Zero-shot learning meets AWS configuration validation through intelligent prompt engineering
Smart LLM system spots serverless setup mistakes without needing training data
SlsDetector uses LLMs to catch AWS serverless configuration errors before they cause problems
https://arxiv.org/abs/2411.00642
🎯 Original Problem:
AWS serverless applications face critical misconfiguration challenges that traditional data-driven detection methods struggle to solve due to complex configuration patterns and incomplete datasets.
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🛠️ Solution in this Paper:
→ SlsDetector leverages LLMs with zero-shot learning to detect misconfigurations in AWS serverless applications
→ It uses a prompt generation component that combines configuration files, task descriptions, and multi-dimensional constraints
→ The system implements Chain of Thought reasoning to systematically evaluate resource types, configuration entries, values, and dependencies
→ SlsDetector generates structured outputs with detailed explanations for each detected misconfiguration issue
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🔍 Key Insights:
→ Serverless configurations involve over 800 cloud resource types and complex dependency relationships
→ Zero-shot learning eliminates the need for labeled training data while maintaining high accuracy
→ Multi-dimensional constraints significantly improve detection accuracy compared to traditional methods
→ The system works effectively across multiple LLM models including ChatGPT-4, Llama 3.1, and Gemini 1.5 Pro
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📊 Results:
→ Achieved 72.88% precision, 88.18% recall, and 79.75% F1-score using ChatGPT-4
→ Outperformed existing data-driven approaches by 53.82%, 17.40%, and 49.72% in precision, recall, and F1-score respectively
→ Demonstrated consistent high performance across different LLM models
