Seed Selection

Concept

Seed selection is described in the Instiller RTL-fuzzing work as a critical fuzzing step that chooses inputs for further fuzzing. In CPU RTL fuzzing, the cited work argues that seed selection should combine basic fuzzing heuristics with hardware-aware heuristics such as special instructions and registers.

First seen 5/27/2026

Last seen 6/3/2026

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Overview

Seed selection is a critical step in fuzzing, alongside mutation. In the context of CPU RTL fuzzing, the Instiller work identifies a gap in prior fuzzing approaches: previous work did not combine seed selection and mutation with hardware features well, and one referenced CPU-fuzzing approach did not consider seed selection when fuzzing the CPU. The paper argues that omitting hardware-related techniques limits fuzzing performance for CPU RTL testing. [C1]

Role in CPU RTL fuzzing

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RELATIONSHIPS

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Hardware-based Seed Selection ← implements 97% 8e

Hardware-based seed selection implements seed selection using hardware-related heuristics.

Hardware-based Seed Selection ← uses 3e

Hardware-based seed selection extends the concept of seed selection with hardware-related heuristics.

RTL Fuzzing ← uses 1e

RTL fuzzing relies on seed selection as a critical step in the fuzzing process.

LINKED ENTITIES

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Hardware-based Seed Selection IMPLEMENTS Extracted graph relationship

CITATIONS

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5 citations — click to expand

[1] Seed selection is a critical fuzzing step, and prior CPU RTL fuzzing work did not combine seed selection with hardware features well; one prior CPU-fuzzing approach did not consider seed selection. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing

[2] Instiller proposes hardware-based seed selection and mutation strategies to improve fuzzing performance in RTL fuzzing. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing

[3] Instiller's seed selection considers basic fuzzing heuristics as well as hardware heuristics such as special instructions and registers. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing

[4] Instiller pairs hardware-related seed selection with hardware-related mutation strategies such as insertion or deletion based on input instruction length. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing

[5] Instiller's reported evaluation includes a 29.4% coverage increase, 79.3% shorter inputs than DiFuzzRTL for input instruction distillation, 17.0% more mismatches, and a 6.7% average speed increase from input instruction distillation. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing