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Use of Insufficiently Random Values

ID: 330Date: (C)2012-05-14   (M)2019-10-11
Type: weaknessStatus: USABLE
Abstraction Type: Class


The software may use insufficiently random numbers or values in a security context that depends on unpredictable numbers.

Extended Description

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.

Likelihood of Exploit: Medium to High

Applicable Platforms
Language Class: Language-independent

Time Of Introduction

  • Architecture and Design
  • Implementation

Related Attack Patterns

Common Consequences

ScopeTechnical ImpactNotes
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.
Bypass protection mechanism
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.
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.

Detection Methods

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.

Potential Mitigations

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.
 Consider a PRNG that re-seeds itself as needed from high quality pseudo-random output sources, such as hardware devices.
 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
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").
 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 can be primary to many other weaknesses such as cryptographic errors, authentication errors, symlink following, information leaks, and others.

Related CWETypeViewChain
CWE-330 ChildOf CWE-905 Category CWE-888  

Demonstrative Examples   (Details)

  1. 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. (Demonstrative Example Id DX-46)
  2. This code generates a unique random identifier for a user's session. (Demonstrative Example Id DX-45)

Observed Examples

  1. CVE-2009-3278 : Crypto product uses rand() library function to generate a recovery key, making it easier to conduct brute force attacks.
  2. CVE-2009-3238 : Random number generator can repeatedly generate the same value.
  3. CVE-2009-2367 : Web application generates predictable session IDs, allowing session hijacking.
  4. CVE-2009-2158 : Password recovery utility generates a relatively small number of random passwords, simplifying brute force attacks.
  5. CVE-2009-0255 : Cryptographic key created with a seed based on the system time.
  6. CVE-2008-5162 : Kernel function does not have a good entropy source just after boot.
  7. CVE-2008-4905 : Blogging software uses a hard-coded salt when calculating a password hash.
  8. CVE-2008-4929 : Bulletin board application uses insufficiently random names for uploaded files, allowing other users to access private files.
  9. CVE-2008-3612 : Handheld device uses predictable TCP sequence numbers, allowing spoofing or hijacking of TCP connections.
  10. CVE-2008-2433 : Web management console generates session IDs based on the login time, making it easier to conduct session hijacking.
  11. CVE-2008-0166 : SSL library uses a weak random number generator that only generates 65,536 unique keys.
  12. CVE-2008-2108 : Chain: insufficient precision causes extra zero bits to be assigned, reducing entropy for an API function that generates random numbers.
  13. CVE-2008-2020 : CAPTCHA implementation does not produce enough different images, allowing bypass using a database of all possible checksums.
  14. CVE-2008-0087 : DNS client uses predictable DNS transaction IDs, allowing DNS spoofing.
  15. CVE-2008-0141 : Application generates passwords that are based on the time of day.

For more examples, refer to CVE relations in the bottom box.

White Box Definitions

Black Box Definitions

Taxynomy Mappings

PLOVER  Randomness and Predictability
7 Pernicious Kingdoms  Insecure Randomness
OWASP Top Ten 2004 A2
Broken Access Control
CERT C Secure Coding MSC30-C
Do not use the rand() function for generating pseudorandom numbers
Brute Force
Credential/Session Prediction
CERT Java Secure Coding MSC02-J
Generate strong random numbers
CERT C++ Secure Coding MSC30-CPP
Do not use the rand() function for generating pseudorandom numbers
CERT C++ Secure Coding MSC32-CPP
Ensure your random number generator is properly seeded


  1. Information Technology Laboratory, National Institute of Standards and Technology .SECURITY REQUIREMENTS FOR CRYPTOGRAPHIC MODULES. 2001-05-25.
  2. John Viega Gary McGraw .Building Secure Software: How to Avoid Security Problems the Right Way 1st Edition. Addison-Wesley. Published on 2002.
  3. M. Howard D. LeBlanc .Writing Secure Code 2nd Edition. Microsoft. Section:'Chapter 8, "Using Poor Random Numbers" Page 259'. Published on 2002.
  4. Michael Howard David LeBlanc John Viega .24 Deadly Sins of Software Security. McGraw-Hill. Section:'"Sin 20: Weak Random Numbers." Page 299'. Published on 2010.
CVE    44

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