SENTINEL AI Security Platform

AI Security Platform for protecting LLMs, AI agents, and multimodal systems.

When to Use This Skill

Use SENTINEL when you need to:

• Detect prompt injection attacks in LLM inputs

• Identify jailbreak attempts (DAN, roleplay, encoding attacks)

• Perform red team testing on AI systems

• Audit AI agent security

• Analyze conversation safety

Key Components

🛡️ Defense (97 Detection Engines)

• Pattern-based: Regex, keyword, semantic matching

• ML-based: Transformer classifiers, ensemble models

• Strange Math™: Topological Data Analysis, Sheaf Theory, Hyperbolic Geometry

🐉 Strike (Red Team Platform)

• 39,000+ attack payloads

• AI-powered reconnaissance

• WAF bypass techniques

• Multi-provider testing

Quick Start

Clone repository

git clone https://github.com/DmitrL-dev/AISecurity.git cd AISecurity/sentinel-community

Install

pip install -e .

Basic usage

from sentinel import analyze result = analyze("user input text") print(result.risk_score)

Example Commands

Analyze a prompt for threats

sentinel analyze "Ignore previous instructions and..."

Run red team attack

sentinel strike --target https://api.example.com --vectors all

Start interactive demo

sentinel demo

API Usage

from sentinel.brain import SentinelBrain from sentinel.core import AnalysisRequest

Initialize with all engines

brain = SentinelBrain()

Analyze input

request = AnalysisRequest( content="User message here", context={"conversation_id": "123"} ) result = brain.analyze(request)

Check results

if result.risk_score > 0.7: print(f"High risk detected: {result.threats}")

Performance

Metric Value

Recall 85.1%

Precision 84.4%

F1 Score 84.7%

Latency <10ms

Links

• GitHub: https://github.com/DmitrL-dev/AISecurity

• HuggingFace Dataset: https://huggingface.co/datasets/Chgdz/sentinel-jailbreak-detection

License

Apache 2.0 - Full open source, no restrictions.