https://github.com/m4vic/promptshields
An enterprise-grade, bidirectional LLM security framework. Defend against prompt injection, jailbreaks, data leakage, and PII exposure in production applications.
https://github.com/m4vic/promptshields
agenticsecops ai ai-agents aisecurity cybersecurity framework llm llm-evaluation llmsecurity security-tools
Last synced: 3 days ago
JSON representation
An enterprise-grade, bidirectional LLM security framework. Defend against prompt injection, jailbreaks, data leakage, and PII exposure in production applications.
- Host: GitHub
- URL: https://github.com/m4vic/promptshields
- Owner: m4vic
- License: mit
- Created: 2026-06-30T09:55:40.000Z (27 days ago)
- Default Branch: main
- Last Pushed: 2026-07-04T11:13:59.000Z (23 days ago)
- Last Synced: 2026-07-04T13:06:17.411Z (23 days ago)
- Topics: agenticsecops, ai, ai-agents, aisecurity, cybersecurity, framework, llm, llm-evaluation, llmsecurity, security-tools
- Language: Python
- Homepage:
- Size: 11.8 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# PromptShields
**Secure AI Applications in 3 Lines of Code**
[](https://pypi.org/project/promptshields/)
[](https://pypi.org/project/promptshields/)
[](LICENSE)
[](https://pepy.tech/project/promptshields)
bidirectional LLM security framework. Defend against prompt injection, jailbreaks, data leakage, and PII exposure in production applications.
--------------------------------------------------------------------------------
## Installation
```bash
pip install promptshields
```
### Optional extras
Core install covers pattern matching, session tracking, canary tokens, and the
pre-trained ML ensemble. Some features need extra dependencies:
```bash
# Output DLP engine (Bloom filters, Aho-Corasick, semantic similarity)
pip install "promptshields[output]"
# Semantic/embedding-based matching (sentence-transformers)
pip install "promptshields[ml]"
# Framework integrations (LangChain, LiteLLM, LlamaIndex, CrewAI)
pip install "promptshields[integrations]"
# Everything
pip install "promptshields[all]"
```
## Quick Start
```python
from 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']}")
```
--------------------------------------------------------------------------------
## Features & Capabilities
| 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** | 3 + DeBERTa | None | Black box |
| **Async** | Native | DIY | Varies |
### Protection Scope
- 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)
--------------------------------------------------------------------------------
## Security Modes
Choose the right tier for your application latency requirements:
```python
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 (3 ML models ensemble)
```
--------------------------------------------------------------------------------
## Upgrading to v3.0.0
Version 3.0.0 introduces a massive update with the new bidirectional **Output Filter**.
### Output Engine (Data Leakage Prevention)
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-transformers` to 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.
```python
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.")
```
### Performance & Hardening
- 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.
--------------------------------------------------------------------------------
## Developer Experience
### YAML Configuration
Launch shields declaratively without changing application code.
```python
shield = Shield.from_config("promptshield.yml")
```
### Slack and Teams Webhooks
Instantly trigger webhooks whenever high-severity threats are blocked natively.
```python
shield = Shield.balanced(webhook_url="https://hooks.slack.com/...")
```
### Async and FastAPI Support
Native middleware integration for modern web frameworks.
```python
from promptshield import Shield
from promptshield.integrations.fastapi import PromptShieldMiddleware
app.add_middleware(PromptShieldMiddleware, shield=Shield.balanced())
```
--------------------------------------------------------------------------------
## Benchmark Results
Trained on the highly curated [neuralchemy/Prompt-injection-dataset](https://huggingface.co/datasets/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:
- [prompt-injection-detector](https://huggingface.co/neuralchemy/prompt-injection-detector)
- [prompt-injection-deberta](https://huggingface.co/neuralchemy/prompt-injection-deberta)
--------------------------------------------------------------------------------
## Documentation
Full API reference, guides, and integration details are available in the **[GitHub repository](https://github.com/m4vic/promptshields)**.
--------------------------------------------------------------------------------
## License
MIT License — see [LICENSE](LICENSE)
**Built by [Sanskar Jajoo / NeurAlchemy](https://github.com/m4vic)** — AI Security and LLM Safety Research