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Analysis of a strong Random Number Generator

Type
Video
Tags
cryptography
Authors
Thomas Biege
Event
Chaos Communication Congress 23th (23C3) 2006
Indexed on
Mar 27, 2013
URL
http://dewy.fem.tu-ilmenau.de/CCC/23C3/video/23C3-1420-en-strong_random_number_generator.m4v
File name
23C3-1420-en-strong_random_number_generator.m4v
File size
670.6 MB
MD5
af7d6dd2e178a8c0a07c97aae47f31e8
SHA1
dd9af8833893f690c42a96c00b7494bceca66cdc

This paper (and slides) will descibe the inner workings of the the random number generator (/dev/{u}random) of Linux. Additionally some possible security flaws are shown (entropy overestimation, zero'izing the pool, etc.) Almost all cryptographic protocols depend on random (unpredictable) values to create keys, cookies, tokens, initialisation vectors, and so on. The Linux (as well as other Unix flavours) kernel provides a character device as a source for randomness. This device represents the essential part needed by various cryptographic protocol implementations for a secure operation (conditional security), therefore it needs special attention from security experts. This paper will give an extract of results taken from analysing the input sources used by Linux' PRNG implementation. The statistical entropy of each source and of the whole pool is calculated to get a better picture of the entropy quality during the boot--process and to spot entropy overestimation by the kernel. Observation taken by process show a repeating behaviour for different system startups. This can be used by an attacker to create profiles and to simulate a more complex system. Even observations of the events generated by the block-device show timing patterns between different boot--sequences. To dispel doubts of developers to add untrusted sources, two kinds of untrusted sources, low-quality and malicious source, were examined. It will be shown that low--quality sources are not able to reduce the entropy in the pool that already exists but can lead to an overestimation. A more dangerous situation exists with the presence of a malicious source which is theoretically able to led the mixing algorithm produce a stream of zeros. The goal of this work is not to show a practical attack against the random device but to provide more transparency and to ease further analysis.

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