The software may use insufficiently random numbers or values in a security context that depends on unpredictable numbers. When software generates predictable values in a context requiring unpredictability, it may be possible for an attacker to guess the next value that will be generated, and use this guess to impersonate another user or access sensitive information. 900 Category ChildOf 867 800 Category ChildOf 808 700 699 Category ChildOf 254 711 Category ChildOf 723 734 Category ChildOf 747 750 Category ChildOf 753 844 Category ChildOf 861 868 Category ChildOf 883 888 Category ChildOf 905 This can be primary to many other weaknesses such as cryptographic errors, authentication errors, symlink following, information leaks, and others. Primary Computers are deterministic machines, and as such are unable to produce true randomness. Pseudo-Random Number Generators (PRNGs) approximate randomness algorithmically, starting with a seed from which subsequent values are calculated. There are two types of PRNGs: statistical and cryptographic. Statistical PRNGs provide useful statistical properties, but their output is highly predictable and forms an easy to reproduce numeric stream that is unsuitable for use in cases where security depends on generated values being unpredictable. Cryptographic PRNGs address this problem by generating output that is more difficult to predict. For a value to be cryptographically secure, it must be impossible or highly improbable for an attacker to distinguish between it and a truly random value. Architecture and Design Implementation Medium to High Confidentiality Other Other When a protection mechanism relies on random values to restrict access to a sensitive resource, such as a session ID or a seed for generating a cryptographic key, then the resource being protected could be accessed by guessing the ID or key. Access_Control Other Bypass protection mechanism Other If software relies on unique, unguessable IDs to identify a resource, an attacker might be able to guess an ID for a resource that is owned by another user. The attacker could then read the resource, or pre-create a resource with the same ID to prevent the legitimate program from properly sending the resource to the intended user. For example, a product might maintain session information in a file whose name is based on a username. An attacker could pre-create this file for a victim user, then set the permissions so that the application cannot generate the session for the victim, preventing the victim from using the application. Access_Control Bypass protection mechanism Gain privileges / assume identity When an authorization or authentication mechanism relies on random values to restrict access to restricted functionality, such as a session ID or a seed for generating a cryptographic key, then an attacker may access the restricted functionality by guessing the ID or key. Black Box Use monitoring tools that examine the software's process as it interacts with the operating system and the network. This technique is useful in cases when source code is unavailable, if the software was not developed by you, or if you want to verify that the build phase did not introduce any new weaknesses. Examples include debuggers that directly attach to the running process; system-call tracing utilities such as truss (Solaris) and strace (Linux); system activity monitors such as FileMon, RegMon, Process Monitor, and other Sysinternals utilities (Windows); and sniffers and protocol analyzers that monitor network traffic. Attach the monitor to the process and look for library functions that indicate when randomness is being used. Run the process multiple times to see if the seed changes. Look for accesses of devices or equivalent resources that are commonly used for strong (or weak) randomness, such as /dev/urandom on Linux. Look for library or system calls that access predictable information such as process IDs and system time. Architecture and Design Use a well-vetted algorithm that is currently considered to be strong by experts in the field, and select well-tested implementations with adequate length seeds. In general, if a pseudo-random number generator is not advertised as being cryptographically secure, then it is probably a statistical PRNG and should not be used in security-sensitive contexts. Pseudo-random number generators can produce predictable numbers if the generator is known and the seed can be guessed. A 256-bit seed is a good starting point for producing a "random enough" number. Implementation Consider a PRNG that re-seeds itself as needed from high quality pseudo-random output sources, such as hardware devices. Testing Use automated static analysis tools that target this type of weakness. Many modern techniques use data flow analysis to minimize the number of false positives. This is not a perfect solution, since 100% accuracy and coverage are not feasible. Architecture and Design Requirements Libraries or Frameworks Use products or modules that conform to FIPS 140-2 [R.330.1] to avoid obvious entropy problems. Consult FIPS 140-2 Annex C ("Approved Random Number Generators"). Testing Use tools and techniques that require manual (human) analysis, such as penetration testing, threat modeling, and interactive tools that allow the tester to record and modify an active session. These may be more effective than strictly automated techniques. This is especially the case with weaknesses that are related to design and business rules. This code generates a unique random identifier for a user's session. PHP function generateSessionID($userID){ srand($userID); return rand(); } Because the seed for the PRNG is always the user's ID, the session ID will always be the same. An attacker could thus predict any user's session ID and potentially hijack the session. This example also exhibits a Small Seed Space (CWE-339). The following code uses a statistical PRNG to create a URL for a receipt that remains active for some period of time after a purchase. Java String GenerateReceiptURL(String baseUrl) { Random ranGen = new Random(); ranGen.setSeed((new Date()).getTime()); return(baseUrl + ranGen.nextInt(400000000) + ".html"); } This code uses the Random.nextInt() function to generate "unique" identifiers for the receipt pages it generates. Because Random.nextInt() is a statistical PRNG, it is easy for an attacker to guess the strings it generates. Although the underlying design of the receipt system is also faulty, it would be more secure if it used a random number generator that did not produce predictable receipt identifiers, such as a cryptographic PRNG. CVE-2009-3278 Crypto product uses rand() library function to generate a recovery key, making it easier to conduct brute force attacks. CVE-2009-3238 Random number generator can repeatedly generate the same value. CVE-2009-2367 Web application generates predictable session IDs, allowing session hijacking. CVE-2009-2158 Password recovery utility generates a relatively small number of random passwords, simplifying brute force attacks. CVE-2009-0255 Cryptographic key created with a seed based on the system time. CVE-2008-5162 Kernel function does not have a good entropy source just after boot. CVE-2008-4905 Blogging software uses a hard-coded salt when calculating a password hash. CVE-2008-4929 Bulletin board application uses insufficiently random names for uploaded files, allowing other users to access private files. CVE-2008-3612 Handheld device uses predictable TCP sequence numbers, allowing spoofing or hijacking of TCP connections. CVE-2008-2433 Web management console generates session IDs based on the login time, making it easier to conduct session hijacking. CVE-2008-0166 SSL library uses a weak random number generator that only generates 65,536 unique keys. CVE-2008-2108 Chain: insufficient precision causes extra zero bits to be assigned, reducing entropy for an API function that generates random numbers. CVE-2008-2020 CAPTCHA implementation does not produce enough different images, allowing bypass using a database of all possible checksums. CVE-2008-0087 DNS client uses predictable DNS transaction IDs, allowing DNS spoofing. CVE-2008-0141 Application generates passwords that are based on the time of day. Non-specific Cryptography Authentication Session management Information Technology Laboratory, National Institute of Standards and Technology SECURITY REQUIREMENTS FOR CRYPTOGRAPHIC MODULES 2001-05-25 http://csrc.nist.gov/publications/fips/fips140-2/fips1402.pdf John Viega Gary McGraw Building Secure Software: How to Avoid Security Problems the Right Way 1st Edition Addison-Wesley 2002 M. Howard D. LeBlanc Writing Secure Code Chapter 8, "Using Poor Random Numbers" Page 259 2nd Edition Microsoft 2002 Michael Howard David LeBlanc John Viega 24 Deadly Sins of Software Security "Sin 20: Weak Random Numbers." Page 299 McGraw-Hill 2010 Randomness and Predictability Insecure Randomness Broken Access Control A2 CWE_More_Specific Do not use the rand() function for generating pseudorandom numbers MSC30-C Brute Force 11 Credential/Session Prediction 18 Generate strong random numbers MSC02-J Do not use the rand() function for generating pseudorandom numbers MSC30-CPP Ensure your random number generator is properly seeded MSC32-CPP 112 281 59 PLOVER Eric Dalci Cigital 2008-07-01 updated Time_of_Introduction CWE Content Team MITRE 2008-09-08 updated Background_Details, Relationships, Other_Notes, Relationship_Notes, Taxonomy_Mappings, Weakness_Ordinalities CWE Content Team MITRE 2008-11-24 updated Relationships, Taxonomy_Mappings CWE Content Team MITRE 2009-01-12 updated Description, Likelihood_of_Exploit, Other_Notes, Potential_Mitigations, Relationships CWE Content Team MITRE 2009-03-10 updated Potential_Mitigations CWE Content Team MITRE 2009-05-27 updated Demonstrative_Examples, Related_Attack_Patterns CWE Content Team MITRE 2009-12-28 updated Applicable_Platforms, Common_Consequences, Description, Observed_Examples, Potential_Mitigations, Time_of_Introduction CWE Content Team MITRE 2010-02-16 updated References, Relationships, Taxonomy_Mappings CWE Content Team MITRE 2010-04-05 updated Related_Attack_Patterns CWE Content Team MITRE 2010-06-21 updated Detection_Factors, Potential_Mitigations CWE Content Team MITRE 2011-03-29 updated Demonstrative_Examples CWE Content Team MITRE 2011-06-01 updated Common_Consequences, Relationships, Taxonomy_Mappings CWE Content Team MITRE 2011-06-27 updated Relationships CWE Content Team MITRE 2011-09-13 updated Potential_Mitigations, References, Relationships, Taxonomy_Mappings CWE Content Team MITRE 2012-05-11 updated Demonstrative_Examples, Observed_Examples, References, Relationships Randomness and Predictability