Effectively safe_mode limits you to accessing files that you uploaded via ftp or ssh and had previously prepared, so that you can only append (or truncate and append) to them. This quite frankly makes performing virtually any file operation a big pain in the ass. Fortunately, there is a bypass, which puts the whole concept of "safe" mode into question. You can upload the php scripts you intend to have to perform file operations via another script, thereby giving you a script who's owner/group is the webserver. This script will then be able to edit/add/remove files and directories since all items involved are owned by the webserver. However, you first need to prepare the directories the uploaded script will be writing to, since existing directories do not have a matching uid/gid (they are owned by you).
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While this is rarely affecting commonly used functions and review extensions, it does not change the fact that many functions could bypass these limitations. In fact an overview of extensions inside the. Pecl repository shows that many of them do not in fact perform these checks. Some of these extensions are fairly popular and i've seen them in use by fairly large number of webhosts. This means that isps that assume (yes, we all know what they say about that) that safe_mode works are really misleading themselves unless they actually perform an audit on the php extensions available on their servers. The second and perhaps the most critical problem is the implementation of the functionality and how it effects the users. Consider the following situation, you have a script script that generates a frequently accessed page, parts of which you can safely cache. The simple thing many developers would essay do, is pregenerate that data and store it inside a file that can be simply included saving a significant chunk of processing time needed to generate that output. Well, under safe_mode, creation of this cache file, would work, but accessing said file would fail. The reason for the failure is that the original script owned by you, does not match the uid/gid of the file it created because the file was created with permissions (uid/gid) of the webserver who php runs under.
To those unfamiliar with this wondrous invention, this setting is primarily intended to paperwork provide file access limits to prevent users from accessing files that do no belong to them. This supposedly should make it impossible to access files of other people in a shared server environment, a common operating environment for php where php runs as an Apache module and as such has read access to all files accessible by the webserver regardless. When enabled, safe_mode will perform a uid/gid (user id and group id) check on the file/directory to be accessed and compare it to the uid/gid of the script that is trying to access the file. If the two match then the file operation will proceed as normal and in all other cases it will fail. In theory this is a fairly simple hack to a problem that is not otherwise easily addressed without significant performance penalties such as running php in cgi mode, whereby the scripts are executed under the user's own user/group. However, as with virtually all hacks there tend to be unforeseen problems that further prove that temporary solutions only escalate problems. So let's examine the safe_mode problems and hopefuly demonstrate why it should be avoided. The first problem that is really more of a bug then anything else is that safe_mode requires that anytime php or the underlying libraries used by php extensions perform a file access a uid/gid check be performed. While it is not difficult to do and wrappers exist for it, every single release since this functionality was introduced had fixes for missing safe_mode checks.
Easy integration with NumPy through the ctypes attribute full argument checking with the ndpointer class factory Its disadvantages include It is difficult to distribute an extension module made using ctypes because of a lack of support for building shared libraries in distutils (but I suspect. You british must have shared-libraries of your code (no static libraries). Very little support for C code and its different library-calling conventions. You will probably need a c wrapper around C code to use with ctypes (or just use thon instead). Because of the difficulty in distributing an extension module made using ctypes, f2py and Cython are still the easiest ways to extend Python for package creation. However, ctypes is in some cases a useful alternative. This should bring more features to ctypes that should eliminate the difficulty in extending Python and distributing the extension using ctypes. A few years ago php developers decided to address a problem not their's to solve by implementing a configuration directive called safe_mode.
Setting up the filtering function is similar and allows the filtering function to be called with ndarray arguments as the first two arguments and with pointers to integers (large enough to handle the strides and shape of an ndarray) as the last two arguments.: stypeNone. Ctypeslib.ndpointer(float, ndim2, flags'aligned. Ctypeslib.ndpointer(float, ndim2, flags'aligned, contiguous 'writeable next, define a simple selection function that chooses which addition function to call in the shared library based on the data-type: def select(dtype if ar in '? BBhHf return dd, single elif ar in 'f return dd, csingle elif ar in 'dg return dd, complex else: return lib. Dadd, float return func, ntype finally, the two functions to be exported by the interface can be written simply as: def add(a, b requires 'contiguous 'aligned' a anyarray(a) func, dtype select(a.dtype) a quire(a, dtype, requires) b quire(b, dtype, requires). Empty_like(a) func(a,b,c, ze) return c and: def filter2d(a a quire(a, float, 'aligned. Dfilter2d(a, b, rides, ape) return b Conclusion Using ctypes is a powerful way to connect Python with arbitrary c-code. Its advantages for extending Python include clean separation of C code from Python code no need to learn a new syntax except Python and c allows re-use of C code functionality in shared libraries written for other purposes can be obtained with a simple python.
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C Which creates a shared_library named rowling in the current directory. On Windows dont forget to either add _declspec(dllexport) in front of void on the line preceding each function definition, or write a f file that lists the names of the functions to be exported. A suitable python interface to this shared library should be constructed. To do this create a file named with the following lines at the top: _all_ 'add 'filter2d' import numpy as n import os _path. Dirname file lib _path) _typedict 'zadd' : complex, 'sadd' : ngle, 'cadd' : ingle, 'dadd' : float for name in _ys val getattr(lib, name) stype none _type _typedictname gtypes. Ctypeslib.ndpointer type, flags'aligned, contiguous.
Ctypeslib.ndpointer type, flags'aligned, contiguous 'writeable. Ctypeslib.c_intp This code loads the shared library named code. Ext located in the same path as this file. It then adds a return type of void to the functions contained in the library. It also adds argument checking to the functions in the library so that ndarrays can be passed as the first three arguments along with an integer (large enough to hold a pointer on the platform) as the fourth argument.
The data attribute returns a c_void_p representing a pointer to the data area. The shape and strides attributes each return an array of ctypes integers (or None representing a null pointer, if a 0-d array). The base ctype of the array is a ctype integer of the same size as a pointer on the platform. There are also methods data_as(ctype shape_as( base ctype and strides_as( base ctype ). These return the data as a ctype object of your choice and the shape/strides arrays using an underlying base type of your choice.
For convenience, the ctypeslib module also contains c_intp as a ctypes integer data-type whose size is the same as the size of c_void_p on the platform (its value is None if ctypes is not installed). Complete example In this example, i will show how the addition function and the filter function implemented previously using the other approaches can be implemented using ctypes. First, the c code which implements the algorithms contains the functions zadd, dadd, sadd, cadd, and dfilter2d. The zadd function is: add arrays of contiguous data typedef struct double real; double imag; cdouble; typedef struct float real; float imag; cfloat; void zadd(cdouble *a, cdouble *b, cdouble *c, long n) while (n-) c- real a- real b- real; c- imag a- imag. C file also contains the function dfilter2d: * Assumes b is contiguous and has strides that are multiples of * sizeof(double) void dfilter2d(double *a, double *b, ssize_t *astrides, ssize_t *dims) ssize_t i, j, m, n, s0, S1; ssize_t r, c, rm1, rp1, cp1, cm1;. Non-contiguous arrays) and may also run faster depending on the optimization capability of your compiler. But, it is an obviously more complicated than the simple code in filter. This code must be compiled into a shared library. On my linux system this is accomplished using: gcc -o -shared code.
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To implement the second method, numPy provides the class-factory function ndpointer in the ctypeslib module. This class-factory function produces an appropriate class that can be placed in an argtypes attribute entry of a ctypes function. The class will contain a from_param method which ctypes will use to convert any ndarray passed in to the function to a ctypes-recognized object. In the process, the conversion will perform checking on any properties of the ndarray that were specified by the user in the call to ndpointer. Aspects of the ndarray that can be checked include the data-type, the number-of-dimensions, the shape, and/or the state of the flags on any array passed. The return value of the from_param method is the ctypes attribute of the array which (because it contains the _as_parameter_ attribute pointing to the array data area) can be used by ctypes directly. The ctypes attribute of an ndarray is also endowed with additional attributes that may be convenient when passing additional information about the array into a ctypes function. The attributes data, shape, and strides can provide ctypes compatible types corresponding to the data-area, the shape, and the strides of the array.
There are two ways around this restriction that allow ctypes to integrate with other objects. Dont set the essay argtypes attribute of the function object and define an _as_parameter_ method for the object you want to pass. The _as_parameter_ method must return a python int which will be passed directly to the function. Set the argtypes attribute to a list whose entries contain objects with a classmethod named from_param that knows how to convert your object to an object that ctypes can understand (an int/long, string, unicode, or object with the _as_parameter_ attribute). Numpy uses both methods with a preference for the second method because it can be safer. The ctypes attribute of the ndarray returns an object that has an _as_parameter_ attribute which returns an integer representing the address of the ndarray to which it is associated. As a result, one can pass this ctypes attribute object directly to a function expecting a pointer to the data in your ndarray. The caller must be sure that the ndarray object is of the correct type, shape, and has the correct flags set or risk nasty crashes if the data-pointer to inappropriate arrays are passed.
mind living a little dangerously ctypes can be an effective tool for quickly taking advantage of a large shared library (or writing extended functionality in your own shared library). Because the ctypes approach exposes a raw interface to the compiled code it is not always tolerant of user mistakes. Robust use of the ctypes module typically involves an additional layer of Python code in order to check the data types and array bounds of objects passed to the underlying subroutine. This additional layer of checking (not to mention the conversion from ctypes objects to c-data-types that ctypes itself performs will make the interface slower than a hand-written extension-module interface. However, this overhead should be negligible if the c-routine being called is doing any significant amount of work. If you are a great Python programmer with weak c skills, ctypes is an easy way to write a useful interface to a (shared) library of compiled code. Converting arguments, python ints/longs, strings, and unicode objects are automatically converted as needed to equivalent ctypes arguments The none object is also converted automatically to a null pointer. All other Python objects must be converted to ctypes-specific types.
ls -l./shutil_test -r root root :42 shutil_test strace - python -c "import shutil;./shutil_test tmp many irrelevant lines open./shutil_test O_rdonly) 3 fstat(3, st_modeS_ifreg0400, st_size10,.) 0 open tmp/shutil_test O_wronlyo_creato_trunc, 0666) 4 fstat(4, st_modeS_ifreg0644, st_size0,.) 0 fstat(3, st_modeS_ifreg0400, st_size10,.) 0 mmap(null, 4096, prot_readprot_write, map_privatemap_anonymous, -1. Fdopen instead of open(dst, 'wb in pyfile procedure which additionally rectifies the problem with symlink attack. However, i am not sure that the last one is portable and won't mess with another code. I have prepared *untested* patches for both propositions. Best regards, radoslaw. Ctypes is a python extension module, included in the stdlib, that allows you to call an arbitrary function in a shared library directly from Python. This approach allows you to interface with C-code directly from Python. This opens up an enormous number of libraries for use from. The drawback, however, is that coding mistakes lab can lead to ugly program crashes very easily (just as can happen in C) because there is little type or bounds checking done on the parameters.
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Featured oreilly conferences, see all events, building the future. Oreilly learning provides individuals, teams, and businesses with expert-created and curated information covering all the areas that will shape our future—including artificial intelligence, operations, data, ux design, finance, leadership, and more. Explore the oreilly approach. Py and py2 first copy a file content and afterwards change permissions of a destination file. Unfortunately, the sequence isn't atomical and may lead to disclosure of matter of any file that is being duplicated. Additionally, pyfile procedure seems to have a problem with symlinks that could result in the corruption of content of any file on filesystem (in favorable conditions). Some functions from shutil module that depend on copy2 writing (and thus copyfile) are vulnerable too. For example, ve is using copy2 when name fails because of file transfer between filesystems. I have attached listing from strace(1) system utility below that illustrates the disclosure problem.