Python-Dev June 2006

python-dev@python.org

144 participants
235 discussions

by Rotem Yaari

Hello everyone! We have been encountering several deadlocks in a threaded Python application which calls subprocess.Popen (i.e. fork()) in some of its threads. This has occurred on Python 2.4.1 on a 2.4.27 Linux kernel. Preliminary analysis of the hang shows that the child process blocks upon entering the execvp function, in which the import_lock is acquired due to the following line: def _ execvpe(file, args, env=None): from errno import ENOENT, ENOTDIR ... It is known that when forking from a pthreaded application, acquisition attempts on locks which were already locked by other threads while fork() was called will deadlock. Due to these oddities we were wondering if it would be better to extract the above import line from the execvpe call, to prevent lock acquisition attempts in such cases. Another workaround could be re-assigning a new lock to import_lock (such a thing is done with the global interpreter lock) at PyOS_AfterFork or pthread_atfork. We'd appreciate any opinions you might have on the subject. Thanks in advance, Yair and Rotem

11 years

Re: subprocess and EINTR errnos

by Peter Astrand

On Wed, 10 Nov 2004, John P Speno wrote: Hi, sorry for the delayed response. > While using subprocess (aka popen5), I came across one potential gotcha. I've had > exceptions ending like this: > > File "test.py", line 5, in test > cmd = popen5.Popen(args, stdout=PIPE) > File "popen5.py", line 577, in __init__ > data = os.read(errpipe_read, 1048576) # Exceptions limited to 1 MB > OSError: [Errno 4] Interrupted system call > > (on Solaris 9) > > Would it make sense for subprocess to use a more robust read() function > which can handle these cases, i.e. when the parent's read on the pipe > to the child's stderr is interrupted by a system call, and returns EINTR? > I imagine it could catch EINTR and EAGAIN and retry the failed read(). I assume you are using signals in your application? The os.read above is not the only system call that can fail with EINTR. subprocess.py is full of other system calls that can fail, and I suspect that many other Python modules are as well. I've made a patch (attached) to subprocess.py (and test_subprocess.py) that should guard against EINTR, but I haven't committed it yet. It's quite large. Are Python modules supposed to handle EINTR? Why not let the C code handle this? Or, perhaps the signal module should provide a sigaction function, so that users can use SA_RESTART. Index: subprocess.py =================================================================== RCS file: /cvsroot/python/python/dist/src/Lib/subprocess.py,v retrieving revision 1.8 diff -u -r1.8 subprocess.py --- subprocess.py 7 Nov 2004 14:30:34 -0000 1.8 +++ subprocess.py 17 Nov 2004 19:42:30 -0000 @@ -888,6 +888,50 @@ pass + def _read_no_intr(self, fd, buffersize): + """Like os.read, but retries on EINTR""" + while True: + try: + return os.read(fd, buffersize) + except OSError, e: + if e.errno == errno.EINTR: + continue + else: + raise + + + def _read_all(self, fd, buffersize): + """Like os.read, but retries on EINTR, and reads until EOF""" + all = "" + while True: + data = self._read_no_intr(fd, buffersize) + all += data + if data == "": + return all + + + def _write_no_intr(self, fd, s): + """Like os.write, but retries on EINTR""" + while True: + try: + return os.write(fd, s) + except OSError, e: + if e.errno == errno.EINTR: + continue + else: + raise + + def _waitpid_no_intr(self, pid, options): + """Like os.waitpid, but retries on EINTR""" + while True: + try: + return os.waitpid(pid, options) + except OSError, e: + if e.errno == errno.EINTR: + continue + else: + raise + def _execute_child(self, args, executable, preexec_fn, close_fds, cwd, env, universal_newlines, startupinfo, creationflags, shell, @@ -963,7 +1007,7 @@ exc_value, tb) exc_value.child_traceback = ''.join(exc_lines) - os.write(errpipe_write, pickle.dumps(exc_value)) + self._write_no_intr(errpipe_write, pickle.dumps(exc_value)) # This exitcode won't be reported to applications, so it # really doesn't matter what we return. @@ -979,7 +1023,7 @@ os.close(errwrite) # Wait for exec to fail or succeed; possibly raising exception - data = os.read(errpipe_read, 1048576) # Exceptions limited to 1 MB + data = self._read_all(errpipe_read, 1048576) # Exceptions limited to 1 MB os.close(errpipe_read) if data != "": child_exception = pickle.loads(data) @@ -1003,7 +1047,7 @@ attribute.""" if self.returncode == None: try: - pid, sts = os.waitpid(self.pid, os.WNOHANG) + pid, sts = self._waitpid_no_intr(self.pid, os.WNOHANG) if pid == self.pid: self._handle_exitstatus(sts) except os.error: @@ -1015,7 +1059,7 @@ """Wait for child process to terminate. Returns returncode attribute.""" if self.returncode == None: - pid, sts = os.waitpid(self.pid, 0) + pid, sts = self._waitpid_no_intr(self.pid, 0) self._handle_exitstatus(sts) return self.returncode @@ -1049,27 +1093,33 @@ stderr = [] while read_set or write_set: - rlist, wlist, xlist = select.select(read_set, write_set, []) + try: + rlist, wlist, xlist = select.select(read_set, write_set, []) + except select.error, e: + if e[0] == errno.EINTR: + continue + else: + raise if self.stdin in wlist: # When select has indicated that the file is writable, # we can write up to PIPE_BUF bytes without risk # blocking. POSIX defines PIPE_BUF >= 512 - bytes_written = os.write(self.stdin.fileno(), input[:512]) + bytes_written = self._write_no_intr(self.stdin.fileno(), input[:512]) input = input[bytes_written:] if not input: self.stdin.close() write_set.remove(self.stdin) if self.stdout in rlist: - data = os.read(self.stdout.fileno(), 1024) + data = self._read_no_intr(self.stdout.fileno(), 1024) if data == "": self.stdout.close() read_set.remove(self.stdout) stdout.append(data) if self.stderr in rlist: - data = os.read(self.stderr.fileno(), 1024) + data = self._read_no_intr(self.stderr.fileno(), 1024) if data == "": self.stderr.close() read_set.remove(self.stderr) Index: test/test_subprocess.py =================================================================== RCS file: /cvsroot/python/python/dist/src/Lib/test/test_subprocess.py,v retrieving revision 1.14 diff -u -r1.14 test_subprocess.py --- test/test_subprocess.py 12 Nov 2004 15:51:48 -0000 1.14 +++ test/test_subprocess.py 17 Nov 2004 19:42:30 -0000 @@ -7,6 +7,7 @@ import tempfile import time import re +import errno mswindows = (sys.platform == "win32") @@ -35,6 +36,16 @@ fname = tempfile.mktemp() return os.open(fname, os.O_RDWR|os.O_CREAT), fname + def read_no_intr(self, obj): + while True: + try: + return obj.read() + except IOError, e: + if e.errno == errno.EINTR: + continue + else: + raise + # # Generic tests # @@ -123,7 +134,7 @@ p = subprocess.Popen([sys.executable, "-c", 'import sys; sys.stdout.write("orange")'], stdout=subprocess.PIPE) - self.assertEqual(p.stdout.read(), "orange") + self.assertEqual(self.read_no_intr(p.stdout), "orange") def test_stdout_filedes(self): # stdout is set to open file descriptor @@ -151,7 +162,7 @@ p = subprocess.Popen([sys.executable, "-c", 'import sys; sys.stderr.write("strawberry")'], stderr=subprocess.PIPE) - self.assertEqual(remove_stderr_debug_decorations(p.stderr.read()), + self.assertEqual(remove_stderr_debug_decorations(self.read_no_intr(p.stderr)), "strawberry") def test_stderr_filedes(self): @@ -186,7 +197,7 @@ 'sys.stderr.write("orange")'], stdout=subprocess.PIPE, stderr=subprocess.STDOUT) - output = p.stdout.read() + output = self.read_no_intr(p.stdout) stripped = remove_stderr_debug_decorations(output) self.assertEqual(stripped, "appleorange") @@ -220,7 +231,7 @@ stdout=subprocess.PIPE, cwd=tmpdir) normcase = os.path.normcase - self.assertEqual(normcase(p.stdout.read()), normcase(tmpdir)) + self.assertEqual(normcase(self.read_no_intr(p.stdout)), normcase(tmpdir)) def test_env(self): newenv = os.environ.copy() @@ -230,7 +241,7 @@ 'sys.stdout.write(os.getenv("FRUIT"))'], stdout=subprocess.PIPE, env=newenv) - self.assertEqual(p.stdout.read(), "orange") + self.assertEqual(self.read_no_intr(p.stdout), "orange") def test_communicate(self): p = subprocess.Popen([sys.executable, "-c", @@ -305,7 +316,8 @@ 'sys.stdout.write("\\nline6");'], stdout=subprocess.PIPE, universal_newlines=1) - stdout = p.stdout.read() + + stdout = self.read_no_intr(p.stdout) if hasattr(open, 'newlines'): # Interpreter with universal newline support self.assertEqual(stdout, @@ -343,7 +355,7 @@ def test_no_leaking(self): # Make sure we leak no resources - max_handles = 1026 # too much for most UNIX systems + max_handles = 10 # too much for most UNIX systems if mswindows: max_handles = 65 # a full test is too slow on Windows for i in range(max_handles): @@ -424,7 +436,7 @@ 'sys.stdout.write(os.getenv("FRUIT"))'], stdout=subprocess.PIPE, preexec_fn=lambda: os.putenv("FRUIT", "apple")) - self.assertEqual(p.stdout.read(), "apple") + self.assertEqual(self.read_no_intr(p.stdout), "apple") def test_args_string(self): # args is a string @@ -457,7 +469,7 @@ p = subprocess.Popen(["echo $FRUIT"], shell=1, stdout=subprocess.PIPE, env=newenv) - self.assertEqual(p.stdout.read().strip(), "apple") + self.assertEqual(self.read_no_intr(p.stdout).strip(), "apple") def test_shell_string(self): # Run command through the shell (string) @@ -466,7 +478,7 @@ p = subprocess.Popen("echo $FRUIT", shell=1, stdout=subprocess.PIPE, env=newenv) - self.assertEqual(p.stdout.read().strip(), "apple") + self.assertEqual(self.read_no_intr(p.stdout).strip(), "apple") def test_call_string(self): # call() function with string argument on UNIX @@ -525,7 +537,7 @@ p = subprocess.Popen(["set"], shell=1, stdout=subprocess.PIPE, env=newenv) - self.assertNotEqual(p.stdout.read().find("physalis"), -1) + self.assertNotEqual(self.read_no_intr(p.stdout).find("physalis"), -1) def test_shell_string(self): # Run command through the shell (string) @@ -534,7 +546,7 @@ p = subprocess.Popen("set", shell=1, stdout=subprocess.PIPE, env=newenv) - self.assertNotEqual(p.stdout.read().find("physalis"), -1) + self.assertNotEqual(self.read_no_intr(p.stdout).find("physalis"), -1) def test_call_string(self): # call() function with string argument on Windows /Peter Åstrand <astrand(a)lysator.liu.se>

11 years, 2 months

Re: [Python-Dev] [Python-checkins] r45510 - python/trunk/Lib/pkgutil.py python/trunk/Lib/pydoc.py

by M.-A. Lemburg

Phillip.eby wrote: > Author: phillip.eby > Date: Tue Apr 18 02:59:55 2006 > New Revision: 45510 > > Modified: > python/trunk/Lib/pkgutil.py > python/trunk/Lib/pydoc.py > Log: > Second phase of refactoring for runpy, pkgutil, pydoc, and setuptools > to share common PEP 302 support code, as described here: > > http://mail.python.org/pipermail/python-dev/2006-April/063724.html Shouldn't this new module be named "pkglib" to be in line with the naming scheme used for all the other utility modules, e.g. httplib, imaplib, poplib, etc. ? > pydoc now supports PEP 302 importers, by way of utility functions in > pkgutil, such as 'walk_packages()'. It will properly document > modules that are in zip files, and is backward compatible to Python > 2.3 (setuptools installs for Python <2.5 will bundle it so pydoc > doesn't break when used with eggs.) Are you saying that the installation of setuptools in Python 2.3 and 2.4 will then overwrite the standard pydoc included with those versions ? I think that's the wrong way to go if not made an explicit option in the installation process or a separate installation altogether. I bothered by the fact that installing setuptools actually changes the standard Python installation by either overriding stdlib modules or monkey-patching them at setuptools import time. > What has not changed is that pydoc command line options do not support > zip paths or other importer paths, and the webserver index does not > support sys.meta_path. Those are probably okay as limitations. > > Tasks remaining: write docs and Misc/NEWS for pkgutil/pydoc changes, > and update setuptools to use pkgutil wherever possible, then add it > to the stdlib. Add setuptools to the stdlib ? I'm still missing the PEP for this along with the needed discussion touching among other things, the change of the distutils standard "python setup.py install" to install an egg instead of a site package. -- Marc-Andre Lemburg eGenix.com Professional Python Services directly from the Source (#1, Apr 18 2006) >>> Python/Zope Consulting and Support ... http://www.egenix.com/ >>> mxODBC.Zope.Database.Adapter ... http://zope.egenix.com/ >>> mxODBC, mxDateTime, mxTextTools ... http://python.egenix.com/ ________________________________________________________________________ ::: Try mxODBC.Zope.DA for Windows,Linux,Solaris,FreeBSD for free ! ::::

12 years, 6 months

Making builtins more efficient

by Steven Elliott

I'm interested in how builtins could be more efficient. I've read over some of the PEPs having to do with making global variables more efficient (search for "global"): http://www.python.org/doc/essays/pepparade.html But I think the problem can be simplified by focusing strictly on builtins. One of my assumptions is that only a small fractions of modules override the default builtins with something like: import mybuiltins __builtins__ = mybuiltins As you probably know each access of a builtin requires two hash table lookups. First, the builtin is not found in the list of globals. It is then found in the list of builtins. Why not have a means of referencing the default builtins with some sort of index the way the LOAD_FAST op code currently works? In other words, by default each module gets the default set of builtins indexed (where the index indexes into an array) in a certain order. The version stored in the pyc file would be bumped each time the set of default builtins is changed. I don't have very strong feelings whether things like True = (1 == 1) would be a syntax error, but assigning to a builtin could just do the equivalent of STORE_FAST. I also don't have very strong feelings about whether the array of default builtins would be shared between modules. To simulate the current behavior where attempting to assign to builtin actually alters that module's global hashtable a separate array of builtins could be used for each module. As to assigning to __builtins__ (like I mentioned at the beginning of this post) perhaps it could assign to the builtin array for those items that have a name that matches a default builtin (such as "True" or "len"). Those items that don't match a default builtin would just create global variables. Perhaps what I'm suggesting isn't feasible for reasons that have already been discussed. But it seems like it should be possible to make "while True" as efficient as "while 1". -- ----------------------------------------------------------------------- | Steven Elliott | selliott4(a)austin.rr.com | -----------------------------------------------------------------------

13 years, 5 months

GeneratorExit inheriting from Exception

by Nick Coghlan

Should GeneratorExit inherit from Exception or BaseException? Currently, a generator that catches Exception and continues on to yield another value can't be closed properly (you get a runtime error pointing out that the generator ignored GeneratorExit). The only decent reference I could find to it in the old PEP 348/352 discussions is Guido writing [1]: > when GeneratorExit or StopIteration > reach the outer level of an app, it's a bug like all the others that > bare 'except:' WANTS to catch. (at that point in the conversation, I believe bare except was considered the equivalent of "except Exception:") While I agree with what Guido says about GeneratorExit being a bug if it reaches the outer level of an app, it seems like a bit of a trap that a correctly written generator can't write "except Exception:" without preceding it with an "except GeneratorExit:" that reraises the exception. Isn't that exactly the idiom we're trying to get rid of for SystemExit and KeyboardInterrupt? Regards, Nick. [1] http://mail.python.org/pipermail/python-dev/2005-August/055173.html -- Nick Coghlan | ncoghlan(a)gmail.com | Brisbane, Australia --------------------------------------------------------------- http://www.boredomandlaziness.org

13 years, 7 months

Plea to distribute debugging lib

by David Abrahams

For years, Boost.Python has been doing some hacks to work around the fact that a Windows Python distro doesn't include the debug build of the library. http://www.boost.org/libs/python/doc/building.html#variants explains. We wanted to make it reasonably convenient for Windows developers (and our distributed testers) to work with a debug build of the Boost.Python library and of their own code. Having to download the Python source and build the debug DLL was deemed unacceptable. Well, those hacks have run out of road. VC++8 now detects that some of its headers have been #included with _DEBUG and some without, and it will refuse to build anything when it does. We have several new hacks to work around that detection, and I think we _might_ be able to get away with them for one more release. But it's really time to do it right. MS is recommending that we (Boost) start distributing a debug build of the Python DLL with Boost, but Boost really seems like the wrong place to host such a thing. Is there any way Python.org can make a debug build more accessible? Thanks, Dave -- Dave Abrahams Boost Consulting www.boost-consulting.com

13 years, 11 months

Long options support

by Georg Brandl

I've just closed a bug report wishing for long option support, pointing to a patch sitting in the patch tracker implementing this. Should we accept at least the very common options "--help" and "--version" in 2.5? Georg

14 years, 2 months

2.5 and beyond

by Neal Norwitz

I'm glad to see Anthony ratcheting down. At this point, we need to be fixing bugs and improving doc. Maybe Anthony and I should have a contest to see who can revert the most changes. :-) There are at least 6 bugs that really, really need to be fixed before release. Several of these are AST bugs. Jeremy knows about them and plans to fix them once he's back from vacation. Anyone else wanna help out? One is for a socket problem and another is for doc. The current list of serious bugs are in the PEP: http://www.python.org/dev/peps/pep-0356/ If there are any bugs you think should be considered show stoppers, mail them to the list and I will update the PEP. If you are a committer, just update the PEP yourself. We really need everyone to help. There were a lot of changes that didn't have tests and/or NEWS entries. I tried to reply to the checkin messages for those I noticed. I have tons of messages in my inbox where I don't know if the issue was addressed or not. Can everyone try to find the holes? And new ones keep popping up! Please let the author know they need to fix the problem. It's really tempting to just revert these changes... We also need to fix the test suite. This warning needs to be addressed! Lib/struct.py:63: DeprecationWarning: struct integer overflow masking is deprecated return o.pack(*args) Since we are in feature freeze, now seems like a good time to make a PEP for 2.6: http://www.python.org/dev/peps/pep-0361/ It's pretty empty right now. The plan is to make the schedule in a year from now. Start adding your new features to the PEP, not the code. n

14 years, 2 months

Add new PyErr_WarnEx() to 2.5?

by Tim Peters

[Ronald Oussoren, hijacking the "test_struct failure on 64 bit platforms" thread] > The really annoying part of the new struct warnings is that the > warning line mentions a line in struct.py instead the caller of > struct.pack. That makes it hard to find the source of the > warning without telling the warnings module to raise an > exception for DeprecationWarnings. The problem seems to be that Python's C API apparently gives no simple way to supply a value for warning.warn's optional `stacklevel` argument. The C-level signature is: int PyErr_Warn(PyObject *category, char *message); and that's what _struct.c calls. I think it would be good to add a new int PyErr_WarnEx(PyObject *category, char *message, long stacklevel); C API function, change PyErr_Warn to call that forcing stacklevel to 1, and change _struct.c to call PyErr_WarnEx with stacklevel=2. Then it would point at struct.pack()'s caller.

14 years, 2 months

doc for new restricted execution design for Python

by Brett Cannon

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 JavaScript on the web. Since this is dealing with security and messing that up can be costly, I am sending it to the list for any possible feedback. 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. Goal ============================= 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. .. contents:: Use Cases ///////////////////////////// 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. Stand-Alone Python ================== 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 fulfill. 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 ///////////////////////////// XXX Threading? XXX CPU? Filesystem =================== 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. Physical Resources =================== 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 (DoS) attacks. Networking =================== 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 zombie). 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. Interpreter =================== 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 unacceptable. 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 gained. 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. Resource Hiding ============================= 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 violated. 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 performed. 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 permission. 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 breaches. 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. Resource Crippling ============================= 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 method level. 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 its action. 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 list. 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__[0])[6]['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 own problems. 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, discussed below). 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. Implementation Details =============================== 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 mean to. 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 discouraged!). API -------------- * 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. * PyXXX_Trusted(error_return) Macro that has the caller return with 'error_return' if the interpreter is not a trusted one. Memory ============================= Protection -------------- 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. Why -------------- 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. API -------------- * 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 nothing. * 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. CPU ============================= XXX Needed? Difficult to get right for all platforms. Would have to be very platform-specific. Reading/Writing Files ============================= Protection -------------- 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. Why -------------- 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. API -------------- * 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 ============================ Protection -------------- 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? Why -------------- 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*() function. 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`_. API -------------- * 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 modules explicitly? * 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 NULL. * 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 ========================== Protection -------------- 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). Why -------------- 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. API -------------- Use PyXXX_Trusted() to protect unsafe code from being executed. Hostile Bytecode ============================= Protection -------------- The code object's constructor is not callable from Python. Importation of .pyc and .pyo files is also prohibited. Why -------------- 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 ----------------------- None known. API -------------- None. Changing the Behaviour of the Interpreter ========================================= Protection -------------- Only a subset of the 'sys' module will be made available to untrusted interpreters. Things to allow from the sys module: * byteorder * subversion * copyright * displayhook * excepthook * __displayhook__ * __excepthook__ * exc_info * exc_clear * exit * getdefaultencoding * _getframe * hexversion * last_type * last_value * last_traceback * maxint * maxunicode * modules * stdin # See `Stdin, Stdout, and Stderr`_. * stdout * stderr * __stdin__ # See `Stdin, Stdout, and Stderr`_ XXX Perhaps not needed? * __stdout__ * __stderr__ * version * api_version Why -------------- 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). API -------------- None. Socket Usage ============================= Protection -------------- Allow sending and receiving data to/from specific IP addresses on specific ports. Why -------------- 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. API -------------- * 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, return NULL. * 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 do nothing. * 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. Network Information ============================= Protection -------------- 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 communicate with. Why -------------- 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. API -------------- * 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. * PyXXX_CheckNetworkInfo(error_return) 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. Filesystem Information ============================= Protection -------------- 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 Why -------------- 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. API -------------- * int PyXXX_AllowFilesystemInfo(interpreter) Allow the untrusted interpreter to expose filesystem information. If the passed-in interpreter is not untrusted, return NULL. * PyXXX_CheckFilesystemInfo(error_return) 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 XXX. Threading ============================= XXX Needed? Stdin, Stdout, and Stderr ============================= Protection -------------- 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. Why -------------- Interference with stdin, stdout, or stderr should not be allowed unless desired. Possible Security Flaws ----------------------- Unless cStringIO instances can be used maliciously, none to speak of. XXX Use StringIO instances instead for even better security? API -------------- * int PyXXX_UseTrueStdin(interpreter) int PyXXX_UseTrueStdout(interpreter) int PyXXX_UseTrueStderr(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 ============================= Protection -------------- 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 as well. 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. Why -------------- 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 system. 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. API -------------- XXX Could also have PyXXXExtended prefix instead for the following functions + Bool * 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. + Membership * 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, return NULL. * 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 nothing. + 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. References /////////////////////////////////////// .. [#rexec] The 'rexec' module (http://docs.python.org/lib/module-rexec.html) .. [#safe-tcl] The Safe-Tcl Security Model (http://research.sun.com/technical-reports/1997/abstract-60.html) .. [#ctypes] 'ctypes' module (http://docs.python.org/dev/lib/module-ctypes.html)

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Python-Dev June 2006