standard \code{__builtin__} module, the code is
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deemed to be unrestricted, else it is deemed to be restricted.
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Python code executing in restricted mode faces a number of limitations
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that are designed to prevent it from escaping from the padded cell.
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For instance, the function object attribute \code{func_globals} and the
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class and instance object attribute \code{__dict__} are unavailable.
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Two modules provide the framework for setting up restricted execution
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environments:
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\begin{description}
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\item[rexec]
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--- Basic restricted execution framework.
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\item[Bastion]
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--- Providing restricted access to objects.
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\end{description}
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