Mutation-based Fuzzing

Technique

Mutation-based fuzzing is a fuzzing technique in which the fuzzer explores a target by mutating test artifacts such as instruction test vectors, bytestreams, or prompt templates. In the provided evidence, it is used inside coverage-guided fuzzing for RISC-V instruction-set-simulator and processor verification, as a counterexample-generation method for model learning, and as the basis of TurboFuzzLLM for black-box LLM jailbreaking-template discovery.

First seen 5/25/2026

Last seen 5/29/2026

Evidence 11 chunks

Wiki v4

WIKI

Mutation-based Fuzzing

Mutation-based fuzzing is a fuzzing technique in which exploration is driven by mutations to test artifacts. In the provided evidence, those artifacts include RISC-V instruction test vectors in coverage-guided processor verification, counterexample candidates for reactive-system model learning, and prompt templates for LLM jailbreak testing. [Mutation-based fuzzing scope]

Relationship to coverage-guided fuzzing

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RELATIONSHIPS

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Coverage-guided Fuzzing ← uses 100% 3e

Coverage-guided fuzzing employs mutation-based techniques to generate test inputs.

Efficient Cross-Level Processor Verification using Coverage-guided Fuzzing ← uses 95% 1e

The paper uses mutation-based algorithms in its coverage-guided fuzzing approach.

LINKED ENTITIES

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Coverage-guided Fuzzing USES Extracted graph relationship

CITATIONS

8 sources

8 citations — click to expand

[1] Mutation-based fuzzing scope Efficient Cross-Level Processor Verification using Coverage-guided Fuzzing

[2] ISS-tailored coverage-guided mutation Verifying Instruction Set Simulators using Coverage-guided Fuzzing

[3] Fuzzing complements testcase generation Verifying Instruction Set Simulators using Coverage-guided Fuzzing

[4] Fast Exploration mutation Efficient Cross-Level Processor Verification using Coverage-guided Fuzzing

[5] Enhanced Havoc mutation Efficient Cross-Level Processor Verification using Coverage-guided Fuzzing

[6] Enhanced AFL results Efficient Cross-Level Processor Verification using Coverage-guided Fuzzing

[7] Model learning counterexamples Complementing Model Learning with Mutation-Based Fuzzing

[8] TurboFuzzLLM mutation-based fuzzing TurboFuzzLLM: Turbocharging Mutation-based Fuzzing for Effectively Jailbreaking Large Language Models in Practice