Directed Fuzzing for Consistent ML Vulnerability Detection
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Solution Overview
Problem
Existing static code analyzers and machine-learning models struggle to accurately identify vulnerabilities in source code due to their reliance on fixed rule sets and inconsistent identification of vulnerable code structures, leading to inefficiencies in resource usage and accuracy.
Innovation Solution
The implementation of directed fuzzing to provide targeted inputs to source code, combining machine-learning models with explanation methods to refine vulnerability detection by empirically verifying identified locations and using feedback to improve model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine-learning models are used to detect vulnerabilities, then detection flexibility improves, but measurement precision deteriorates due to inconsistent identification of vulnerable code structures
Solution Approach 1:
The system implements feedback by using directed fuzzing to empirically verify vulnerability predictions made by machine-learning models. The fuzzing results feed back into the system to confirm or correct model predictions, ensuring that the flexibility of ML-based detection does not compromise identification consistency. This closed-loop feedback mechanism resolves the contradiction by validating model outputs against actual runtime behavior.
Solution Approach 2:
Directed fuzzing serves as an intermediary between machine-learning models and final vulnerability confirmation. Instead of relying solely on ML predictions (which lack consistency) or traditional static analysis (which lacks flexibility), the system introduces directed fuzzing as a mediating step that provides empirical verification, thereby reconciling the trade-off between detection flexibility and measurement precision.
2Measurement precision
If directed fuzzing is implemented to verify vulnerability predictions, then measurement precision improves, but use of energy worsens due to additional computational requirements
Solution Approach 1:
The system applies partial action by using directed fuzzing selectively only for predictions made by machine-learning models, rather than applying exhaustive fuzzing to all code. This targeted approach verifies vulnerability predictions with high precision while limiting computational resource consumption to only those cases where ML identification is made, thereby resolving the contradiction between measurement precision and energy usage.
3Adaptability or versatility
If multiple explanation methods are used to identify vulnerable code structures, then adaptability improves, but device complexity worsens
Solution Approach 1:
Directed fuzzing acts as an intermediary that simplifies the complex outputs of multiple explanation methods. Instead of manually analyzing and reconciling results from multiple diverse explanation methods (which increases device complexity), the system uses directed fuzzing to empirically verify which identified structures are actually vulnerable, thereby managing the complexity of using multiple explanation methods while maintaining their adaptability benefits.
Data Source
AI summary
Applications may contain vulnerabilities to attack via malicious inputs. Machine-learning models may be trained to detect these vulnerabilities by accepting source code as input and outputting a probability that each of a set of vulnerabilities exists in the source code. Explanation methods may identify one or more locations within the source code that are likely to cause the vulnerability. Directed fuzzing provides a range of inputs to source code. The inputs that cause the source code to fail are detected and the portions of the source code that were vulnerable are identified. The results of the directed fuzzing are used to select between explanations generated by multiple explanation methods, to provide additional training data to a machine-learning model, to provide additional training data to an explanation method, or any suitable combination thereof.