CWE-330 Detail

The product uses insufficiently random numbers or values in a security context that depends on unpredictable numbers.

When product 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.

Background Details

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.

Modes Of Introduction

Architecture and Design
Implementation : REALIZATION: This weakness is caused during implementation of an architectural security tactic.

Applicable Platforms

Language

Class: Not Language-Specific (Undetermined)

Technologies

Class: Not Technology-Specific (Undetermined)

Common Consequences

Observed Examples

Potential Mitigations

Phases : 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.

Phases : Implementation
Consider a PRNG that re-seeds itself as needed from high quality pseudo-random output sources, such as hardware devices.
Phases : 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.
Phases : Architecture and Design // Requirements
Use products or modules that conform to FIPS 140-2 [REF-267] to avoid obvious entropy problems. Consult FIPS 140-2 Annex C ("Approved Random Number Generators").
Phases : 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.

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.

Automated Static Analysis - Binary or Bytecode

According to SOAR, the following detection techniques may be useful:

Effectiveness : SOAR Partial

Manual Static Analysis - Binary or Bytecode

According to SOAR, the following detection techniques may be useful:

Effectiveness : SOAR Partial

Dynamic Analysis with Manual Results Interpretation

According to SOAR, the following detection techniques may be useful:

Effectiveness : SOAR Partial

Manual Static Analysis - Source Code

According to SOAR, the following detection techniques may be useful:

Effectiveness : High

Automated Static Analysis - Source Code

According to SOAR, the following detection techniques may be useful:

Effectiveness : SOAR Partial

Architecture or Design Review

According to SOAR, the following detection techniques may be useful:

Effectiveness : High

Vulnerability Mapping Notes

Justification : This CWE entry is a level-1 Class (i.e., a child of a Pillar). It might have lower-level children that would be more appropriate
Comment : Examine children of this entry to see if there is a better fit

Related Attack Patterns

NotesNotes

This can be primary to many other weaknesses such as cryptographic errors, authentication errors, symlink following, information leaks, and others.
As of CWE 4.3, CWE-330 and its descendants are being investigated by the CWE crypto team to identify gaps related to randomness and unpredictability, as well as the relationships between randomness and cryptographic primitives. This "subtree analysis" might result in the addition or deprecation of existing entries; the reorganization of relationships in some views, e.g. the research view (CWE-1000); more consistent use of terminology; and/or significant modifications to related entries.
As of CWE 4.5, terminology related to randomness, entropy, and predictability can vary widely. Within the developer and other communities, "randomness" is used heavily. However, within cryptography, "entropy" is distinct, typically implied as a measurement. There are no commonly-used definitions, even within standards documents and cryptography papers. Future versions of CWE will attempt to define these terms and, if necessary, distinguish between them in ways that are appropriate for different communities but do not reduce the usability of CWE for mapping, understanding, or other scenarios.

References

REF-267

SECURITY REQUIREMENTS FOR CRYPTOGRAPHIC MODULES
Information Technology Laboratory, National Institute of Standards and Technology.
https://csrc.nist.gov/csrc/media/publications/fips/140/2/final/documents/fips1402.pdf

REF-207

Building Secure Software: How to Avoid Security Problems the Right Way
John Viega, Gary McGraw.

REF-7

Writing Secure Code
Michael Howard, David LeBlanc.
https://www.microsoftpressstore.com/store/writing-secure-code-9780735617223

REF-44

24 Deadly Sins of Software Security
Michael Howard, David LeBlanc, John Viega.

Submission

Modifications