Automated API Backdoor Detection via Data Flow Monitoring
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Solution Overview
Problem
Existing approaches to detecting backdoor vulnerabilities in software systems are inefficient, particularly when backdoors are well-hidden, and require extensive manual labor, leading to latency and error-prone results.
Innovation Solution
An automated system that analyzes system information related to API operations, constructs and executes call functions, monitors data flows, and generates impact data to determine if the impact is counter to historical functioning, enabling proactive detection and temporary remediation of vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual frameworks are used to detect backdoor vulnerabilities, then detection accuracy can be maintained, but latency increases and manual labor requirements increase
Solution Approach 1:
The system performs self-service by automatically executing API calls, monitoring data flows, and detecting vulnerabilities without requiring manual intervention. The framework autonomously traverses the API graph, constructs call functions, executes them, and analyzes impacts to detect backdoor vulnerabilities, eliminating the need for manual testing while maintaining detection accuracy.
Solution Approach 2:
The patent replaces manual mechanical inspection processes with automated computational mechanisms. Instead of manual frameworks that require human operators to systematically test APIs, the system uses automated code generation, execution, and analysis algorithms to perform the same detection functions, thereby reducing latency and manual labor while maintaining reliability.
2Reliability
If manual frameworks are used to detect backdoor vulnerabilities, then detection thoroughness can be maintained, but manual labor and expense increase
Solution Approach 1:
The framework performs self-service by automatically traversing the API graph, constructing call functions, executing them, and analyzing data flows to detect vulnerabilities. This autonomous operation eliminates the need for manual labor while maintaining thorough detection coverage through systematic automated exploration of all API interactions.
Solution Approach 2:
The system achieves universality by creating a multi-functional framework that can detect various types of backdoor vulnerabilities through a single unified approach. The framework handles different API types, monitors various data flow patterns, and identifies multiple vulnerability indicators automatically, replacing the need for multiple manual detection procedures.
3Productivity
If automated API execution is performed to detect vulnerabilities, then productivity increases, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the vulnerability detection system into distinct modular components: API graph traversal module, call function construction module, API execution module, data flow monitoring module, and impact analysis module. This segmentation allows each component to perform a specific function independently, improving productivity while managing complexity through modular architecture.
Solution Approach 2:
The framework introduces an intermediary layer in the form of a structured call function construction and execution mechanism. This intermediary layer coordinates between the API graph traversal and the actual API executions, managing the complexity of automated testing by providing a standardized interface that simplifies the overall system architecture while enabling high-speed detection.
Data Source
AI summary
Technology described herein relates to managing backdoor vulnerabilities of a computer system. A system for the managing can comprise a processor, and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising analyzing system information relating to operation of an application programming interface (API); based on a result of the analyzing of the system information, constructing a call function for execution of the API and executing the call function; based on monitoring a data flow of the system with respect to the execution of the API, generating impact data representative of an impact of the execution of the API; and determining whether the impact is counter to historical functioning of the system as represented by historical functioning data.