radamsa

name	radamsa
description	Auth/lab ref: General-purpose mutation engine for generating malformed test inputs.
license	MIT
compatibility	Cross-platform builds; canonical source is GitLab.
metadata	{"author":"GitHub Copilot","version":"1.1"}

radamsa

Mutation-focused fuzzer/generator: excellent as an input transformer in pipelines.

Quick Start

# Example mutation from file -> stdout
radamsa seed.bin > mutated.bin

# Batch generate samples
radamsa -n 100 -o out-%n.bin seeds/*

Operator Flow

1. Collect representative seed samples (multiple real-world variants).
2. Generate mutations in deterministic mode when triaging (--seed).
3. Pipe outputs into target/harness and monitor crash/timeout/resource metrics.
4. Preserve crashing case + seed metadata.
5. Replay with identical seed/options before root-cause analysis.

Best Pattern

• Use Radamsa as a feeder into:
  • web fuzzers,
  • API clients,
  • parser harnesses,
  • protocol replay tools.
• Keep reproducibility via controlled seeds when triaging (--seed patterns).

Practical Tricks

• Use %n output patterns with -o to preserve one-file-per-testcase workflows.
• Prefer many diverse seeds over one huge synthetic seed.
• For network workflows, pair with replay harnesses or server/client output modes supported by Radamsa.
• Start broad first, then constrain mutator options only if throughput or relevance requires it.

Common Pitfalls

• Treating Radamsa as coverage-guided: it is mutation-focused, not coverage-native.
• Ignoring seed quality; weak seeds produce weak campaigns.
• Running unreproducible random campaigns without seed tracking.

Notes

• GitHub mirror is stale; upstream moved to GitLab.
• Strong complement to coverage-guided engines (AFL++/libFuzzer/honggfuzz).

Resources

• https://gitlab.com/akihe/radamsa
• https://github.com/aoh/radamsa
• https://gitlab.com/akihe/radamsa/-/blob/develop/README.md