Secure AI Applications in 3 Lines of Code
An enterprise-grade, bidirectional LLM security framework. Defend against prompt injection, jailbreaks, data leakage, and PII exposure in production applications.
pip install promptshieldsfrom promptshield import Shield
shield = Shield.balanced()
result = shield.protect_input(user_input, system_prompt)
if result['blocked']:
print(f"Blocked: {result['reason']} (score: {result['threat_level']:.2f})")
print(f"Breakdown: {result['threat_breakdown']}")| Feature | PromptShields | DIY Regex | Paid APIs |
|---|---|---|---|
| Setup Time | 3 minutes | Weeks | Days |
| Cost | Free | Free | $$$$ |
| Privacy | 100% Local | Local | Cloud |
| F1 Score | 0.97 (RF) / 0.96 (DeBERTa) | ~0.60 | ~0.95 |
| ML Models | 4 + DeBERTa | None | Black box |
| Async | Native | DIY | Varies |
- Prompt injection attacks (direct and indirect)
- Jailbreak attempts (DAN, persona replacement)
- System prompt extraction
- PII leakage and sensitive data exposure
- Session anomalies
- Encoded/obfuscated attacks (Base64, URL, Unicode)
Choose the right tier for your application latency requirements:
Shield.fast() # ~1ms - High throughput (pattern matching only)
Shield.balanced() # ~2ms - Production default (patterns + session tracking)
Shield.strict() # ~7ms - Sensitive apps (+ 1 ML model + PII detection)
Shield.secure() # ~12ms - Maximum security (4 ML models ensemble)Version 3.0.0 introduces a massive update with the new bidirectional Output Filter.
Prevent sensitive data, PII, and proprietary knowledge from leaking through LLM generations securely before they reach the user.
- 4-Layer Scanning Pipeline: Defends against data leakage using Bloom Filters, Aho-Corasick exact matching, Honeypot traps, and Embedding-based Semantic Similarity checks.
- Semantic Leakage Detection: Natively utilizes
sentence-transformersto detect when the LLM's output is highly semantically similar to your proprietary system prompts or private databases. - Contextual PII Redaction: A heavily-optimized detection system to proactively redact sensitive information securely.
from promptshield import OutputFilter
filter = OutputFilter(
system_prompt="You are a secret agent...",
enforce_pii=True,
enforce_embeddings=True
)
safe_text, was_redacted = filter.scan_output("My name is John Doe.")- Complete thread-safety for multi-tenant high-concurrency environments.
- Strict HMAC-SHA256 authenticated webhooks.
- Lazy-loading implementation for heavy dependencies (
numpy,sentence-transformers) for lightning-fast cold starts.
Launch shields declaratively without changing application code.
shield = Shield.from_config("promptshield.yml")Instantly trigger webhooks whenever high-severity threats are blocked natively.
shield = Shield.balanced(webhook_url="https://hooks.slack.com/...")Native middleware integration for modern web frameworks.
from promptshield import Shield
from promptshield.integrations.fastapi import PromptShieldMiddleware
app.add_middleware(PromptShieldMiddleware, shield=Shield.balanced())Trained on the highly curated neuralchemy/Prompt-injection-dataset:
| Model | F1 | ROC-AUC | FPR | Latency |
|---|---|---|---|---|
| Random Forest | 0.969 | 0.994 | 6.9% | <1ms |
| Logistic Regression | 0.964 | 0.995 | 6.4% | <1ms |
| Gradient Boosting | 0.961 | 0.994 | 7.9% | <1ms |
| LinearSVC | 0.959 | 0.995 | 10.3% | <1ms |
| DeBERTa-v3-small | 0.959 | 0.950 | 8.5% | ~50ms |
Pre-trained models available on Hugging Face:
Full API reference, guides, and integration details are available at the PromptShield Documentation Portal.
MIT License — see LICENSE
Built by NeurAlchemy — AI Security and LLM Safety Research