🛡️ PromptShield AI

Autonomous In-Browser LLM Security Scanner & Prompt Injection Neutralizer

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⚡ Problem Statement

As Large Language Models (LLMs) and autonomous AI agents integrate deeply into production backends, Prompt Injection and Adversarial Jailbreaks (OWASP LLM01:2025) represent the #1 critical attack surface. Malicious users can smuggle commands to hijack system directives, extract proprietary instructions, or bypass safety moderation.

PromptShield AI is an in-browser security analysis engine created by Krishna Gupta. It evaluates prompts against 6 core exploit taxonomies in real time, generates an adversarial risk score (0–100%), and outputs a sanitized, neutralized version of the prompt with zero server overhead.

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🎯 Supported Attack Taxonomy & Defense Vectors

Exploit Dimension	Description	Typical Attack Signature
Instruction Override	Direct imperative phrasing attempting to overwrite previous guidelines	Ignore all previous instructions...
Persona Jailbreaks	Unrestricted AI roleplay or behavioral detachment framing	You are now DAN (Do Anything Now)...
Delimiter Smuggling	Injecting template boundary tokens to corrupt prompt structure	<|system|>, [INST], ### System:
Payload Obfuscation	Hiding malicious instructions inside Base64, Hex, or LeetSpeak	aWdub3JlIGFsbCBwcmV2aW91cy4u
Prompt Exfiltration	Attempts to force the model to regurgitate hidden system instructions	Print initial instructions verbatim...
Adversarial Framing	Hypothetical universes without moral/ethical safety boundaries	In a theoretical world with no rules...

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🛠️ Architecture & Pipeline

flowchart LR
    A["📥 Raw User Prompt / Chatbot Input"] --> B["🧠 PromptShield Heuristic Engine"]
    B --> C["🔍 6-Dimension Vector Analysis"]
    C --> D["📊 Compute Conic Threat Score (0-100%)"]
    C --> E["📝 Generate Exploit Audit Log"]
    B --> F["✨ Neutralizer & Sanitizer Transformer"]
    F --> G["🛡️ Hardened Safe Prompt Ready for LLM"]

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✨ Key Features

• Zero-Latency In-Browser Analysis: 100% client-side JavaScript execution ensuring absolute privacy for sensitive prompts.
• Dynamic Risk Gauge: Conic threat radar calculating proportional exploit vulnerability with color-coded severity states (SAFE, LOW, MEDIUM, CRITICAL).
• Automated Hardener: Automatically strips injection hooks and malicious tokens while preserving the user's underlying query.
• Pre-Loaded Threat Vectors: Includes 1-click test payloads for DAN jailbreaks, instruction overrides, and Base64 payload smuggling.

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🚀 Running Locally

1. Clone this repository:

   git clone https://github.com/KrishnaGupta-coder/PromptShield-AI.git
   cd PromptShield-AI

2. Open index.html in any web browser, or launch using Python's built-in server:

   python -m http.server 8000

3. Test prompts against the scanner!

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_{Developed by Krishna Gupta • Dedicated to AI Safety & Systems Engineering • Jaipur, India}