BugOff

AI-Powered Code Vulnerability Analysis in an Integrated DevSecOps Pipeline

Overview

BugOff is a comprehensive platform that uses artificial intelligence and Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) to detect vulnerabilities in code across multiple programming languages. The platform leverages a polyglot microservices architecture and integrates with modern DevSecOps practices to provide accurate, context-aware security analysis for software development teams.

Key Features

AI-powered code vulnerability detection with context-aware insights
Polyglot microservices architecture with five specialized services
Retrieval Augmented Generation (RAG) for improved detection accuracy
Seamless integration with DevSecOps pipelines and workflows
Comprehensive security analysis across multiple programming languages
Detailed vulnerability reporting with actionable recommendations
Real-time security assessment through Kafka message streaming
Robust monitoring and observability with Prometheus and Grafana

Development Process

The project was developed using an agile methodology, with infrastructure defined as code using Terraform. We designed a polyglot microservices architecture, where each service was built using the most appropriate technology for its function. The complete CI/CD pipeline integrated multiple security scans at every stage, with deployment to AWS EKS. The core of the platform leverages LLMs enhanced with RAG technology to provide intelligent and context-aware vulnerability detection.

Project Details

Date: 2025
Category: DevSecOps & Cybersecurity
Team: Yassine El Jakani Amine Elhasbi Saaida Hnais Achraf Lkhal

Technologies Used

TypeScript

React

Next.js

Spring Boot

Flask

Docker

Kubernetes

AWS

Terraform

Kafka

MongoDB

SQL

RAG

Challenges and Solutions

Implementing Effective Microservices Communication

Building seamless communication between services developed in different programming languages posed significant challenges.

Solution: We implemented Apache Kafka as a message broker, allowing asynchronous communication between services regardless of their implementation language, while ensuring reliability and fault tolerance.

Enhancing AI Detection Accuracy

Initial LLM implementations had limitations in accurately identifying complex vulnerabilities across different programming languages.

Solution: By integrating Retrieval Augmented Generation (RAG) with specialized knowledge bases for various programming languages, we significantly improved detection accuracy and reduced false positives.

Securing the Infrastructure

Ensuring comprehensive security across a distributed cloud infrastructure presented complex challenges.

Solution: We implemented a multi-layered security approach with private subnets, bastion hosts, and network segmentation, along with continuous security scanning at each stage of the CI/CD pipeline.

Results and Impact

The BugOff platform successfully demonstrates the integration of AI-powered security analysis within a modern cloud-native architecture. The implementation of RAG technology enhanced the system's ability to provide context-aware vulnerability detection with higher accuracy than traditional tools. The platform's polyglot microservices architecture proved both scalable and maintainable, while the comprehensive DevSecOps integration ensured security at every stage of development and deployment.