Tokenizing User Content for XSS and SQL Injection Prevention
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Solution Overview
Problem
Current methods using blacklists to filter user-generated content are insufficient in preventing attacks like XSS and SQL injection, as they miss nested attacks, incorrectly remove benign content, and are unable to defend against new or 'zero-day' exploits, being reactive rather than proactive.
Innovation Solution
Implementing a tokenization process that converts user-generated content into a token stream, which is then compared to a whitelist to identify and filter out malicious elements, rather than relying on blacklists, ensuring proactive defense against attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blacklist-based filtering is used to remove malicious content, then some attacks are blocked, but nested attacks are missed and benign content is incorrectly removed
Solution Approach 1:
The patent segments user-generated content into discrete tokens representing different data types (HTML tags, attributes, text, CSS, JavaScript). This tokenization allows the system to analyze and validate each component separately, enabling detection of nested attacks that span multiple tokens while preserving benign content that individual tokens represent correctly.
Solution Approach 2:
Instead of using a blacklist to identify and remove malicious content, the patent inverts the approach by using a whitelist to identify and preserve only valid, safe content types and structures. Any content not matching the whitelist definitions is automatically rejected, eliminating false positives while maintaining security.
2Adaptability or versatility
If blacklist filters are updated frequently to counter new attacks, then defense against new threats improves, but system complexity and maintenance burden increase
Solution Approach 1:
The patent changes the fundamental parameter of content validation from pattern-matching against evolving blacklist entries to structural validation against fixed grammar rules and type definitions. This allows the system to adapt to new attack vectors by adding new token types or validation rules without requiring constant updates to core filtering logic, reducing maintenance complexity while improving adaptability.
3Ease of manufacture
If iterative blacklist replacement is used to filter content, then processing is simple, but nested and concatenated attacks are not detected
Solution Approach 1:
The patent performs preliminary tokenization and classification of content into structured tokens before validation. This preliminary structuring enables the subsequent validation phase to easily detect nested attacks by examining the hierarchy and relationships between tokens, while maintaining overall process simplicity through automated pipeline execution.
Solution Approach 2:
The patent introduces tokens as an intermediary representation between raw user content and the validation logic. These tokens serve as a standardized intermediate form that simplifies processing while enabling comprehensive validation of nested structures, bridging the gap between simple filtering and complex attack detection.
Data Source
AI summary
The present invention relates to systems and methods for the tokenization of user-generated content in order to prevent attacks on the user-generated content. The systems and methods initially pre-process the user-generated content string utilizing a secondary input of target language. Pre-processing may also include initialization of finite state machines, token markers and string buffers (text, HTML tag name, HTML attribute name, HTML attribute value, CSS selector, CSS property name, and CSS property value). The user-generated content string is scanned by rune, and the system sends each rune to a specific buffer based upon signaling by individual finite state machine states. Buffers are then converted to token stream nodes to be inserted into the token stream. The tokens represent a string of characters and are symbolically categorized according to activated finite state machine states.