LLM-Generated Mitigation Policies With Feedback for API Security
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies lack an automated method for effectively mitigating vulnerabilities in application programming interfaces (APIs) and applications.
Innovation Solution
Utilizing large language models (LLMs) with an automated feedback loop to generate and refine mitigation policies for API gateways, web application firewalls, and intrusion prevention/detection systems, incorporating textual descriptions, exploit examples, and meta-data to create precise and adaptive security configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated vulnerability mitigation methods are implemented, then productivity and security response speed are improved, but reliability and policy accuracy may worsen due to lack of human review
Solution Approach 1:
The system implements an automated feedback loop where the FLAE executes generated policies against vulnerability exploits, captures execution results and updated vulnerability data, and feeds this information back to the LLM. This continuous feedback mechanism allows the LLM to learn from policy effectiveness and refine future policy generation, thereby improving reliability while maintaining automated high-speed response.
Solution Approach 2:
The system performs preliminary actions by pre-processing vulnerability data, exploit code, and meta-data before policy generation. The LLM receives prepared inputs including vulnerability descriptions, exploit examples, and contextual metadata, enabling it to generate accurate policies efficiently without requiring human review of every detail.
2Reliability
If complex feedback loops are added to improve policy accuracy, then reliability is improved, but device complexity increases
Solution Approach 1:
The Feedback Loop Automation Engine (FLAE) is designed as a multi-functional component that handles policy execution, vulnerability data collection, feedback generation, and LLM communication within a single integrated system. This universal approach consolidates complexity into one coordinated module rather than requiring separate systems for each function.
3Reliability
If human review is required for policy generation, then reliability is improved, but productivity and response time worsen
Solution Approach 1:
The system implements self-service automation where the LLM autonomously generates vulnerability mitigation policies without requiring human intervention in the core policy creation process. The automated feedback loop further enables the system to self-refine and improve its own policy generation accuracy over time, eliminating the need for human review while maintaining high reliability.
Data Source
AI summary
The method for generating mitigation policies may include receiving, by a large language model (LLM), a plurality of inputs including textual description of vulnerabilities, code with one or more vulnerability exploits, and meta-data; generating, by the LLM, API and application vulnerability mitigation policies based on the received inputs; generating, by a feedback loop automation engine (FLAE), feedback data based on the API and application vulnerability mitigation policies and the code with one or more vulnerability exploits; and updating, by the LLM, the API and application vulnerability mitigation policies based on the feedback data generated by the FLAE.