The Python secrets module claims to produce cryptographically secure random numbers. I did some research on which random number generator is used when you call the secrets module on Windows. I found the following:
_PyOS_URandom() from Python/bootstrap_hash.c
_PyOS_URandom() uses the Windows CryptGenRandom algorithm.
However, Leo Dorrendorf stated in his article that the design of the CryptGenRandom algorithm is flawed. In principle, an attacker can `predict all random values, such as SSL keys, used by a process in all its past and future operation'.
Wikipedia has the following to say about it:
A cryptanalysis of CryptGenRandom, published in November 2007 by Leo Dorrendorf and others from the Hebrew University of Jerusalem and University of Haifa, found significant weaknesses in the Windows 2000 implementation of the algorithm.
To take advantage of the vulnerability, an attacker would first need to compromise the program running the random number generator. The weaknesses in the paper all depend on an attacker siphoning the state bits out of the generator. An attacker in a position to carry out this attack would typically already be in a position to defeat any random number generator (for instance, they can simply sniff the outputs of the generator, or fix them in memory to known values). However, the Hebrew University team notes that an attacker only need steal the state bits once in order to persistently violate the security of a CryptGenRandom instance. They can also use the information they glean to determine past random numbers that were generated, potentially compromising information, such as credit card numbers, already sent.
The paper's attacks are based on the fact that CryptGenRandom uses the stream cipher RC4, which can be run backwards once its state is known. They also take advantage of the fact that CryptGenRandom runs in user mode, allowing anyone who gains access to the operating system at user level, for example by exploiting a buffer overflow, to get CryptGenRandom's state information for that process. Finally, CryptGenRandom refreshes its seed from entropy infrequently. This problem is aggravated by the fact that each Win32 process has its own instance of CryptGenRandom state; while this means that a compromise of one process does not transitively compromise every other process, it may also increase the longevity of any successful break.
Because the details of the CryptGenRandom algorithm are not public, Dorrendorf's team used reverse engineering tools to discern how the algorithm works. Their paper is the first published record of how the Windows cryptographic random number generator operates.
Does this mean that on Windows, the random numbers produced by the Python secrets module (and other modules that use CryptGenRandom) are not cryptographically secure?