API Attack Script Generation for BOLA Vulnerability Testing
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Solution Overview
Problem
Software applications are vulnerable to Broken Object Level Authorization (BOLA) attacks due to insufficient verification of user authorization, allowing malicious users to access or manipulate data objects they are not authorized to access.
Innovation Solution
A method using a large language model (LLM) to generate artificial user identities and test scripts to identify and exploit potential BOLA vulnerabilities by analyzing the application programming interface (API) specification, including generating registration and login test scripts, and identifying execution paths to detect security vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If automated testing with LLM-generated test scripts is implemented, then detection capability for BOLA vulnerabilities is improved, but device complexity increases
Solution Approach 1:
The patent introduces a large language model (LLM) as an intermediary component that automatically generates test scripts and identifies execution paths. The LLM acts as a mediator between the testing system and the software application under test, handling the complex tasks of script generation and vulnerability detection without requiring manual intervention, thus improving detection capability while managing system complexity through AI automation
Solution Approach 2:
The testing system employs self-service mechanisms where the LLM autonomously generates test scripts, identifies execution paths, and detects vulnerabilities without human intervention. The system feeds API specifications into the LLM, which automatically produces comprehensive test cases and executes them to detect BOLA vulnerabilities, enabling the system to service itself and improve detection capabilities without proportionally increasing operational complexity
2Difficulty of detecting and measuring
If comprehensive testing of all API endpoints and execution paths is performed, then detection thoroughness is improved, but testing time increases
Solution Approach 1:
The patent applies preliminary action by having the LLM analyze API specifications and pre-identify critical execution paths and potential vulnerability points before actual testing begins. The system generates test scripts in advance based on the API documentation, prioritizing paths that are most likely to reveal BOLA vulnerabilities, thus achieving thorough detection while reducing overall testing time through proactive identification of test priorities
Solution Approach 2:
The system dynamically adjusts testing parameters by having the LLM prioritize execution paths based on vulnerability likelihood and risk assessment. Instead of uniformly testing all paths with equal intensity, the system changes testing parameters to focus resources on high-risk paths first, achieving comprehensive detection thoroughness while optimizing testing time through intelligent parameter adjustment and prioritization
Data Source
AI summary
Methods, storage systems and computer program products implement embodiments of the present invention for testing a software application by inputting, to an LLM, a specification of an API of the software application, and prompting the LLM to identify, based on the specification, a set of API consumers that access information provided by the software application. For each given API consumer in the set, the LLM is prompted to identify, based on the specification, one or more execution paths leading to the given consumer from respective API producers, which deliver the information to the software application, and the LLM is prompted to generate, based on the specification, respective test scripts to test the identified execution paths. Finally, the software application is tested with the generated test scripts to discover a security vulnerability in the software application. Additional embodiments can be used to generate artificial user identities for testing the software application.