Troubleshooting Random Number Generation Issues
Incorrect random number generation causes security vulnerabilities, biased results, and non-reproducible tests. This guide covers common RNG pitfalls and how to verify your random numbers are truly random.
Key Takeaways
- Understanding the difference between PRNG and CSPRNG is critical for choosing the right tool for each use case.
- Using `random() % n` to generate numbers in a range introduces bias when the random source's range isn't evenly divisible by n.
- Histogram test**: Generate many numbers and verify uniform distribution.
- ## Types of Random Number Generators Understanding the difference between PRNG and CSPRNG is critical for choosing the right tool for each use case.
Fake Data Generator
Types of Random Number Generators
Understanding the difference between PRNG and CSPRNG is critical for choosing the right tool for each use case.
PRNG (Pseudo-Random Number Generator)
Deterministic algorithms that produce sequences that appear random. Given the same seed, they produce the same sequence. Examples: Mersenne Twister, xoshiro256.
CSPRNG (Cryptographically Secure PRNG)
PRNGs that are safe for security applications. Their output is indistinguishable from true randomness. Examples: /dev/urandom, crypto.getRandomValues().
Common Issues
Modulo Bias
Using random() % n to generate numbers in a range introduces bias when the random source's range isn't evenly divisible by n. For example, random_byte() % 100 slightly favors values 0-55.
Fix: Use rejection sampling โ discard values that would cause bias.
Seed Predictability
Seeding a PRNG with the current timestamp makes the output predictable to anyone who knows approximately when the generator was initialized.
Fix: Use OS-provided entropy sources for seeds, or use CSPRNGs.
Insufficient Entropy
Systems with low entropy (embedded devices, early boot) may produce predictable random numbers.
Fix: Wait for entropy pool to fill, use hardware RNG if available.
Testing Randomness
- Histogram test: Generate many numbers and verify uniform distribution.
- Chi-squared test: Statistical test for deviation from expected distribution.
- Sequence correlation: Check that consecutive values aren't correlated.
- Bit pattern analysis: Verify all bit positions have approximately equal 0s and 1s.
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