Random number generator attack
From CryptoDox, The Online Encyclopedia on Cryptography and Information Security
The security of cryptographic systems depends on some secret data that is known to authorized persons but unknown and unpredictable to others. To achieve this unpredictability, some randomization is typically employed. Modern cryptographic protocols often require frequent generation of random quantities (see also nonce).
Quality in the random number generation (RNG) process is almost always required for security, and lack of quality generally provides attack vulnerabilities and so to lack of security, even to complete compromise, in cryptographic systems. The RNG process is particularly attractive to attackers because it is typically a single isolated hardware or software component easy to locate. If the attacker can substitute pseudo-random bits generated in a way he can predict, security is totally compromised, yet generally undetectable by any upstream test of the bits. Furthermore, such attacks require only a single access to the system that is being compromised. No data need be sent back in contrast to, say, a computer virus that steals keys and then e-mails them to some drop point.
Human generation of random quantities
Humans generally do poorly at generating random quantities. Magicians, professional gamblers and con artists depend on the predictability of human behavior. In World War II German code clerks were instructed to select three letters at random to be the initial rotor setting for each Enigma machine message. Instead some chose predictable values like their own or a girl-friend's initials, greatly aiding allied breaking of these encryption systems. Another example is the often predictable ways computer users choose passwords. See: Password cracking.
A prominent poorly designed random number generator example
Early versions of Netscape's Secure Socket Layer (SSL) encryption protocol used pseudo-random quantities derived from a PRNG seeded with just three values, the time of day, the process ID, and the parent process ID. These quantities are often relatively predictable, making that version of SSL insecure. The problem was discovered in 1995 by Ian Goldberg and David Wagner, who had to reverse engineer the object code because Netscape refused to reveal the details of its random number generation. The SSL RNG was fixed in later releases (version 2 and higher) by more robust seeding.
Attacks on software random number generators
Just as with other components of a cryptosystem, a software random number generator should be designed to resist certain attacks. Exactly which attacks must be defended against depends on the system, but here are a few:
Attacks on hardware random number generators
A number of attacks on hardware random number generators are possible, including trying to capture radio-frequency emissions from the computer (obtaining hard drive interrupt times from motor noise, for example), or trying to feed controlled signals into a supposedly random source (such as turning off the lights in a lava lamp or feeding a strong, known signal into a sound card).
Subverted random numbers can be created using a cryptographically secure pseudorandom number generator with a seed value known to the attacker but concealed in the software. A relatively short, say 24 to 40 bit, portion of the seed can be truly random to prevent tell-tale repetitions, but not long enough to prevent the attacker from recovering, say, a "randomly" produced key.
Random numbers typically go through several layers of hardware and software before they are used. Bits may be generated in a peripheral device, sent over a serial cable, collected in an operating system utility and retrieved by a system call. The subverted bits can be substituted at any point in this process with little likelihood of detection.
A hardware circuit to produce subverted bits can be built on an integrated circuit a few millimeters square. The most sophisticated hardware random number generator can be subverted by placing such a chip anywhere upstream of where the source of randomness is digitized, say in an output driver chip or even in the cable connecting the RNG to the computer. The subversion chip can include a clock to limit the start of operation to some time after the unit is first turned on and run through acceptance tests, or it can contain a radio receiver for on/off control. It could be installed by the manufacturer at the behest of his national signals intelligence service, or added later by anyone with physical access. CPU chips with built-in hardware random number generators can be replaced by compatible chips with a subverted RNG in the chips firmware.
Designing a secure random number generator requires at least as high a level of care as designing other elements of a cryptographic system.