Black-Box Device Software Fuzzing With Side-Channel Feedback

Solution Overview

Problem

Existing fuzzing methods, particularly in black-box environments, are ineffective in identifying specific vulnerable areas in device software due to the lack of feedback on code execution, leading to undetected potential errors.

Innovation Solution

A method and apparatus utilizing a fuzzing algorithm that generates fuzzed messages, detects side-channel information, recognizes anomalies using machine learning and statistical algorithms, and adjusts fuzzability weights to enhance vulnerability detection in device software, especially in IoT and automotive devices.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If black-box fuzzing is used without source code access, then the testing process is simpler and more flexible, but the effectiveness in identifying vulnerable areas is limited

Engineering Contradiction:

Improveflexibility in testingVSAvoideffectiveness in identifying vulnerabilities

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent introduces side-channel information as an intermediary mechanism between the fuzzing algorithm and the device software. This intermediary provides indirect feedback about code execution paths and vulnerability indicators without requiring direct source code access, thereby maintaining black-box flexibility while improving vulnerability detection effectiveness

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent implements a feedback mechanism where side-channel information detected during fuzzing is used to adjust the fuzzing process. The feedback loop continuously monitors side-channel data, analyzes it to identify anomalies, and modifies subsequent fuzzing test cases to target areas with higher vulnerability risk, thereby improving effectiveness while maintaining black-box approach

Inventive Principle:

Principle #23Feedback

2Extent of automation

If conventional fuzzing tools are used with source code coverage feedback, then the testing process is automated, but the internal workings remain opaque and not fully adjustable

Engineering Contradiction:

Improveautomation of testingVSAvoidopacity of internal workings

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent uses side-channel information as an intermediary that bridges the gap between automated fuzzing execution and adjustable control. This intermediary provides interpretable feedback signals that can be processed by the fuzzing algorithm to make informed adjustments, reducing opacity while maintaining automation

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent implements a feedback mechanism where side-channel information detected during fuzzing is used to adjust the fuzzing process. The feedback loop continuously monitors side-channel data, analyzes it to identify anomalies, and modifies subsequent fuzzing test cases to target areas with higher vulnerability risk, thereby improving effectiveness while maintaining black-box approach

Inventive Principle:

Principle #23Feedback

3Device complexity

If random fuzzing is performed without targeted feedback, then the testing process is simpler, but certain code paths and vulnerabilities remain untouched

Engineering Contradiction:

Improvesimplicity of testing processVSAvoidcomprehensive code coverage

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The patent implements a feedback mechanism where side-channel information detected during fuzzing is used to adjust the fuzzing process. The feedback loop continuously monitors side-channel data, analyzes it to identify anomalies, and modifies subsequent fuzzing test cases to target areas with higher vulnerability risk, thereby improving effectiveness while maintaining black-box approach

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent introduces dynamic adjustment of the fuzzing process based on detected side-channel anomalies. The fuzzing algorithm transitions from static random testing to dynamic targeted testing, where test cases are adaptively generated and selected based on real-time side-channel feedback, thereby improving code path coverage while maintaining process simplicity

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20250348406A1Method for testing device software of a device by means of a fuzzing algorithm

Publication Date: 2025.11.13 ROBERT BOSCH GMBH

AI summary

A method for testing device software of a device using a fuzzing algorithm. The method includes: initializing the fuzzing algorithm to detect an initial behavior of the device software of the device under test; executing a fuzzing test loop including: generating a fuzzed message from predefined message types; sending the generated, fuzzed message to the device under test to test the device software on the basis of the fuzzed message; detecting side-channel information during testing of the device software on the basis of the fuzzed message; recognizing anomalies in the side-channel information using at least one machine learning model and/or statistical algorithm; and if an anomaly is recognized, adjusting a parameter including adjusting fuzzability weights, of the initialized fuzzing algorithm and performing a next loop iteration of the fuzzing test loop; and if no anomaly is recognized, performing the next loop iteration of the fuzzing test loop.