CWE-335: Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG)

Extended Description

PRNGs are deterministic and, while their output appears random, they cannot actually create entropy. They rely on cryptographically secure and unique seeds for entropy so proper seeding is critical to the secure operation of the PRNG. Management of seeds could be broken down into two main areas: (1) protecting seeds as cryptographic material (such as a cryptographic key); (2) whenever possible, using a uniquely generated seed from a cryptographically secure source PRNGs require a seed as input to generate a stream of numbers that are functionally indistinguishable from random numbers. While the output is, in many cases, sufficient for cryptographic uses, the output of any PRNG is directly determined by the seed provided as input. If the seed can be ascertained by a third party, the entire output of the PRNG can be made known to them. As such, the seed should be kept secret and should ideally not be able to be guessed. For example, the current time may be a poor seed. Knowing the approximate time the PRNG was seeded greatly reduces the possible key space. Seeds do not necessarily need to be unique, but reusing seeds may open up attacks if the seed is discovered.

Code Examples & CVEs

Demonstrative Examples

The following code uses a statistical PRNG to generate account IDs.

Because the program uses the same seed value for every invocation of the PRNG, its values are predictable, making the system vulnerable to attack.

BadJava

private static final long SEED = 1234567890;public int generateAccountID() {Random random = new Random(SEED);return random.nextInt();}

Both of these examples use a statistical PRNG seeded with the current value of the system clock to generate a random number:

An attacker can easily predict the seed used by these PRNGs, and so also predict the stream of random numbers generated. Note these examples also exhibit CWE-338 (Use of Cryptographically Weak PRNG).

BadJava

Random random = new Random(System.currentTimeMillis());int accountID = random.nextInt();

Both of these examples use a statistical PRNG seeded with the current value of the system clock to generate a random number:

An attacker can easily predict the seed used by these PRNGs, and so also predict the stream of random numbers generated. Note these examples also exhibit CWE-338 (Use of Cryptographically Weak PRNG).

BadC

srand(time());int randNum = rand();

This code grabs some random bytes and uses them for a seed in a PRNG, in order to generate a new cryptographic key.

Since only 2 bytes are used as a seed, an attacker will only need to guess 2^16 (65,536) values before being able to replicate the state of the PRNG.

BadPython

# getting 2 bytes of randomness for the seeding the PRNG  				  seed = os.urandom(2) 				  random.seed(a=seed) 				  key = random.getrandbits(128)

Observed CVE Examples (4)

CVE-2020-7010

Cloud application on Kubernetes generates passwords using a weak random number generator based on deployment time.

View Details

CVE-2019-11495

server uses erlang:now() to seed the PRNG, which results in a small search space for potential random seeds

View Details

CVE-2018-12520

Product's PRNG is not seeded for the generation of session IDs

View Details

CVE-2016-10180

Router's PIN generation is based on rand(time(0)) seeding.

View Details

Source-backed guidance

Additional facts reviewed against primary or authoritative security sources.

Detect Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) with Automated Static Analysis

Documented detection approaches include Automated Static Analysis; recorded impacts include Bypass Protection Mechanism, and Other; observed examples include CVE-2020-7010, CVE-2019-11495, and CVE-2018-12520. Use these source-defined anchors to turn CWE-335 into implementation, review, and verification checks for the affected component.

CWE-335: Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) — MITRE CWE

Prevent CWE-335 through an SSDF control plan

Use NIST SSDF practices to make prevention of CWE-335, Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG), an explicit development outcome. Translate the CWE definition into security requirements and design constraints, choose safer implementation patterns and toolchain checks, and define acceptance evidence before release. Feed every confirmed occurrence back into the requirements, design review, and coding rules so the same root cause is removed across the product rather than patched in one location.

NIST SP 800-218 Secure Software Development Framework — National Institute of Standards and Technology

Triage CWE-335 against known exploitation evidence

Use CISA's Known Exploited Vulnerabilities catalog to test whether a vulnerability mapped to CWE-335, Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG), has evidence of exploitation in the wild. Confirm the CVE-to-CWE root-cause mapping independently before attaching the example, then capture the affected product, required action, and remediation deadline. A missing KEV match is not evidence that the weakness is unexploited, and a KEV entry must not be generalized to every occurrence of this CWE.

Known Exploited Vulnerabilities Catalog — Cybersecurity and Infrastructure Security Agency

Apply precise root-cause mapping to CWE-335

Apply MITRE's full root-cause mapping guidance when using CWE-335, Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG). Separate weakness language from attacker prerequisites and technical impact, check the entry's abstraction and vulnerability-mapping notes, and prefer the most specific Base or Variant supported by the evidence. Record the rejected alternatives and require an independent review before the mapping is used for remediation trends or program metrics.

CVE to CWE Root Cause Mapping Guidance — MITRE CWE

Validate CWE-335 with root-cause mapping checks

Apply MITRE's root-cause mapping quick tips to CWE-335, Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG). Confirm the finding describes the causal weakness rather than an impact or attack pattern, compare the abstraction and mapping notes with plausible alternatives, and have a second reviewer challenge the selection. Preserve the evidence and reasoning so recurring defects can be measured against one consistent identifier.

CVE to CWE Root Cause Mapping Quick Tips — MITRE CWE

Frequently Asked Questions

What is CWE-335: Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG)?+

CWE-335: Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) is a Common Weakness Enumeration (CWE) entry maintained by MITRE. The product uses a Pseudo-Random Number Generator (PRNG) but does not correctly manage seeds. PRNGs are deterministic and, while their output appears random, they cannot actually create entropy. They rely on cryptographically secure and unique seeds for entropy so proper seeding is critical to the secure operation of the PRNG. Management of seeds could be broken down into two main areas: (1) protecting seeds as cryptographic material (such as a cryptographic key); (2) whenever possible, using a uniquely generated seed from a cryptographically secure source PRNGs require a seed as input to generate a stream of numbers that are functionally indistinguishable from random numbers. While the output is, in many cases, sufficient for cryptographic uses, the output of any PRNG is directly determined by the seed provided as input. If the seed can be ascertained by a third party, the entire output of the PRNG can be made known to them. As such, the seed should be kept secret and should ideally not be able to be guessed. For example, the current time may be a poor seed. Knowing the approximate time the PRNG was seeded greatly reduces the possible key space. Seeds do not necessarily need to be unique, but reusing seeds may open up attacks if the seed is discovered.

What are the security consequences of Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG)?+

If exploited, CWE-335 (Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG)) it can compromise Access Control and Other, leading to outcomes such as Bypass Protection Mechanism and Other.

Which programming languages are affected by Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG)?+

CWE-335 commonly affects Not Language-Specific. Note that weaknesses are often language-agnostic patterns, so secure coding practices apply broadly.

What are real-world examples of Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG)?+

MITRE documents real CVEs mapped to CWE-335, including CVE-2020-7010, CVE-2019-11495, CVE-2018-12520 and CVE-2016-10180. You can look up the full details of each CVE, including CVSS scores and remediation guidance, on our CVE Lookup tool.

What is the difference between a CWE and a CVE?+

A CWE (Common Weakness Enumeration) like CWE-335 describes a category of software weakness — the underlying flaw type. A CVE (Common Vulnerabilities and Exposures) identifies a specific, real-world vulnerability in a particular product. In short, a CWE is the kind of mistake, and a CVE is an instance of that mistake being found in software.

CWE-335: Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG)

Extended Description

Technical Details

Applicable To

Security Consequences

Scope

Impact

Mitigation Strategies

Detection Methods

Code Examples & CVEs

Demonstrative Examples

Observed CVE Examples (4)

CWE Relationships

Parent Weaknesses

Source-backed guidance

Detect Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) with Automated Static Analysis

Prevent CWE-335 through an SSDF control plan

Triage CWE-335 against known exploitation evidence

Apply precise root-cause mapping to CWE-335

Validate CWE-335 with root-cause mapping checks

Frequently Asked Questions

Learn More