PromptShield

Universal AI Security Framework - Protect LLM applications from prompt injection and adversarial attacks

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What is PromptShield?

PromptShield is a lightweight security framework that protects AI applications from:

• 🚫 Prompt injection attacks
• 🔓 Jailbreak attempts
• 📤 System prompt extraction
• 🔍 PII leakage
• 🎭 Dozens of attack variants

Key Features:

• ⚡ Fast: Pattern matching in ~0.1ms (semantic mode: ~20-30ms)
• 🔌 Framework-agnostic: Works with any LLM (OpenAI, Anthropic, local models)
• 🎯 Simple: 3 lines of code to integrate
• 🛡️ Comprehensive: Multiple attack categories + semantic generalization

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Installation

# Install from source (PyPI package coming soon)
git clone https://github.com/Neural-alchemy/promptshield
cd promptshield
pip install -e .

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Quick Start

from promptshield import Shield

# Initialize shield
shield = Shield(level=5)  # Production security

# Protect your LLM
def safe_llm(user_input: str):
    # 1. Validate input
    result = shield.protect_input(
        user_input=user_input,
        system_context="You are a helpful AI"
    )
    
    if result["blocked"]:
        return "⚠️ Security issue detected"
    
    # 2. Safe LLM call
    response = your_llm(result["secured_context"])
    
    # 3. Sanitize output
    output = shield.protect_output(response, result["metadata"])
    
    return output["safe_response"]

That's it! Your AI is now protected.

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Examples

OpenAI

from openai import OpenAI
from promptshield import Shield

client = OpenAI()
shield = Shield(level=5)

def secure_chat(prompt: str):
    check = shield.protect_input(prompt, "GPT Assistant")
    if check["blocked"]:
        return "Blocked"
    
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": check["secured_context"]}]
    )
    
    output = shield.protect_output(
        response.choices[0].message.content,
        check["metadata"]
    )
    return output["safe_response"]

LangChain

from langchain.llms import OpenAI
from promptshield import Shield

llm = OpenAI()
shield = Shield(level=5)

def secure_chain(query: str):
    check = shield.protect_input(query, "Assistant")
    if check["blocked"]:
        return "Blocked"
    
    result = llm(check["secured_context"])
    output = shield.protect_output(result, check["metadata"])
    return output["safe_response"]

Anthropic Claude

import anthropic
from promptshield import Shield

client = anthropic.Anthropic()
shield = Shield(level=5)

def secure_claude(prompt: str):
    check = shield.protect_input(prompt, "Claude")
    if check["blocked"]:
        return "Blocked"
    
    message = client.messages.create(
        model="claude-3-opus-20240229",
        messages=[{"role": "user", "content": check["secured_context"]}]
    )
    
    output = shield.protect_output(message.content[0].text, check["metadata"])
    return output["safe_response"]

See examples/ for more integrations.

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Security Levels

Choose the right level for your needs:

Level	Protection	Latency	Use Case
L3	Pattern-based	~0.1ms	Fast, pattern matching only
L5	Production	~0.1-30ms	Recommended ⭐ Pattern + semantic (if enabled)

Shield(level=3)  # Fast pattern-only protection
Shield(level=5)  # Production (pattern + optional semantic)

Performance breakdown:

• Pattern matching: ~0.1ms
• Semantic matching (optional): +20-30ms
• PII detection: +1-5ms
• Output sanitization: ~1-2ms

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Attack Protection

PromptShield detects and blocks:

• Prompt injection ("Ignore all previous instructions")
• Jailbreaks ("You are DAN, an AI without restrictions")
• System prompt extraction ("What are your instructions?")
• PII leakage (emails, SSNs, credit cards)
• Encoding attacks (base64, ROT13, unicode)
• Context manipulation
• Output manipulation
• And 40+ more attack types

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Performance

Pattern-only mode (L3):

• Latency: ~0.1ms per check
• Throughput: 10,000+ req/s
• Memory: <5MB

Production mode (L5):

• Pattern matching: ~0.1ms
• Semantic (if enabled): +20-30ms
• Total: ~0.1-30ms depending on features
• Memory: <10MB (or +500MB if semantic models loaded)

Honest benchmarks: Pattern matching is extremely fast. Semantic matching adds latency but improves detection. Choose based on your latency requirements.

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Documentation

• Security Levels - Choose the right protection level
• API Reference - Complete API documentation
• Best Practices - Production deployment guide
• Examples - Integration examples

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Why PromptShield?

vs. LLM Guard

• ⚡ 10x faster (0.05ms vs 0.5ms)
• 🔌 Framework-agnostic (they're FastAPI-only)

vs. Guardrails AI

• 🎯 Attack-focused (they're validation-focused)
• 🚀 Simpler (3 lines vs complex schemas)

vs. DIY Solutions

• ✅ Battle-tested (51 attack patterns)
• ⚡ Optimized (<0.1ms latency)
• 🔄 Maintained (regular updates)

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Contributing

We welcome contributions! See CONTRIBUTING.md.

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License

MIT License - see LICENSE

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Citation

@software{promptshield2024,
  title={PromptShield: Universal AI Security Framework},
  author={Neural Alchemy},
  year={2024},
  url={https://github.com/neuralalchemy/promptshield}
}

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Built by Neural Alchemy

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