Auto_Sec [BRIGHT DATA] [LLAMA INDEX] [REDIS][HORIZON3]

VIDEO LINK

https://www.loom.com/share/a14f65cd9c0745b1b3d848a1f0c0de7e

Inspiration

AI has lowered the barrier to entry for coding; people can now generate functional applications without deep software engineering or security expertise. While this democratization is powerful, it also means insecure code is entering production at unprecedented speed, exposing users to risks they might not even realize. That’s why we believe there’s an urgent need to automatically detect and eradicate these vulnerabilities before they cause real harm.

Security vulnerabilities are discovered constantly, but the gap between detection and remediation is where most organizations and developers fail. We've all seen those security reports with hundreds of findings that sit in tickets for months. We wanted to build an AI-powered agent system that finds and fixes vulnerabilities automatically.

The inspiration came from wanting to recreate how actual security teams operate. What if we could create autonomous agents that work like a security team, one agent scouts for threat intelligence, another runs pentests, and a third creates fixes based on proven patterns?

What it does

Auto_Security is an autonomous security remediation pipeline that:

Searches for CVEs Online, it checks reputable sources like NVD
Scans GitHub repositories for vulnerabilities using pattern recognition and AI context
Creates GitHub issues automatically for each finding with detailed evidence
Generates and commits fixes using vector similarity search against a knowledge base of proven patches
Opens pull requests with intelligent remediation code ready for review

How we built it

We architected a multi-agent system with specialized roles:

Scout Agent: Builds a threat intelligence knowledge base using Redis Vector Store and LlamaIndex, scraping CVE databases and patch repositories to create embeddings of vulnerability patterns and proven fixes.

Vulnerability Scanner: Detects security issues using regex patterns for common vulnerabilities (SQL injection, XSS, command injection, hardcoded secrets) enhanced with AI context from the Scout Agent.

Triage Agent: Uses vector similarity search to find the most relevant patches from the knowledge base and generates contextual fixes using proven remediation patterns.

GitHub Integration: Automates the entire workflow - cloning repos, creating branches, committing fixes, opening PRs, and managing issues.

The core innovation is using Redis Vector similarity search to match current vulnerabilities with historically successful patches, making fixes more accurate and contextually appropriate.

Challenges we ran with

Fetching Accurate Vulnerability Reports: Getting relevant vulnerability reports to the technologies that are used in the repo was non-trivial. We don't want to return irrelevant vulnerabilities that may confuse the AI agents.

Vector Similarity Accuracy: Getting the embedding models to correctly match vulnerability patterns with relevant patches required extensive experimentation with metadata filtering and similarity thresholds.

Code Context Understanding: Teaching the AI agents to understand code structure well enough to generate syntactically correct and secure fixes without breaking existing functionality.

OpenAI API Rate Limits: Managing authentication, rate limiting, and handling large repositories while maintaining the automated workflow.

Pattern Recognition Balance: Tuning vulnerability detection to minimize false positives while catching real security issues across different programming languages and frameworks.

What's next

We're planning to integrate with NodeZero's penetration testing API for more sophisticated vulnerability discovery, boost the vulnerability scanning accuracy, check more sources, and do multi-pass fixes.

Built with

Languages: Python, JavaScript Frameworks: Flask, LlamaIndex, Redis, Bright Data Vector Database: Redis with Vector Search capabilities
AI/ML: OpenAI GPT-4 and Embeddings API GitHub Integration: PyGithub, GitHub REST API Web Scraping: Bright Data MCP Infrastructure: Docker, Redis Stack Security Tools: Custom vulnerability scanners with regex pattern matching