securevector-ai-monitor 2.1.0

Real-time AI threat monitoring. Protect your apps from prompt injection, leaks, and attacks in just a few lines of code.

SecureVector

Runtime Firewall for AI Agents & Bots

Block prompt injection, jailbreaks, and data leaks before they reach your AI.

Website · Getting Started · Local App Screenshots

How It Works

SecureVector sits between your AI agent and the LLM provider, scanning every request and response for security threats. Runs entirely on your machine — nothing leaves your infrastructure.

pip install securevector-ai-monitor[app]
securevector-app --web

Or download: Windows · macOS · Linux · DEB · RPM

Open-source. 100% local by default. No API keys required.

Highlights

• ☑ 100% Local by Default — No data transmitted externally. Complete privacy.
• ☑ Agents Protected — LangChain, LangGraph, CrewAI, n8n, OpenClaw, and any OpenAI-compatible app.
• ☑ Input Scanning — Block prompt injection, jailbreaks, and manipulation before they reach the LLM.
• ☑ Output Scanning — Detect credential leaks, PII exposure, and system prompt disclosure.
• ☑ 18+ Providers — OpenAI, Anthropic, Gemini, Ollama, Groq, Azure, and more.
• ☑ Full Visibility — Real-time dashboard shows every threat, who sent it, and what was blocked.
• ☑ Protect Your API Account — Block abuse before it triggers ToS violations or key suspension.
• ☑ One Command — securevector-app --web and follow the UI to start protecting.

What SecureVector Catches

1. Your API account is the real target. One successful jailbreak generating prohibited content gets your key suspended. All your users lose service.

2. You have zero visibility. Without SecureVector, you don't know who's abusing your app until OpenAI sends you a ToS violation notice.

3. LLMs can't police their own output. When your bot has access to user data, it doesn't know what's sensitive. SecureVector catches leaked credentials, PII, and system prompts in responses.

4. Blocked requests are free requests. Junk gets stopped locally in ~50ms — you never pay the API for processing it.

Example: You built an image generation app with 100 users on DALL-E 3 ($0.04/image). Ten users discover they can jailbreak your bot and start generating free images for fun — 20 junk requests/day each. That's 200 × $0.04 × 30 = $240/month in abuse. SecureVector blocks them all locally for $0.

Install

Option 1: pip

Requires: Python 3.9+ (MCP requires 3.10+)

pip install securevector-ai-monitor[app]
securevector-app --web

Option 2: Binary installers

No Python required. Download and run.

Platform	Download
Windows	SecureVector-v2.1.0-Windows-Setup.exe
macOS	SecureVector-2.1.0-macOS.dmg
Linux (AppImage)	SecureVector-2.1.0-x86_64.AppImage
Linux (DEB)	securevector_2.1.0_amd64.deb
Linux (RPM)	securevector-2.1.0-1.x86_64.rpm

All Releases · SHA256 Checksums

Security: Only download installers from this official GitHub repository. Always verify SHA256 checksums before installation. SecureVector is not responsible for binaries obtained from third-party sources.

Quick Start

Step 1: Start SecureVector app

securevector-app --web

Or launch the binary installer if you downloaded one.

Step 2: Go to Integrations in the UI, choose your agent framework and LLM provider, then click Start Proxy.

Step 3: Point your app to the proxy (shown in the UI).

That's it! Every request is scanned for prompt injection. Every response is scanned for data leaks.

Supported providers: openai anthropic gemini ollama groq openrouter deepseek mistral xai azure together fireworks perplexity cohere cerebras lmstudio litellm

Agent Integrations

Agent/Framework	Integration
LangChain	LLM Proxy or SDK Callback
LangGraph	LLM Proxy or Security Node
CrewAI	LLM Proxy or SDK Callback
Ollama / Open WebUI	LLM Proxy — see Integrations in UI
OpenClaw / ClaudBot	LLM Proxy — see Integrations in UI
n8n	Community Node
Claude Desktop	MCP Server Guide
Any OpenAI-compatible app	LLM Proxy — set OPENAI_BASE_URL to proxy
Any HTTP Client	POST http://localhost:8741/analyze with {"text": "..."}

What It Detects

Input Threats (User → LLM)	Output Threats (LLM → User)
Prompt injection	Credential leakage (API keys, tokens)
Jailbreak attempts	System prompt exposure
Data exfiltration requests	PII disclosure (SSN, credit cards)
Social engineering	Jailbreak success indicators
SQL injection patterns	Encoded malicious content

Full coverage: OWASP LLM Top 10

Screenshots

Dashboard — stats, risk distribution, recent threats	Threat Analytics — blocked, redacted, logged
Integrations — LangChain, Ollama, OpenClaw, and more	Detection Rules — community rules, or create your own for your use case or industry
Getting Started — onboarding guide with setup steps

Documentation

• Installation Guide — Binary installers, pip, service setup
• Use Cases & Examples — LangChain, LangGraph, CrewAI, n8n, FastAPI
• MCP Server Guide — Claude Desktop, Cursor integration
• API Reference — REST API endpoints
• Security Policy — Vulnerability disclosure

Editions

Other install options

Install	Use Case	Size
pip install securevector-ai-monitor	SDK only — lightweight, for programmatic integration	~18MB
pip install securevector-ai-monitor[mcp]	MCP server — Claude Desktop, Cursor	~38MB

Open Source vs Cloud

Open Source (100% Free)	Cloud (Optional)
Apache 2.0 license	Expert-curated rule library
Community detection rules	Multi-stage ML threat analysis
Custom YAML rules	Real-time cloud dashboard
100% local by default, no data sharing	Team collaboration
Desktop app + local API	Priority support

Cloud is optional. SecureVector runs entirely locally by default. Connect to app.securevector.io only if you want enterprise-grade threat intelligence with specialized algorithms designed to minimize false positives.

Try Free

Update

Method	Command
PyPI	pip install --upgrade securevector-ai-monitor[app]
Source	git pull && pip install -e ".[app]"
Windows	Download latest .exe installer and run it (overwrites previous version)
macOS	Download latest .dmg, drag to Applications (replace existing)
Linux AppImage	Download latest .AppImage and replace the old file
Linux DEB	sudo dpkg -i securevector_<version>_amd64.deb
Linux RPM	sudo rpm -U securevector-<version>.x86_64.rpm

After updating, restart SecureVector.

Contributing

git clone https://github.com/Secure-Vector/securevector-ai-threat-monitor.git
cd securevector-ai-threat-monitor
pip install -e ".[dev]"
pytest tests/ -v

Contributing Guidelines · Code of Conduct

License

Apache License 2.0 — see LICENSE.

SecureVector is a trademark of SecureVector. See NOTICE.

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Get Started · Documentation · GitHub Issues · [e-mail]