Custom AI Co-Pilot Prompt Refinement for Security Pen-Testing
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Solution Overview
Problem
Large language models (LLMs) face challenges in software pen-testing due to inconsistent and inaccurate vulnerability detection, high computational costs for retraining, and the risk of divulging private information, making them unreliable for consistent and accurate software vulnerability analysis.
Innovation Solution
A method involving a trained AI model that identifies vulnerabilities in source code, detects false positives, generates augmented prompts, and stores them in a database to improve LLM performance through iterative prompt refinement, using a combination of LLMs and non-LLM tools for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-trained LLMs are used for vulnerability detection, then the model can analyze code, but the detection accuracy is inconsistent and unreliable
Solution Approach 1:
The system implements feedback by using detected false positives to generate augmented prompts that are fed back into the LLM. This iterative process allows the model to learn from its mistakes and improve detection accuracy over time, directly addressing the inconsistency and unreliability of pre-trained models.
Solution Approach 2:
The system enables the LLM to improve itself by using its own false positive detections to generate learning prompts. The model serves its own training needs by processing its own errors, eliminating the need for external retraining while improving reliability.
2Reliability
If LLMs are retrained for specific security tasks, then detection accuracy may improve, but computational costs increase significantly
Solution Approach 1:
The system uses the LLM's own false positive detections to generate augmented prompts for self-improvement. This self-service approach eliminates the need for computationally expensive external retraining while still improving detection accuracy through iterative prompt refinement.
Solution Approach 2:
Instead of retraining the entire model (which requires massive computational resources), the system changes the prompt parameters dynamically. By adjusting and augmenting the prompts based on false positive patterns, the system achieves improved accuracy without the high computational cost of retraining.
3Reliability
If LLMs are trained on vast amounts of code data, then vulnerability detection capability improves, but the risk of divulging private information increases
Solution Approach 1:
The system trains the LLM on its own false positive data rather than external code datasets. This self-service training approach ensures that only benign false positive examples are used for improvement, eliminating the risk of training on private or sensitive code information while still enhancing detection capability.
Solution Approach 2:
The system extracts only the relevant learning information from false positive detections and uses that for training. By taking out only the necessary patterns from the model's own errors and using those for prompt augmentation, the system avoids exposing private information while still achieving improvement.
4Productivity
If traditional code analysis tools are used, then vulnerability detection can be performed, but false alarms are numerous leading to pen-tester fatigue
Solution Approach 1:
The system uses feedback from false positive detections to generate augmented prompts that refine the LLM's detection patterns. This iterative feedback loop continuously improves the model's ability to distinguish true vulnerabilities from false alarms, reducing false alarm rates while maintaining productive detection output.
Solution Approach 2:
The system replaces traditional mechanical code analysis tools with an LLM-based approach that uses natural language processing and learning. This substitution enables the system to handle code more intelligently, reducing false alarms while maintaining the ability to detect vulnerabilities effectively.
Data Source
AI summary
Systems and method for detecting vulnerabilities in code are provided herein. A pre-trained artificial intelligence (AI) model is engaged, and a plurality of prompts and the source code are provided to the AI model. A plurality of detected vulnerabilities and a plurality of code locations in the source code are identified using the AI model. Each of the plurality of code locations corresponds to at least one of the plurality of detected vulnerabilities. One or more false positive vulnerabilities in the plurality of detected vulnerabilities are identified. A plurality of augmented prompts is generated, based on the one or more false positive vulnerabilities. The plurality of augmented prompts is outputted to a database of prompts for use in future code analyses, without necessarily having to retrain the AI model.