arXiv:2607.06619v1 Announce Type: cross Abstract: Modern processor verification struggles to reach deep architectural states due to the inefficiencies of traditional mutation-based fuzzing. We propose HiFuzz, a novel hierarchical reinforcement learning framework that replaces mutation with a structured, two-layer generation process: a Program Agent for global layout and a Basic Block Agent for precise instruction filling. To overcome reward sparsity, HiFuzz integrates an adaptive coverage reward mechanism and a semantic-aware basic block encoder providing intrinsic feedback. Extensive evaluati
The increasing complexity of modern processors and the inefficiencies of traditional verification methods are driving the need for more advanced fuzzing techniques.
Improved processor verification through advanced AI-driven techniques directly impacts the reliability and security of all digital infrastructure, from consumer devices to critical national systems.
The adoption of hierarchical reinforcement learning for CPU fuzzing could significantly enhance the detection of deep architectural vulnerabilities, leading to more robust and secure silicon.
- · Semiconductor manufacturers
- · Cybersecurity industry
- · AI/ML research institutions
- · Hardware verification tool vendors
- · Traditional mutation-based fuzzing approaches
- · Malicious actors targeting processor vulnerabilities
Processor design and verification cycles become more efficient and effective at identifying complex bugs.
Increased hardware security could reduce the attack surface for sophisticated exploits targeting underlying chip architectures.
More reliable silicon could accelerate innovation in AI, autonomous systems, and other compute-intensive fields by building on a more trusted foundation.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG