Directed Fuzzing Feedback for Consistent Code Vulnerability Explanations
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Solution Overview
Problem
Classical static code analyzers rely on fixed rule sets and struggle to accurately identify vulnerabilities in source code, while machine-learning models often provide inconsistent explanations for their predictions, leading to inefficiencies in vulnerability detection.
Innovation Solution
Directed fuzzing is employed to train machine-learning models using a training set of source code, where unlabeled code is tested with varied inputs to identify vulnerabilities, and explanation methods are refined through feedback from directed fuzzing, improving the model's accuracy and providing clear code locations for vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learning models are used to detect vulnerabilities, then detection accuracy is improved, but explanation consistency deteriorates
Solution Approach 1:
The patent implements feedback loops where directed fuzzing results are used to retrain and refine machine-learning models. The models generate predictions, explanation methods provide code locations, directed fuzzing validates these locations, and the results feed back to improve model accuracy and explanation consistency iteratively
Solution Approach 2:
The patent introduces explanation methods as intermediaries between machine-learning models and developers. These methods translate model predictions into consistent, interpretable code locations, bridging the gap between accurate but opaque model outputs and developer-needs for clear explanations
2Use of energy by moving object
If classical static code analyzers are used, then computational resources are conserved, but vulnerability detection precision deteriorates
Solution Approach 1:
The patent segments the vulnerability detection process into multiple components: initial screening with classical analyzers, followed by targeted machine-learning model application only to suspicious regions identified by the analyzers. This segmentation reduces overall computational load while maintaining high detection precision where it matters most
Solution Approach 2:
The patent applies different detection strategies to different code regions based on their vulnerability characteristics. Machine-learning models and directed fuzzing are applied selectively to regions identified as high-risk by initial analysis, rather than uniformly across all code, optimizing resource allocation to where precision is most needed
3Measurement precision
If directed fuzzing is applied to all identified vulnerabilities, then detection precision is improved, but computational resources increase
Solution Approach 1:
The patent applies directed fuzzing selectively to a subset of vulnerabilities identified by machine-learning models rather than all potential vulnerabilities. By focusing computational resources on the most promising candidates based on model confidence scores and risk prioritization, it achieves high detection precision without the exponential resource cost of exhaustive fuzzing
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.