PEP 506 – Adding A Secrets Module To The Standard Library | peps.python.org (original) (raw)

Author:

Steven D’Aprano

Status:

Final

Type:

Standards Track

Created:

19-Sep-2015

Python-Version:

3.6

Post-History:

Table of Contents

• Abstract
• Definitions
• Rationale
• Proposal
• API and Implementation
  • Default arguments
  • Naming conventions
• Alternatives
• Comparison To Other Languages
• What Should Be The Name Of The Module?
• Frequently Asked Questions
• References
• Copyright

Abstract

This PEP proposes the addition of a module for common security-related functions such as generating tokens to the Python standard library.

Definitions

Some common abbreviations used in this proposal:

• PRNG:
  Pseudo Random Number Generator. A deterministic algorithm used to produce random-looking numbers with certain desirable statistical properties.
• CSPRNG:
  Cryptographically Strong Pseudo Random Number Generator. An algorithm used to produce random-looking numbers which are resistant to prediction.
• MT:
  Mersenne Twister. An extensively studied PRNG which is currently used by the random module as the default.

Rationale

This proposal is motivated by concerns that Python’s standard library makes it too easy for developers to inadvertently make serious security errors. Theo de Raadt, the founder of OpenBSD, contacted Guido van Rossum and expressed some concern [1] about the use of MT for generating sensitive information such as passwords, secure tokens, session keys and similar.

Although the documentation for the random module explicitly states that the default is not suitable for security purposes [2], it is strongly believed that this warning may be missed, ignored or misunderstood by many Python developers. In particular:

• developers may not have read the documentation and consequently not seen the warning;
• they may not realise that their specific use of the module has security implications; or
• not realising that there could be a problem, they have copied code (or learned techniques) from websites which don’t offer best practises.

The first [3] hit when searching for “python how to generate passwords” on Google is a tutorial that uses the default functions from the randommodule [4]. Although it is not intended for use in web applications, it is likely that similar techniques find themselves used in that situation. The second hit is to a StackOverflow question about generating passwords [5]. Most of the answers given, including the accepted one, use the default functions. When one user warned that the default could be easily compromised, they were told “I think you worry too much.” [6]

This strongly suggests that the existing random module is an attractive nuisance when it comes to generating (for example) passwords or secure tokens.

Additional motivation (of a more philosophical bent) can be found in the post which first proposed this idea [7].

Proposal

Alternative proposals have focused on the default PRNG in the randommodule, with the aim of providing “secure by default” cryptographically strong primitives that developers can build upon without thinking about security. (See Alternatives below.) This proposes a different approach:

• The standard library already provides cryptographically strong primitives, but many users don’t know they exist or when to use them.
• Instead of requiring crypto-naive users to write secure code, the standard library should include a set of ready-to-use “batteries” for the most common needs, such as generating secure tokens. This code will both directly satisfy a need (“How do I generate a password reset token?”), and act as an example of acceptable practises which developers can learn from [8].

To do this, this PEP proposes that we add a new module to the standard library, with the suggested name secrets. This module will contain a set of ready-to-use functions for common activities with security implications, together with some lower-level primitives.

The suggestion is that secrets becomes the go-to module for dealing with anything which should remain secret (passwords, tokens, etc.) while the random module remains backward-compatible.

API and Implementation

This PEP proposes the following functions for the secrets module:

• Functions for generating tokens suitable for use in (e.g.) password recovery, as session keys, etc., in the following formats:
  • as bytes, secrets.token_bytes;
  • as text, using hexadecimal digits, secrets.token_hex;
  • as text, using URL-safe base-64 encoding, secrets.token_urlsafe.
• A limited interface to the system CSPRNG, using either os.urandomdirectly or random.SystemRandom. Unlike the random module, this does not need to provide methods for seeding, getting or setting the state, or any non-uniform distributions. It should provide the following:
  • A function for choosing items from a sequence, secrets.choice.
  • A function for generating a given number of random bits and/or bytes as an integer, secrets.randbits.
  • A function for returning a random integer in the half-open range 0 to the given upper limit, secrets.randbelow [9].
• A function for comparing text or bytes digests for equality while being resistant to timing attacks, secrets.compare_digest.

The consensus appears to be that there is no need to add a new CSPRNG to the random module to support these uses, SystemRandom will be sufficient.

Some illustrative implementations have been given by Alyssa (Nick) Coghlan [10]and a minimalist API by Tim Peters [11]. This idea has also been discussed on the issue tracker for the “cryptography” module [12]. The following pseudo-code should be taken as the starting point for the real implementation:

from random import SystemRandom from hmac import compare_digest

_sysrand = SystemRandom()

randbits = _sysrand.getrandbits choice = _sysrand.choice

def randbelow(exclusive_upper_bound): return _sysrand._randbelow(exclusive_upper_bound)

DEFAULT_ENTROPY = 32 # bytes

def token_bytes(nbytes=None): if nbytes is None: nbytes = DEFAULT_ENTROPY return os.urandom(nbytes)

def token_hex(nbytes=None): return binascii.hexlify(token_bytes(nbytes)).decode('ascii')

def token_urlsafe(nbytes=None): tok = token_bytes(nbytes) return base64.urlsafe_b64encode(tok).rstrip(b'=').decode('ascii')

The secrets module itself will be pure Python, and other Python implementations can easily make use of it unchanged, or adapt it as necessary. An implementation can be found on BitBucket [13].

Default arguments

One difficult question is “How many bytes should my token be?”. We can help with this question by providing a default amount of entropy for the “token_*” functions. If the nbytes argument is None or not given, the default entropy will be used. This default value should be large enough to be expected to be secure for medium-security uses, but is expected to change in the future, possibly even in a maintenance release [14].

Naming conventions

One question is the naming conventions used in the module [15], whether to use C-like naming conventions such as “randrange” or more Pythonic names such as “random_range”.

Functions which are simply bound methods of the private SystemRandominstance (e.g. randrange), or a thin wrapper around such, should keep the familiar names. Those which are something new (such as the varioustoken_* functions) will use more Pythonic names.

Alternatives

One alternative is to change the default PRNG provided by the randommodule [16]. This received considerable scepticism and outright opposition:

• There is fear that a CSPRNG may be slower than the current PRNG (which in the case of MT is already quite slow).
• Some applications (such as scientific simulations, and replaying gameplay) require the ability to seed the PRNG into a known state, which a CSPRNG lacks by design.
• Another major use of the random module is for simple “guess a number” games written by beginners, and many people are loath to make any change to the random module which may make that harder.
• Although there is no proposal to remove MT from the random module, there was considerable hostility to the idea of having to opt-in to a non-CSPRNG or any backwards-incompatible changes.
• Demonstrated attacks against MT are typically against PHP applications. It is believed that PHP’s version of MT is a significantly softer target than Python’s version, due to a poor seeding technique [17]. Consequently, without a proven attack against Python applications, many people object to a backwards-incompatible change.

Alyssa Coghlan made an earlier suggestionfor a globally configurable PRNG which uses the system CSPRNG by default, but has since withdrawn it in favour of this proposal.

Comparison To Other Languages

• PHP
  PHP includes a function uniqid [18] which by default returns a thirteen character string based on the current time in microseconds. Translated into Python syntax, it has the following signature:
  def uniqid(prefix='', more_entropy=False)->str
  The PHP documentation warns that this function is not suitable for security purposes. Nevertheless, various mature, well-known PHP applications use it for that purpose (citation needed).
  PHP 5.3 and better also includes a function openssl_random_pseudo_bytes [19]. Translated into Python syntax, it has roughly the following signature:
  def openssl_random_pseudo_bytes(length:int)->Tuple[str, bool]
  This function returns a pseudo-random string of bytes of the given length, and a boolean flag giving whether the string is considered cryptographically strong. The PHP manual suggests that returning anything but True should be rare except for old or broken platforms.
• JavaScript
  Based on a rather cursory search [20], there do not appear to be any well-known standard functions for producing strong random values in JavaScript. Math.random is often used, despite serious weaknesses making it unsuitable for cryptographic purposes [21]. In recent years the majority of browsers have gained support for window.crypto.getRandomValues [22].
  Node.js offers a rich cryptographic module, crypto [23], most of which is beyond the scope of this PEP. It does include a single function for generating random bytes, crypto.randomBytes.
• Ruby
  The Ruby standard library includes a module SecureRandom [24]which includes the following methods:
  • base64 - returns a Base64 encoded random string.
  • hex - returns a random hexadecimal string.
  • random_bytes - returns a random byte string.
  • random_number - depending on the argument, returns either a random integer in the range(0, n), or a random float between 0.0 and 1.0.
  • urlsafe_base64 - returns a random URL-safe Base64 encoded string.
  • uuid - return a version 4 random Universally Unique IDentifier.

What Should Be The Name Of The Module?

There was a proposal to add a “random.safe” submodule, quoting the Zen of Python “Namespaces are one honking great idea” koan. However, the author of the Zen, Tim Peters, has come out against this idea [25], and recommends a top-level module.

In discussion on the python-ideas mailing list so far, the name “secrets” has received some approval, and no strong opposition.

There is already an existing third-party module with the same name [26], but it appears to be unused and abandoned.

Frequently Asked Questions

• Q: Is this a real problem? Surely MT is random enough that nobody can predict its output.
  A: The consensus among security professionals is that MT is not safe in security contexts. It is not difficult to reconstruct the internal state of MT [27] [28] and so predict all past and future values. There are a number of known, practical attacks on systems using MT for randomness [29].
• Q: Attacks on PHP are one thing, but are there any known attacks on Python software?
  A: Yes. There have been vulnerabilities in Zope and Plone at the very least. Hanno Schlichting commented [30]:
  "In the context of Plone and Zope a practical attack was
  demonstrated, but I can't find any good non-broken links about
  this anymore. IIRC Plone generated a random number and exposed
  this on each error page along the lines of 'Sorry, you encountered
  an error, your problem has been filed as , please
  include this when you contact us'. This allowed anyone to do large
  numbers of requests to this page and get enough random values to
  reconstruct the MT state. A couple of security related modules used
  random instead of system random (cookie session ids, password reset
  links, auth token), so the attacker could break all of those."
  Christian Heimes reported this issue to the Zope security team in 2012 [31], there are at least two related CVE vulnerabilities [32], and at least one work-around for this issue in Django [33].
• Q: Is this an alternative to specialist cryptographic software such as SSL?
  A: No. This is a “batteries included” solution, not a full-featured “nuclear reactor”. It is intended to mitigate against some basic security errors, not be a solution to all security-related issues. To quote Alyssa Coghlan referring to her earlier proposal [34]:
  "...folks really are better off learning to use things like
  cryptography.io for security sensitive software, so this change
  is just about harm mitigation given that it's inevitable that a
  non-trivial proportion of the millions of current and future
  Python developers won't do that."
• Q: What about a password generator?
  A: The consensus is that the requirements for password generators are too variable for it to be a good match for the standard library [35]. No password generator will be included in the initial release of the module, instead it will be given in the documentation as a recipe (à la the recipes in theitertools module) [36].
• Q: Will secrets use /dev/random (which blocks) or /dev/urandom (which doesn’t block) on Linux? What about other platforms?
  A: secrets will be based on os.urandom and random.SystemRandom, which are interfaces to your operating system’s best source of cryptographic randomness. On Linux, that may be /dev/urandom [37], on Windows it may beCryptGenRandom(), but see the documentation and/or source code for the detailed implementation details.

References

Copyright

This document has been placed in the public domain.