HiFuzz: Hierarchical Reinforcement Learning for Semantic-Aware and Adaptive CPU Fuzzing

2026-07-07 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Hardware Architecture · Depth: Expert, quick

Summary

HiFuzz is a novel hierarchical reinforcement learning framework designed to improve CPU fuzzing for modern processor verification. It addresses the challenge of reaching deep architectural states, which traditional mutation-based fuzzing struggles with due to inefficiencies. HiFuzz replaces the conventional mutation process with a structured, two-layer generation approach: a Program Agent handles the global program layout, while a Basic Block Agent precisely fills in instructions. To mitigate reward sparsity, the framework incorporates an adaptive coverage reward mechanism and a semantic-aware basic block encoder, which provides intrinsic feedback. Extensive evaluations conducted on three real-world RISC-V cores demonstrate that HiFuzz significantly surpasses existing state-of-the-art fuzzers in both coverage metrics and its ability to detect bugs.

Key takeaway

For AI Hardware Engineers focused on processor verification, HiFuzz demonstrates a significant shift from mutation-based fuzzing. You should consider integrating hierarchical reinforcement learning frameworks to reach deeper architectural states more efficiently. This approach, leveraging semantic-aware agents and adaptive rewards, can substantially improve coverage and bug detection in your RISC-V core designs, reducing verification time and enhancing reliability.

Key insights

HiFuzz uses hierarchical reinforcement learning with semantic-aware agents to overcome traditional CPU fuzzing limitations, improving coverage and bug detection.

Principles

• Hierarchical RL improves complex state exploration.
• Semantic awareness enhances feedback for fuzzing.
• Adaptive rewards mitigate sparsity in verification.

Method

HiFuzz employs a two-layer generation process: a Program Agent determines global layout, and a Basic Block Agent fills instructions. It integrates adaptive coverage rewards and a semantic-aware basic block encoder.

In practice

• Apply hierarchical RL to verification tasks.
• Use semantic encoders for intrinsic feedback.
• Test on RISC-V cores for robust evaluation.

Topics

• CPU Fuzzing
• Hierarchical Reinforcement Learning
• Processor Verification
• RISC-V Architecture
• Bug Detection
• Semantic-Aware Agents

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Hardware Engineer

Related on AIssential

• Hawk: Harnessing Hardware-Aware Knowledge for High-Performance NPU Kernel Generation — Hawk overcomes NPU kernel generation challenges by integrating hardware-aware knowledge through synthesis, retrieval, and distillation, improving accuracy and speed.
• FuzzingRL: Reinforcement Fuzz-Testing for Revealing VLM Failures — FuzzingRL automatically generates adversarial questions to expose and amplify Vision-Language Model vulnerabilities through iterative refinement.
• SkillFuzz: Fuzzing Skill Composition for Implicit Intents Discovery in Open Skill Marketplaces — SkillFuzz uses fuzzing over LLM agent skill compositions to detect implicit, unintended behaviors before execution.
• Reinforcement Learning for Software Vulnerability Analysis: A Systematic Review with Emphasis on C/C++ Source Code and Static Analysis — RL for C/C++ vulnerability analysis is nascent, with a critical gap in using static source-code structural representations for detection.
• Veritas: A Semantically Grounded Agentic Framework for Memory Corruption Vulnerability Detection in Binaries — Semantic grounding in static and runtime evidence is crucial for reliable binary vulnerability detection.
• Moira: Language-driven Hierarchical Reinforcement Learning for Pair Trading — Language-driven hierarchical reinforcement learning with prompt optimization effectively solves complex financial decision-making problems.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.