aeondave/radamsa
offensive-tools/fuzzing/radamsa/SKILL.md
Auth/lab ref: General-purpose mutation engine for generating malformed test inputs.
$ install --global
skillsauthnpx skillsauth add aeondave/malskill radamsaInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Jun 8, 2026, 3:50 AM117.0s1 file scanned
SKILL.md
- 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.
- 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
- Collect representative seed samples (multiple real-world variants).
- Generate mutations in deterministic mode when triaging (
--seed). - Pipe outputs into target/harness and monitor crash/timeout/resource metrics.
- Preserve crashing case + seed metadata.
- 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 (
--seedpatterns).
Practical Tricks
- Use
%noutput patterns with-oto 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
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