Insufficient Randomness Anti-Pattern

Severity: High

Summary

Insufficient randomness occurs when security-sensitive values (session tokens, password reset codes, encryption keys) are generated using predictable non-cryptographic PRNGs. AI models frequently suggest Math.random() or Python's random module for simplicity. These generators enable attackers to predict outputs after observing a few values, allowing token forgery, session hijacking, and cryptographic compromise.

The Anti-Pattern

Never use predictable, non-cryptographic random number generators for security-sensitive values.

BAD Code Example

// VULNERABLE: Using Math.random() to generate a session token.

function generateSessionToken() {
    let token = '';
    const chars = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789';
    // Math.random() is a standard PRNG, not a cryptographically secure one.
    // Its output is predictable if an attacker can observe enough previous values
    // or has some knowledge of the initial seed (which can be time-based).
    for (let i = 0; i < 32; i++) {
        token += chars.charAt(Math.floor(Math.random() * chars.length));
    }
    return token;
}

// An attacker who obtains a few of these tokens can potentially
// reverse-engineer the PRNG's internal state and predict future tokens.

GOOD Code Example

// SECURE: Using a cryptographically secure pseudo-random number generator (CSPRNG).
const crypto = require('crypto');

function generateSessionToken() {
    // `crypto.randomBytes()` generates random data using the operating system's
    // underlying entropy sources, making it unpredictable.
    // It is designed specifically for cryptographic use cases.
    const buffer = crypto.randomBytes(32); // Generate 32 bytes of random data.
    return buffer.toString('hex'); // Convert to a hex string for easy use.
}

// The resulting token is 64 characters long and has 256 bits of entropy,
// making it infeasible for an attacker to guess or predict.

Language-Specific Examples

Python:

# VULNERABLE: Using random module for security
import random
import string

def generate_reset_token():
    chars = string.ascii_letters + string.digits
    # random module is predictable - can be reversed with ~624 observations
    return ''.join(random.choice(chars) for _ in range(32))

# SECURE: Using secrets module (Python 3.6+)
import secrets

def generate_reset_token():
    # secrets module uses os.urandom() - cryptographically secure
    return secrets.token_urlsafe(32)  # 32 bytes = 256 bits

# Alternative: Using os.urandom directly
import os
import base64

def generate_session_id():
    return base64.urlsafe_b64encode(os.urandom(32)).decode('utf-8')

Java:

// VULNERABLE: Using java.util.Random for security
import java.util.Random;

public String generateSessionToken() {
    Random random = new Random(); // Predictable PRNG!
    byte[] bytes = new byte[32];
    random.nextBytes(bytes);
    return Base64.getEncoder().encodeToString(bytes);
}

// SECURE: Using SecureRandom
import java.security.SecureRandom;
import java.util.Base64;

public String generateSessionToken() {
    SecureRandom secureRandom = new SecureRandom();
    byte[] bytes = new byte[32];
    secureRandom.nextBytes(bytes);
    return Base64.getEncoder().encodeToString(bytes);
}

C#:

// VULNERABLE: Using System.Random for security
using System;

public string GenerateApiKey()
{
    var random = new Random(); // Predictable!
    var bytes = new byte[32];
    random.NextBytes(bytes);
    return Convert.ToBase64String(bytes);
}

// SECURE: Using RandomNumberGenerator
using System;
using System.Security.Cryptography;

public string GenerateApiKey()
{
    using (var rng = RandomNumberGenerator.Create())
    {
        var bytes = new byte[32];
        rng.GetBytes(bytes);
        return Convert.ToBase64String(bytes);
    }
}

Detection

Search for weak PRNGs in security contexts: Grep for non-cryptographic random functions:
- rg 'Math\.random\(\)' --type js (JavaScript)
- rg 'import random[^_]|from random import' --type py (Python random module)
- rg 'new Random\(\)|Random\.next' --type java (Java util.Random)
- rg '\brand\(|mt_rand\(' --type php (PHP rand/mt_rand)
Identify manual seeding: Find predictable seeds:
- rg 'random\.seed|Random\(time|srand\(time'
- CSPRNGs should never be manually seeded
Audit token generation: Find session/token creation logic:
- rg 'session.*token|reset.*token|api.*key' -A 10
- Verify CSPRNG usage for all security tokens

Prevention

Always use a cryptographically secure pseudo-random number generator (CSPRNG) for any security-related value.
Know your language's CSPRNG:
- Python: Use the secrets module or os.urandom().
- JavaScript (Node.js): Use crypto.randomBytes() or crypto.getRandomValues().
- Java: Use java.security.SecureRandom.
- Go: Use the crypto/rand package.
- C#: Use System.Security.Cryptography.RandomNumberGenerator.
Ensure sufficient entropy: Generate at least 128 bits (16 bytes) of randomness for tokens and unique identifiers. Use 256 bits (32 bytes) for encryption keys.
Never seed a CSPRNG manually. They are designed to automatically draw entropy from the operating system.

Related Security Patterns & Anti-Patterns

Session Fixation Anti-Pattern: Secure session ID generation is a key defense against session fixation.
Hardcoded Secrets Anti-Pattern: If an encryption key is generated with insufficient randomness, it's as bad as hardcoding a weak key.
Weak Encryption Anti-Pattern: The security of an encryption algorithm relies on the unpredictability of its key.