language model-based fuzzer

Technique

A language model-based fuzzer is a fuzzing technique that uses a language model to intelligently generate structured test inputs, such as assembly instructions, to discover vulnerabilities in complex systems. In the hardware security domain, it has been applied to generate RISC-V assembly instructions whose effectiveness is refined through real-time hardware feedback.

First seen 8/3/2026

Last seen 8/3/2026

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Language Model-based Fuzzer

Overview

A language model-based fuzzer is a fuzzing technique that leverages a language model to generate syntactically and semantically structured test inputs—rather than purely random mutations—to expose bugs and vulnerabilities in a target system. By learning the structure of valid inputs, the language model produces test cases that are more likely to exercise meaningful behavior, enabling deeper exploration of the target's state space than purely random approaches.

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RELATIONSHIPS

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GenHuzz ← uses 100% 1e

GenHuzz utilizes a language model-based fuzzer to generate RISC-V assembly instructions.

RISC-V assembly instruction generation uses → 100% 1e

The language model-based fuzzer intelligently generates RISC-V assembly instructions.

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RISC-V assembly instruction generation USES Extracted graph relationship

CITATIONS

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3 citations — click to collapse

[1] A language model-based fuzzer can intelligently generate RISC-V assembly instructions for hardware vulnerability detection. GenHuzz: An Efficient Generative Hardware Fuzzer

[2] GenHuzz combines the language model-based fuzzer with a Hardware-Guided Reinforcement Learning framework that incorporates real-time feedback from the hardware. GenHuzz: An Efficient Generative Hardware Fuzzer

[3] The language model-based fuzzer used by GenHuzz is capable of understanding and exploiting complex interdependences between instructions, enabling discovery of deeper bugs and vulnerabilities. GenHuzz: An Efficient Generative Hardware Fuzzer