Hardware-Based Mutation
TechniqueHardware-Based Mutation is a technique reported in the Instiller RTL fuzzing work. The evidence describes it as part of Instiller’s hardware-based seed selection and mutation strategies, using hardware-related heuristics and mutation operations to improve fuzzing performance for RTL fuzzing.
First seen 5/27/2026
Last seen 6/3/2026
Evidence 3 chunks
Wiki v1
WIKI
Hardware-Based Mutation
Hardware-Based Mutation is a technique described in the Instiller RTL fuzzing work. In the cited paper, it appears as part of “hardware-based seed selection and mutation strategies” intended to improve fuzzing performance in RTL fuzzing by using hardware-related heuristics and mutation operations.[1]
Context
NEIGHBORHOOD
No graph connections found for this entity yet. It may appear in future ingestion runs.
explore full graph →RELATIONSHIPS
3 connectionsInstiller uses hardware-based mutation strategies to improve fuzzing performance.
Instiller implements hardware-based mutation to improve fuzzing performance.
Instiller introduces hardware-based mutation as a novel contribution.
LINKED ENTITIES
1 linksCITATIONS
4 sources4 citations — click to collapse
[1] Hardware-Based Mutation is described as part of Instiller’s hardware-based seed selection and mutation strategies for RTL fuzzing, using hardware-related heuristics and mutation operations to improve fuzzing performance. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing
[2] Instiller is an RTL fuzzer for hardware/CPU bug detection, and the paper identifies growing RTL input-instruction length and ineffective longer inputs as problems in prior CPU fuzzing work. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing
[3] The Instiller paper lists hardware-based seed selection and mutation strategies as one of its contributions, alongside input-instruction distillation and realistic interruption/exception handling. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing
[4] Instiller reports 29.4% more coverage than DiFuzzRTL, 17.0% more detected mismatches, 79.3% shorter input instructions with VACO, and a 6.7% average execution-speed increase from distillation. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing