Neural Transformer Vulnerability Screening for False Positive Reduction
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Solution Overview
Problem
Existing software vulnerability detection tools suffer from high false positive rates, making it difficult and resource-intensive for developers to identify true vulnerabilities in large codebases.
Innovation Solution
Utilizing neural encoder and decoder transformer models to analyze source code context and predict whether identified vulnerabilities are true or false positives, by completing partially-formed code prompts to indicate legitimacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static code vulnerability analyzers are used to detect vulnerabilities, then vulnerability detection capability is improved, but false positive rate increases
Solution Approach 1:
A neural transformer model is introduced as an intermediary system between the static code vulnerability analyzer and the developer. The model takes vulnerability detections as input and predicts whether they are true positives or false positives, thereby mediating the information flow and filtering out false alarms while preserving true vulnerabilities.
Solution Approach 2:
The patent replaces traditional mechanical/static analysis mechanisms with a neural transformer model that uses deep learning to analyze code context. This substitution allows the system to learn patterns from data and make intelligent predictions about vulnerability legitimacy, overcoming the limitations of rule-based static analysis.
2Measurement precision
If developers manually verify each vulnerability warning, then false positive rate decreases, but time consumption increases
Solution Approach 1:
The neural transformer model provides self-service by automatically analyzing vulnerability detections and predicting their legitimacy without requiring developer intervention. The model processes code context independently and outputs predictions that developers can trust, eliminating the need for manual verification of each warning.
Solution Approach 2:
The system performs preliminary action by having the neural transformer model pre-analyze and classify vulnerability detections before developers need to review them. This advance processing filters out false positives in advance, so developers only need to investigate true vulnerabilities, significantly reducing their time commitment.
3Reliability
If vulnerability detection is performed on large codebases, then detection coverage is improved, but resource requirements increase
Solution Approach 1:
The neural transformer model applies local quality by analyzing only the specific code context surrounding each vulnerability detection rather than re-analyzing the entire codebase. The model focuses its computational resources on the local region where the vulnerability was detected, making the process scalable to large codebases without proportionally increasing resource requirements.
Data Source
AI summary
A false positive vulnerability system detects whether a software vulnerability identified by a static code vulnerability analyzer is a true vulnerability or a false positive. The system utilizes deep learning models to predict whether an identified vulnerability is accurate given the source code context of the identified vulnerability. A neural encoder transformer model is trained to classify a false positive given the method body including the identified vulnerability. A neural decoder transformer model is trained to predict a candidate line-of-code to complete a prompt inserted into the context of the identified vulnerability. The candidate line-of-code that successfully completes the prompt is used as a signal to identify that the identified vulnerability is a false positive.