Content Security Policy Reports for Script Attack Classification
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Solution Overview
Problem
Conventional content security policy (CSP) implementations fail to accurately detect and mitigate scripting attacks, such as JavaScript attacks, due to their inability to distinguish between policy violations and malicious code, leaving web applications vulnerable to XSS and other security threats.
Innovation Solution
A system is configured to analyze CSP violations using machine learning models to identify and classify scripting attacks by extracting and correlating features from violation reports, generating scores, and taking automatic mitigation actions to block malicious code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional CSP implementations are used to block all policy violations, then security coverage is improved, but false positive rate increases and legitimate code is blocked
Solution Approach 1:
The patent segments CSP violations into different categories (benign vs. malicious) by analyzing multiple features of each violation report. Instead of treating all violations uniformly, the system divides them into segments based on their characteristics, allowing differentiated responses that maintain security coverage while reducing false positives.
Solution Approach 2:
The system changes the parameters used for violation evaluation by considering multiple features (violation type, source URL, referrer URL, user agent, etc.) rather than relying on a single parameter. This multi-parameter approach enables more accurate classification of violations, improving detection precision while maintaining comprehensive security coverage.
2Speed
If CSP violations are blocked without analysis, then response speed is improved, but detection precision deteriorates
Solution Approach 1:
The system performs preliminary analysis of CSP violation reports by extracting and evaluating multiple features before taking blocking action. This preliminary classification step prepares the data in advance, enabling faster and more accurate decision-making about which violations to block, thus improving both response speed and detection precision.
Solution Approach 2:
The patent introduces an intermediary classification layer between CSP violation detection and blocking actions. This intermediary system analyzes violation features and determines whether to block, allowing the system to maintain fast response times while achieving high detection precision through intelligent mediation.
3Measurement precision
If machine learning models are used to classify violations, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system employs self-service mechanisms where the classification model automatically evaluates violation reports based on extracted features without requiring manual intervention. The model serves itself by making real-time classification decisions, improving detection accuracy while managing system complexity through automation rather than human oversight.
4Reliability
If all CSP violations are treated as potential attacks, then security coverage is improved, but false positive rate increases
Solution Approach 1:
The patent segments violations into distinct categories (benign third-party code vs. malicious scripts) by analyzing multiple features. This segmentation allows the system to maintain comprehensive security coverage while identifying and excluding benign violations, thereby reducing the false positive rate generated by treating all violations uniformly.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for Scripting attack detection and mitigation. A method generally includes receiving a first report indicating a first violation for a first security policy applied to a first web application and one or more second reports indicating one or more second violations for one or more second security policies applied to second web application(s); identifying a first plurality of features associated with the first violation and second pluralities of features associated with the one or more second violations; classifying the first violation and the one or more second violations as a first scripting attack based on the first plurality of features and the second pluralities of features; and based on classifying the first violation and the one or more second violations as the scripting attack, taking action to mitigate the first scripting attack on the first web application and the second web application(s).