What is Incorrect Usage of Seeds (CWE-335)?

TL;DR: Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) is a security vulnerability where predictable or static seed values are used, leading to compromised cryptographic functions. Fix by ensuring random number generators use high-quality entropy sources.

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

Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) (CWE-335) is a type of cryptographic vulnerability that occurs when pseudo-random number generators are initialized with predictable or static seed values. As defined by the MITRE Corporation under CWE-335, and classified by the OWASP Foundation under A04:2025 - Cryptographic Failures…

Quick Summary

Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) is a critical security flaw that undermines cryptographic functions. This can lead to unauthorized access, data tampering, and other severe consequences. Jump to: What it is · Why it matters · Overview · How it works · Business Impact · Attack Scenario · Detection · Fix · Framework Fixes · Ask AI · Best Practices · FAQ · Related Vulnerabilities

Jump to: Quick Summary · Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) Overview · How Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) Works · Business Impact of Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) · Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) Attack Scenario · How to Detect Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) · How to Fix Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) · Framework-Specific Fixes for Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) · How to Ask AI to Check Your Code for Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) · Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) Best Practices Checklist · Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) FAQ · Vulnerabilities Related to Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) · References · Scan Your Own Site

Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) Overview

What: A vulnerability where PRNGs are initialized with predictable or static seed values.

Why it matters: Predictable seeds compromise cryptographic functions, leading to unauthorized access and data tampering.

Where it occurs: In applications that rely on secure random numbers for encryption keys and other sensitive operations.

Who is affected: Developers and security teams responsible for cryptographic implementations in various programming languages and frameworks.

Who is NOT affected: Systems already using cryptographically secure methods for generating PRNG seeds at runtime.

How Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) Works

Root Cause

The root cause lies in the improper management or initialization of seed values used by pseudo-random number generators. Using predictable or static seeds can lead to easily guessable random numbers, compromising cryptographic functions and security mechanisms that rely on randomness.

Attack Flow

1. Attacker identifies a PRNG initialized with a static or predictable seed.
2. Predicts future random numbers generated by the PRNG based on the known seed value.
3. Uses predicted values to bypass protection mechanisms such as encryption keys or authentication tokens.

Prerequisites to Exploit

• The application must use a PRNG that is seeded with a static or easily guessable value.
• Access to the initialization code or configuration settings where the seed is set.

Vulnerable Code

import random

seed_value = 12345
random.seed(seed_value)

This code initializes the random module with a fixed seed, making future random numbers predictable and compromising security.

Secure Code

import secrets

secure_seed = secrets.token_bytes(16)
random.seed(secure_seed)

Using secrets.token_bytes() to generate a secure random seed ensures that the PRNG is initialized with high-quality entropy, preventing predictability.

Business Impact of Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG)

Confidentiality: Exposure of sensitive data such as encryption keys and authentication tokens. Integrity: Unauthorized modification of data through tampering attacks. Availability: Potential denial-of-service scenarios if random values are crucial for system operations.

• Financial loss due to unauthorized access or data breaches.
• Compliance penalties from failing security audits.
• Reputational damage from publicized vulnerabilities.

Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) Attack Scenario

1. Attacker identifies a PRNG seeded with a static value in an application’s initialization code.
2. Predicts future random numbers generated by the PRNG based on the known seed.
3. Uses predicted values to bypass encryption and gain unauthorized access to sensitive data.

How to Detect Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG)

Manual Testing

• Review code for static or predictable seed initialization.
• Check configuration files for hardcoded seeds.
• Verify that random number generators are properly seeded with secure entropy sources.

Automated Scanners (SAST / DAST)

Static analysis can detect patterns of static seed values in source code. Dynamic testing requires runtime observation to identify actual usage and predictability issues.

PenScan Detection

PenScan’s scanner engines such as ZAP, Nuclei, Wapiti, Nikto, SSLyze, Dalfox, and Nmap can flag potential CWE-335 vulnerabilities during automated scans.

False Positive Guidance

False positives may occur if the code uses a static seed but is part of a larger secure mechanism that mitigates predictability. Ensure context is considered when evaluating findings.

How to Fix Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG)

• Use cryptographically secure random number generators.
• Initialize PRNGs with high-quality entropy sources at runtime.
• Avoid static or predictable seed values.

Framework-Specific Fixes for Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG)

Python

import secrets

secure_seed = secrets.token_bytes(16)
random.seed(secure_seed)

Ensure that the secrets module is used to generate secure random seeds, preventing predictability.

Java

import java.security.SecureRandom;

SecureRandom secureRandom = new SecureRandom();
int seedValue = secureRandom.nextInt();

Use SecureRandom for generating cryptographically strong pseudo-random numbers with a high-quality entropy source.

Node.js

const crypto = require('crypto');

let secureSeed = crypto.randomBytes(16);
Math.seedrandom(secureSeed.toString('hex'));

Utilize the crypto module to generate secure random seeds, ensuring PRNGs are initialized securely.

PHP

$secure_seed = random_bytes(16);
mt_srand(bin2hex($secure_seed));

Use random_bytes() for generating cryptographically strong random values and initialize mt_rand() with a secure seed.

How to Ask AI to Check Your Code for Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG)

Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) Best Practices Checklist

✅ Use cryptographically secure random number generators. ✅ Initialize PRNGs with high-quality entropy sources at runtime. ✅ Avoid static or predictable seed values.

Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) FAQ

How does incorrect usage of seeds in PRNG affect system security?

Incorrect seed usage can lead to predictable random numbers, compromising cryptographic functions and potentially allowing unauthorized access or data tampering.

Can you show me an example of vulnerable code for CWE-335?

A common mistake is initializing a PRNG with a static value instead of a truly random one. This makes the sequence easily guessable by attackers.

Why is it important to use secure random numbers in cryptographic applications?

Secure random numbers prevent predictability, ensuring that encryption keys and other sensitive values are not guessable or reproducible.

How do automated tools identify CWE-335 vulnerabilities?

Automated scanners look for patterns like static seed initialization or predictable seeding mechanisms, flagging them as potential issues.

What are the best practices to prevent CWE-335?

Use cryptographically secure random number generators and ensure seeds are generated from a high-quality entropy source.

How can I test my application for incorrect PRNG seed usage?

Conduct code reviews, static analysis scans, and dynamic testing with fuzzing tools to identify potential issues in seed initialization.

What should developers do if they find CWE-335 vulnerabilities during a security audit?

Replace any statically defined seeds with secure random values generated at runtime using library functions designed for cryptographic purposes.

Vulnerabilities Related to Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG)

References

• MITRE
• OWASP Cryptographic Failures
• NVD NIST

Scan Your Own Site

Manual code review catches what you know to look for. An automated scan catches what you didn’t. Scan your own website using PenScan to find Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG) and other risks before an attacker does.