LLM Security Playground: An OWASP-Aligned MLSecOps Platform for Secure Deployment

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LLM Security Playground

An OWASP-Aligned MLSecOps Platform for Secure LLM Deployment

Track: AI Security / GenAI Governance | Hackathon Project – IIT Selection

Why We Built This Project

As LLMs integrate into enterprise environments, they expose new attack surfaces traditional tools miss. Prompt injection and data leakage are real risks, not theory.

We saw a gap: most teams deploy without structured security testing. This platform treats LLM security as an engineering discipline, not a manual checklist.

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The Core Security Problem

Attackers can override behavior via Prompt Injection.

Sensitive data (keys, PII) can be leaked through outputs.

System prompts and internal logic are easily extracted.

Lack of automated, enforceable security mechanisms.

Why Existing Systems Fail

Current deployments rely on static filters or manual reviews. These fail because:

• They do not simulate real attack behavior.
• They lack CI/CD integration.
• No formatted runtime protection.
• No standardized security evidence.

Result: Vulnerabilities are found by users, not engineers.

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What Is the LLM Security Playground?

SIMULATE real LLM attacks in a controlled environment.

COMPARE vulnerable vs. protected models side-by-side.

ENFORCE security policies using an intelligent sidecar proxy.

COLLECT standardized evidence (SARIF) for compliance.

Isometric 3D diagram of a software architecture: Client connects to a glowing blue Security Proxy Sidecar, which then connects to a backend AI Model. Clean lines on dark background.

System Architecture Overview

Designed around a modular architecture where security is decoupled from the LLM.

The Security Sidecar acts as an intelligent proxy, inspecting inputs and outputs without modifying model internals. This ensures vendor independence and scalability.

Architecture Components

Frontend (React + TS): Interactive UI for real-time prompt testing and security observation.

Security Sidecar (Node.js): Intercepts requests, enforces rules, inspects payloads.

Backend Targets: Mock & Azure Models to demonstrate vulnerable vs. protected states.

Testing Harness (Python): Automated attack simulation and SARIF reporting.

Security Sidecar Design

The sidecar pattern allows security logic to exist independently of the LLM.

Features:
• No changes to model code required
• Works with local, cloud, or hosted models
• Centralized enforcement point
• Easy horizontal scaling

This mirrors enterprise-grade service mesh logic.

Futuristic technical blueprint visualization of a 'Sidecar' proxy module attaching to a main application container, glowing schematics, blue and cyan colors.

Automated Security Testing

We moved beyond manual prompting to automated engineering.

Our Python Testing Harness simulates attack campaigns against LLM endpoints, making security checks repeatable and comparable.

Output: Standardized SARIF reports for CI/CD integration.

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Attack Scenarios Demonstrated

⚠ Prompt Injection: Attempts to override strict system instructions.

⚠ Data Leakage: Requests engineered to reveal API keys or config.

⚠ Prompt Extraction: Techniques to compel the model to reveal its own rules.

⚠ Malicious Compliance: Forcing the model to generate harmful content.

Demo Comparison

VULNERABLE MODEL:
Responds to malicious prompts, leaks secrets, and exposes logic.

PROTECTED MODEL (w/ Sidecar):
Identifies intent, blocks or sanitizes inputs, and logs the security event. Model integrity is preserved.

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Real-Time VisibilitySecurity is not a black box. Every decision made by the sidecar is logged instanly.

• Blocked attacks
• Sanitized responses
• Policy violations

This creates a feedback loop for debugging and auditing.

Evidence & Reporting

Security That Can Be Proven

Generates SARIF reports compatible with GitHub Security.

Log evidence aligned with OWASP LLM Top-10.

Measurable audit trails for enterprise compliance.

Technology Stack

• Frontend: React, TypeScript, Tailwind CSS
• Backend/Sidecar: Node.js, Express
• Testing: Python, PyTest
• Deployment: Docker, Azure Container Instances

"Security evidence is as important as security controls."

Key Learning / Project Takeaway

Why Select This Project?

✔️ Solves a critical, emerging AI security problem.

✔️ Demonstrates code-complete implementation, not just theory.

✔️ Aligns with OWASP and MLSecOps global standards.

✔️ Presents legitimate engineering and architectural thinking.

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Secure AI Deployment Starts Here

The LLM Security Playground

LLM Security Playground

An OWASP-Aligned MLSecOps Platform for Secure LLM Deployment

Track: AI Security / GenAI Governance | Hackathon Project – IIT Selection

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