Software Code Injection Testing With Execution Tokens
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Solution Overview
Problem
Existing run-time security testing methods fail to penetrate deeply into software systems, missing code injection vulnerabilities introduced by new components, leading to undetected security risks.
Innovation Solution
A code injection vulnerability detection system generates and injects specially crafted strings containing exploits that execute to produce execution detection tokens, allowing deep analysis of internal software behaviors to identify vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing run-time security testing methods are used, then the testing process is simple and easy to operate, but the detection depth is limited and code injection vulnerabilities cannot be detected
Solution Approach 1:
The system performs preliminary actions by generating and injecting specially crafted exploit strings containing execution detection tokens before the actual vulnerability assessment. These pre-prepared exploit strings with embedded tokens are injected into the software system to trigger potential code injection vulnerabilities, allowing the system to detect deep internal vulnerabilities that conventional methods miss.
Solution Approach 2:
The system uses execution detection tokens as intermediaries to bridge the gap between exploit injection and vulnerability confirmation. These tokens act as markers that facilitate the detection process by providing clear indicators of successful exploit execution, enabling deep penetration testing while maintaining systematic analysis.
2Reliability
If deep penetration testing is performed to detect internal vulnerabilities, then vulnerability detection capability improves, but computational resources and time consumption increase
Solution Approach 1:
The system creates simplified copies of exploit patterns and uses execution detection tokens as lightweight markers instead of performing complex full vulnerability analysis for each test case. This copying approach allows rapid iteration through multiple exploit scenarios while maintaining detection reliability through the use of distinctive token markers that can be quickly identified in system responses.
Solution Approach 2:
The system changes parameters by using unique execution detection tokens with distinctive characteristics that make them easily identifiable. By varying the token parameters and injection patterns, the system can efficiently test multiple vulnerability scenarios without requiring extensive computational resources for each individual test.
3Adaptability or versatility
If comprehensive vulnerability testing is conducted across all software components, then detection coverage improves, but human effort and operational complexity increase
Solution Approach 1:
The system achieves universality by using a standardized exploit string injection mechanism that can test multiple software components and vulnerability types through a single unified approach. The execution detection tokens serve as universal markers that work across different software environments and vulnerability scenarios, eliminating the need for component-specific testing procedures and simplifying operational complexity.
Data Source
AI summary
Systems and methods are disclosed for detecting code injection vulnerabilities in software systems. In embodiments, an injection string is created to implement an exploit against a software system. The exploit includes an unauthorized executable code that will generate an easily detectable token if executed by the software system. The string is injected into the software system during execution to simulate a code injection attack on the software system. The software system's execution result is obtained and analyzed to determine whether the execution detection token was generated. If so, the software system is proven to be vulnerable to the attack, and the vulnerability may be indicated in an alert or a report. The vulnerability detection technique may be for both software under development as well as production software systems running in the wild to analyze and monitor these systems for code injection vulnerabilities.