Top 10 Random Number Generators for Developers in 2025Random numbers power everything from simulations and games to cryptography and statistical sampling. Choosing the right random number generator (RNG) depends on your requirements: speed, security, reproducibility, distribution quality, or platform compatibility. This article reviews the top 10 RNGs developers should consider in 2025, with practical notes on use cases, strengths, weaknesses, and example language bindings or libraries.
1) Fortuna (and modern successors)
Fortuna is a well-regarded cryptographically secure PRNG (CSPRNG) design that emphasizes robust entropy accumulation and reseeding. Modern implementations have refined the original approach and integrated hardware entropy sources.
- Best for: cryptographic applications, secure tokens, long-running servers requiring strong forward/backward secrecy.
- Strengths: well-designed entropy pooling and reseeding; strong security proofs when implemented correctly.
- Weaknesses: more complex to implement correctly; requires careful entropy collection.
- Example libraries: OpenSSL (with Fortuna-like constructs in some versions), libsodium for high-level secure RNG access.
2) ChaCha20-based CSPRNGs
ChaCha20-based generators (e.g., ChaCha20-PRNG) are widely used as CSPRNGs because ChaCha20 is fast, simple, and well-analyzed. They are often preferred over older designs like AES-CTR for software performance on platforms lacking AES acceleration.
- Best for: secure session keys, nonces, general-purpose secure randomness where speed matters.
- Strengths: high throughput in software, strong cryptographic analysis, easy to implement.
- Weaknesses: requires secure seeding; not suitable if hardware entropy is the only allowed source without careful design.
- Example libraries: libsodium (randombytes_buf uses ChaCha20 on many platforms), BoringSSL, Rust’s rand_core when using chacha-based backends.
3) Xoshiro / Xoroshiro family (xoshiro256**, xoroshiro128+)
xoshiro/xoroshiro PRNGs are high-speed, high-quality non-cryptographic generators suitable for simulations and games. They provide excellent performance and statistical quality for many non-crypto use cases.
- Best for: simulations, games, procedural content, Monte Carlo where cryptographic security is not required.
- Strengths: extremely fast, low state size, good statistical properties for non-cryptographic use.
- Weaknesses: not cryptographically secure; poor choice for security-sensitive contexts.
- Example libraries: C/C++ reference implementations, Rust’s rand crate (older versions offered xoshiro), many language ports.
4) PCG (Permuted Congruential Generator)
PCG offers a family of PRNGs with excellent statistical properties and minimal code size. Designed by Melissa O’Neill, PCG aims to provide strong statistical quality with simplicity and small state.
- Best for: general-purpose application-level random needs where reproducibility and statistical quality matter.
- Strengths: compact implementations, excellent distributional properties, easily reproducible.
- Weaknesses: still non-cryptographic; different variants give different trade-offs in period/space.
- Example libraries: PCG C/C++ reference, ports in Python, Java, Rust, and many others.
5) AES-CTR / AES-DRBG
AES-CTR used as a PRNG or the NIST-approved AES-DRBG are common choices where hardware AES acceleration is available. AES-DRBG provides an approved deterministic random bit generator with defined security margins.
- Best for: regulated environments, platforms with AES hardware acceleration, cryptographic key material generation.
- Strengths: strong security assumptions, standards-approved variants, excellent performance on AES-capable CPUs.
- Weaknesses: slower on platforms without AES acceleration; implementation complexity and side-channel considerations.
- Example libraries: OpenSSL RAND_bytes (can be backed by AES-DRBG), libs that implement NIST SP 800-90A DRBGs.
6) /dev/urandom and getrandom()
OS-provided randomness sources remain primary for seeding and for applications that require system-level entropy. Modern kernels provide robust, non-blocking generators (e.g., Linux getrandom()).
- Best for: seeding local PRNGs, general secure randomness needs where OS guarantees are acceptable.
- Strengths: integrates hardware entropy and kernel mixing; easy to use; cross-language availability.
- Weaknesses: behavior varies by OS/version (historically blocking vs non-blocking concerns), potential attacks on OS-level entropy in compromised environments.
- Example usage: getrandom(2) syscall on Linux, /dev/urandom on Unix-like systems, BCryptGenRandom on Windows.
7) Web Crypto API (crypto.getRandomValues)
For web and cross-platform JavaScript development, the Web Crypto API’s crypto.getRandomValues provides secure randomness from the browser or Node.js (crypto.randomBytes).
- Best for: browser-based cryptography, secure tokens in web apps, client-side seeding.
- Strengths: standardized API, secure backing from the platform, simple to use.
- Weaknesses: limited to environment-provided entropy and API constraints; not suitable for heavy local non-crypto PRNG needs.
- Example usage: crypto.getRandomValues(new Uint8Array(32)) in browsers; crypto.randomBytes in Node.
8) Blum Blum Shub (BBS) — academic/cryptographic niche
BBS is a theoretically strong cryptographic RNG based on number-theoretic hardness (quadratic residues). It’s primarily of academic interest and rarely used in production due to performance.
- Best for: academic study, specific high-assurance use cases where theoretical properties are required.
- Strengths: strong theoretical unpredictability based on factoring difficulty.
- Weaknesses: extremely slow, large parameters, impractical for most applications.
- Example libraries: niche cryptography libraries and academic implementations.
9) Hardware RNGs (TPM, RDRAND, / HRNG modules)
Hardware RNGs provide entropy directly from silicon — e.g., Intel RDRAND, AMD’s equivalent, TPM RNGs, and dedicated HRNG modules. Contemporary best practice is to mix hardware RNG output into a software CSPRNG rather than relying solely on it.
- Best for: seeding CSPRNGs, embedded devices, environments needing hardware-derived entropy.
- Strengths: direct entropy source, low-latency randomness.
- Weaknesses: vendor bugs or backdoor concerns (historically debated), recommended to mix with other entropy sources.
- Example usage: RDRAND instruction, TPM_GetRandom, platform APIs.
10) SplitMix64 (and similar simple split generators)
SplitMix64 is a simple, fast generator often used to initialize other PRNGs (like xoshiro) because of good speed and decent statistical properties for seeding.
- Best for: quick seeding, low-overhead non-crypto randomness, algorithm initialization.
- Strengths: extremely fast, tiny code, good for seeding larger generators.
- Weaknesses: not cryptographically secure; limited to non-security use cases.
- Example libraries: small C implementations, used internally in some PRNG setups.
Choosing the right RNG — practical guidance
- For cryptographic/security needs: use a CSPRNG (ChaCha20-based or AES-DRBG) seeded from OS entropy. Avoid xorshift/xoshiro/PCG for keys.
- For simulations, games, Monte Carlo: prefer xoshiro, PCG, or SplitMix64 for speed and reproducibility.
- For web apps: use Web Crypto API (crypto.getRandomValues).
- For embedded/hardware: mix hardware RNG output with a CSPRNG; don’t rely solely on vendor RNG without mixing.
- For reproducible research: pick a deterministic PRNG with documented seed/state (PCG or xoshiro variants are common).
Example: seeding a ChaCha20 CSPRNG in Python (concept)
import os from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes seed = os.urandom(32) # seed from OS # Use libsodium or a high-level library in production; this is conceptual.
Final notes
RNG selection should be driven by threat model and performance needs. In 2025, ChaCha20-based CSPRNGs and robust OS-provided entropy sources are the safe defaults for security, while xoshiro/PCG remain excellent for high-performance, non-cryptographic tasks.