UUID Randomness & Entropy Analyzer
Check the quality of a UUID v4's randomness. This tool analyzes the hex digits for patterns and biases, providing a simple assessment to help identify potentially weak identifiers generated by flawed random number generators.
UUID Entropy Checker
Analyze entropy and randomness of UUIDs
How to use
Enter your data in the input field, click Convert, and the result will appear in the output field. You can then copy or download the result.
How it works
Analyze the randomness and entropy of UUID strings to verify they were generated using a proper random number generator. The checker evaluates character distribution, bit patterns, and statistical randomness.
Enter one or more UUIDs to test. The tool calculates entropy metrics, checks for suspicious patterns, and flags UUIDs that may have been generated with weak randomness.
Analysis metrics:
UUID: f47ac10b-58cc-4372-a567-0e02b2c3d479 Character Distribution: 0-9: 45% (expected ~42%) a-f: 55% (expected ~58%) Shannon Entropy: 3.89 bits/char (max: 4.0) Pattern Analysis: No suspicious patterns detected Verdict: Good randomness ✓ Suspicious UUID: 00000000-0000-4000-8000-000000000000 Verdict: Low entropy - possibly test/fake ✗
The entropy checker uses Shannon entropy calculation and chi-squared tests to evaluate randomness. High entropy indicates good randomness; low entropy suggests predictable or fake UUIDs.
When You'd Actually Use This
Security audit of ID generation
Verify that your application's UUID generator produces truly random IDs. Low entropy could indicate a broken random number generator, making IDs predictable to attackers.
Detecting fake or test data
Production databases sometimes contain test UUIDs like all-zeros or sequential patterns. The entropy checker flags these for cleanup before they cause issues.
Validating third-party integrations
External services send you UUIDs. Verify they're properly generated before storing. Malformed or low-entropy UUIDs might indicate integration problems or malicious input.
Compliance and quality assurance
Some standards require cryptographically secure random identifiers. Entropy analysis provides evidence that your UUID generation meets these requirements.
Debugging random number generators
After changing RNG implementations, verify the output quality. Compare entropy before and after to ensure the new generator produces equally random UUIDs.
Forensic analysis
Investigate suspicious activity by analyzing UUID patterns. Attackers might use predictable IDs. Low entropy could reveal automated tools or compromised systems.
What to Know Before Using
UUID v1 has lower entropy by design.Time-based UUIDs (v1) include structured timestamp data, reducing randomness. Don't flag them as suspicious - their lower entropy is expected and correct.
Version and variant bits are fixed.UUID v4 has 6 fixed bits (version and variant). Maximum entropy is slightly less than 128 bits of pure randomness. The checker accounts for this.
Single UUID analysis has limitations.One UUID might look random by chance. For reliable assessment, analyze batches of UUIDs. Patterns emerge across multiple samples.
High entropy doesn't guarantee security.Random-looking UUIDs could still be predictable if the seed is known. Entropy measures distribution, not unpredictability to an attacker with inside knowledge.
Pro tip: For security-critical applications, use cryptographically secure random number generators (CSPRNG). Regular Math.random() is not sufficient for generating secure UUIDs.
Common Questions
What is Shannon entropy?
Shannon entropy measures the average information content per character. For hex digits (0-9, a-f), maximum entropy is log2(16) = 4 bits per character. Higher is more random.
What entropy value indicates good randomness?
For UUID v4, expect 3.8-4.0 bits per character. Below 3.5 suggests potential issues. Below 3.0 indicates clearly non-random data (test values, sequential IDs, etc.).
Can I use this to compare RNG quality?
Yes, generate UUIDs from different sources and compare entropy. crypto.getRandomValues() should score higher than Math.random(). This helps choose the best RNG for your needs.
What patterns should I watch for?
Repeated sequences, all-zeros, all-ones, sequential values, or heavy bias toward certain characters. These indicate weak or broken random number generation.
Is UUID v4 always random?
By specification, yes. But poor implementations might use weak RNGs. Always verify the implementation uses crypto.getRandomValues() or equivalent, not Math.random().
How many UUIDs should I test?
For statistical significance, test at least 100-1000 UUIDs. Small samples might not reveal patterns. Larger samples give more confidence in the randomness assessment.
What's a nil UUID and is it valid?
The nil UUID (all zeros) is a valid format but represents a null value. It has zero entropy and should be flagged. Don't use it as an actual identifier.
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