LLM Runtime Protection via Automatic SDK Code Injection

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Solution Overview

Problem

Existing methods for integrating software development kits (SDKs) into cloud-to-cloud workflows are inefficient, time-consuming, error-prone, and costly due to manual integration efforts, static analysis limitations, configuration complexity, and performance overhead, leading to difficulties in ensuring proper SDK integration and maintaining code security.

Innovation Solution

A system that uses machine learning models to analyze call flows in code, automatically identify optimal points for SDK integration, and inject SDKs into code samples, dynamically adapting to changes and ensuring proper integration without manual modifications.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual SDK integration methods are used, then developers can integrate SDKs into code, but the process becomes time-consuming and error-prone

Engineering Contradiction:

ImproveSDK integration accuracyVSAvoidSDK integration time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables self-service by allowing the code analysis engine and machine learning models to automatically detect call flows, identify integration points, and inject SDK code without manual developer intervention. The system serves itself by learning from code patterns and autonomously performing integration tasks that were previously requiring manual effort.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical integration processes with automated computational systems. The code analysis engine uses static analysis and machine learning to automatically detect call flows and determine optimal integration points, substituting the mechanical manual process of code review and integration with intelligent automated analysis and injection.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If static analysis methods are used for SDK integration, then code can be analyzed, but the method lacks adaptability to dynamic code changes

Engineering Contradiction:

ImproveAdaptability to code changesVSAvoidCall flow detection accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system merges static analysis with machine learning-based dynamic detection. The code analysis engine combines traditional static analysis capabilities with ML models that learn from code execution patterns, creating a hybrid approach that maintains precision while gaining adaptability to dynamic code changes.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system introduces dynamics by using machine learning models that can adapt to changing code patterns. The ML-based call flow detector learns from execution data and adapts to dynamic code changes, making the system flexible and responsive to modifications while maintaining accurate detection through continuous learning.

Inventive Principle:

Principle #15Dynamics

3Reliability

If complex configuration processes are used for SDK integration, then proper integration can be achieved, but the complexity increases and maintenance becomes difficult

Engineering Contradiction:

ImproveSDK integration propernessVSAvoidIntegration configuration complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system eliminates complex configuration by enabling self-service integration. The code analysis engine automatically analyzes the codebase, detects call flows, identifies integration points, and injects SDK code without requiring developers to manually configure integration parameters or understand complex SDK setup procedures.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent introduces an intermediary code analysis engine that mediates between the SDK and the target codebase. This intermediary automatically handles the complex integration process by analyzing code, determining optimal injection points, and performing integration, thereby shielding developers from configuration complexity while ensuring proper integration.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Productivity

If traditional SDK integration methods are used, then code functionality can be added, but performance overhead increases

Engineering Contradiction:

ImproveCode functionality deliveryVSAvoidRuntime performance overhead

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary action by automatically injecting optimized SDK code at the most efficient locations during the development phase. The code analysis engine identifies optimal integration points that minimize runtime overhead, and the ML models learn from performance data to continuously improve integration efficiency, ensuring functionality is delivered with minimal performance penalty.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20260003954A1Auto generate security SDK code injections into custom application for run time LLM protection

Publication Date: 2026.01.01 PALO ALTO NETWORKS INC

AI summary

The present application discloses a method, system, and computer system for securing code. The method includes (i) obtaining a code sample, (ii) detecting a call flow associated with the code sample, (iii) determining whether to inject a software development kit (SDK) to the code sample based at least in part on the call flow, and (iv) in response to determining to inject the SDK to the code sample: (a) automatically injecting the SDK to the code sample to obtain an SDK-injected code sample, and (b) providing the SDK-injected code sample.