Embedded Software Fuzz Testing Using Network Input Seeds
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Solution Overview
Problem
Existing fuzz testing tools for embedded software applications, such as AFL/AFL++, face inefficiencies in generating high-quality test cases, leading to reduced vulnerability detection efficiency and increased computational costs.
Innovation Solution
Utilize network user input (NUI) keywords as seeds for fuzz testing, prioritizing their mutation to efficiently reach deep states in the software and identify critical vulnerabilities, while employing a lightweight approach to exclude non-NUI keywords and instrument key functions for injection attack detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If general fuzz testing techniques (AFL/AFL++) are used on embedded software applications, then the testing process can be executed, but the test case generation quality is low and vulnerability detection efficiency is reduced
Solution Approach 1:
The patent applies local quality by making the fuzz testing process adaptive to the specific characteristics of embedded software. The system dynamically adjusts testing parameters, mutation strategies, and seed selection based on the observed software behavior, code coverage feedback, and vulnerability patterns. This localized adaptation improves test case quality and detection efficiency without requiring complete redesign of the fuzzing framework.
2Measurement precision
If traditional taint analysis is used to determine keyword propagation, then precise analysis can be achieved, but computational costs increase and the process may not terminate due to complex control flow and loops
Solution Approach 1:
The patent implements partial action by selectively applying taint analysis only to critical paths and high-risk functions within the embedded software. Instead of analyzing the entire codebase, the system identifies and focuses on specific functions, variables, and execution paths that are most likely to contain vulnerabilities. This selective approach maintains analysis precision while significantly reducing computational overhead and avoiding non-termination issues.
3Reliability
If comprehensive fuzz testing is performed on all seeds, then thorough coverage can be achieved, but computational resources are wasted on non-critical paths and false positives increase
Solution Approach 1:
The patent employs feedback mechanisms where the fuzz testing system continuously monitors execution outcomes, code coverage metrics, and vulnerability detection results. Based on this feedback, the system dynamically adjusts the testing strategy by prioritizing seeds that lead to high-value test cases, adjusting mutation rates, and focusing resources on unexplored code paths. This feedback-driven approach improves detection reliability while minimizing resource waste on low-value testing activities.
Data Source
AI summary
An apparatus, an embedded software application, a data carrier, a computer program, and a method for fuzz testing embedded software applications is disclosed. The method comprises identifying, from a plurality of keywords, network user input keywords related to network user inputs. Also, the method comprises providing the identified keywords as seed for fuzz testing an embedded software application and testing the software based on the seed.