CWE - CWE-330: Use of Insufficiently Random Values (4.16)

Weakness ID: 330

Vulnerability Mapping: DISCOURAGED This CWE ID should not be used to map to real-world vulnerabilities
Abstraction: Class Class - a weakness that is described in a very abstract fashion, typically independent of any specific language or technology. More specific than a Pillar Weakness, but more general than a Base Weakness. Class level weaknesses typically describe issues in terms of 1 or 2 of the following dimensions: behavior, property, and resource.

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Description

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

Extended Description

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.

Common Consequences

This table specifies different individual consequences associated with the weakness. The Scope identifies the application security area that is violated, while the Impact describes the negative technical impact that arises if an adversary succeeds in exploiting this weakness. The Likelihood provides information about how likely the specific consequence is expected to be seen relative to the other consequences in the list. For example, there may be high likelihood that a weakness will be exploited to achieve a certain impact, but a low likelihood that it will be exploited to achieve a different impact.

Scope	Impact	Likelihood
Confidentiality Other	Technical Impact: 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	Technical Impact: Bypass Protection Mechanism; Other If product 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	Technical Impact: Bypass Protection Mechanism; Gain Privileges or 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.

Potential Mitigations

Phase: 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.

Phase: Implementation

Consider a PRNG that re-seeds itself as needed from high quality pseudo-random output sources, such as hardware devices.

Phase: 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

Strategy: Libraries or Frameworks

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").

Phase: 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.

Relationships

This table shows the weaknesses and high level categories that are related to this weakness. These relationships are defined as ChildOf, ParentOf, MemberOf and give insight to similar items that may exist at higher and lower levels of abstraction. In addition, relationships such as PeerOf and CanAlsoBe are defined to show similar weaknesses that the user may want to explore.

Relevant to the view "Research Concepts" (CWE-1000)

Nature	Type	ID	Name
ChildOf	Pillar - a weakness that is the most abstract type of weakness and represents a theme for all class/base/variant weaknesses related to it. A Pillar is different from a Category as a Pillar is still technically a type of weakness that describes a mistake, while a Category represents a common characteristic used to group related things.	693	Protection Mechanism Failure
ParentOf	Base - a weakness that is still mostly independent of a resource or technology, but with sufficient details to provide specific methods for detection and prevention. Base level weaknesses typically describe issues in terms of 2 or 3 of the following dimensions: behavior, property, technology, language, and resource.	331	Insufficient Entropy
ParentOf	Base - a weakness that is still mostly independent of a resource or technology, but with sufficient details to provide specific methods for detection and prevention. Base level weaknesses typically describe issues in terms of 2 or 3 of the following dimensions: behavior, property, technology, language, and resource.	334	Small Space of Random Values
ParentOf	Base - a weakness that is still mostly independent of a resource or technology, but with sufficient details to provide specific methods for detection and prevention. Base level weaknesses typically describe issues in terms of 2 or 3 of the following dimensions: behavior, property, technology, language, and resource.	335	Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG)
ParentOf	Base - a weakness that is still mostly independent of a resource or technology, but with sufficient details to provide specific methods for detection and prevention. Base level weaknesses typically describe issues in terms of 2 or 3 of the following dimensions: behavior, property, technology, language, and resource.	338	Use of Cryptographically Weak Pseudo-Random Number Generator (PRNG)
ParentOf	Class - a weakness that is described in a very abstract fashion, typically independent of any specific language or technology. More specific than a Pillar Weakness, but more general than a Base Weakness. Class level weaknesses typically describe issues in terms of 1 or 2 of the following dimensions: behavior, property, and resource.	340	Generation of Predictable Numbers or Identifiers
ParentOf	Base - a weakness that is still mostly independent of a resource or technology, but with sufficient details to provide specific methods for detection and prevention. Base level weaknesses typically describe issues in terms of 2 or 3 of the following dimensions: behavior, property, technology, language, and resource.	344	Use of Invariant Value in Dynamically Changing Context
ParentOf	Base - a weakness that is still mostly independent of a resource or technology, but with sufficient details to provide specific methods for detection and prevention. Base level weaknesses typically describe issues in terms of 2 or 3 of the following dimensions: behavior, property, technology, language, and resource.	1204	Generation of Weak Initialization Vector (IV)
ParentOf	Base - a weakness that is still mostly independent of a resource or technology, but with sufficient details to provide specific methods for detection and prevention. Base level weaknesses typically describe issues in terms of 2 or 3 of the following dimensions: behavior, property, technology, language, and resource.	1241	Use of Predictable Algorithm in Random Number Generator
CanPrecede	Base - a weakness that is still mostly independent of a resource or technology, but with sufficient details to provide specific methods for detection and prevention. Base level weaknesses typically describe issues in terms of 2 or 3 of the following dimensions: behavior, property, technology, language, and resource.	804	Guessable CAPTCHA

Relevant to the view "Weaknesses for Simplified Mapping of Published Vulnerabilities" (CWE-1003)

Nature	Type	ID	Name
MemberOf	View - a subset of CWE entries that provides a way of examining CWE content. The two main view structures are Slices (flat lists) and Graphs (containing relationships between entries).	1003	Weaknesses for Simplified Mapping of Published Vulnerabilities
ParentOf	Base - a weakness that is still mostly independent of a resource or technology, but with sufficient details to provide specific methods for detection and prevention. Base level weaknesses typically describe issues in terms of 2 or 3 of the following dimensions: behavior, property, technology, language, and resource.	331	Insufficient Entropy
ParentOf	Base - a weakness that is still mostly independent of a resource or technology, but with sufficient details to provide specific methods for detection and prevention. Base level weaknesses typically describe issues in terms of 2 or 3 of the following dimensions: behavior, property, technology, language, and resource.	335	Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG)
ParentOf	Base - a weakness that is still mostly independent of a resource or technology, but with sufficient details to provide specific methods for detection and prevention. Base level weaknesses typically describe issues in terms of 2 or 3 of the following dimensions: behavior, property, technology, language, and resource.	338	Use of Cryptographically Weak Pseudo-Random Number Generator (PRNG)

Relevant to the view "Architectural Concepts" (CWE-1008)

Nature	Type	ID	Name
MemberOf	Category - a CWE entry that contains a set of other entries that share a common characteristic.	1013	Encrypt Data

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

The different Modes of Introduction provide information about how and when this weakness may be introduced. The Phase identifies a point in the life cycle at which introduction may occur, while the Note provides a typical scenario related to introduction during the given phase.

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

Applicable Platforms

This listing shows possible areas for which the given weakness could appear. These may be for specific named Languages, Operating Systems, Architectures, Paradigms, Technologies, or a class of such platforms. The platform is listed along with how frequently the given weakness appears for that instance.

Languages

Class: Not Language-Specific (Undetermined Prevalence)

Technologies

Class: Not Technology-Specific (Undetermined Prevalence)

Likelihood Of Exploit

High

Demonstrative Examples

Example 1

This code attempts to generate a unique random identifier for a user's session.

(bad code)

Example Language: 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).

Example 2

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.

(bad code)

Example Language: 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.

Observed Examples

Reference	Description
CVE-2021-3692	PHP framework uses mt_rand() function (Marsenne Twister) when generating tokens
CVE-2020-7010	Cloud application on Kubernetes generates passwords using a weak random number generator based on deployment time.
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-2008-2108	Chain: insufficient precision (CWE-1339) in random-number generator causes some zero bits to be reliably generated, reducing the amount of entropy (CWE-331)
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.

Weakness Ordinalities

Ordinality	Description
Primary	(where the weakness exists independent of other weaknesses)

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