securevector-ai-monitor 3.2.0

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

SecureVector

AI Firewall for Agents — Block prompt injection, tool abuse, and data leaks before and after the LLM.

Protect your AI agents, track costs, and set budget limits — no coding required. Download the app or install with pip.

Website · Getting Started · Discord · Dashboard Screenshots

▶ Watch the Demo

Threat detection, tool permissions, and cost tracking — running locally in real time.

🆕 New in v3.2.0:

• Skill Scanner — static analysis for AI agent skills with optional AI-powered review
• Skill Scan Policy Engine — risk scoring, trusted publishers, and per-category allow/block rules
• Tool Permissions — allow/block agent tool calls
• Cost Tracking & Budget Limits — per-agent spend tracking and global daily budget
• 28 new threat detection rules

How It Works

SecureVector sits between your AI agent and the LLM provider, scanning every request and response for security threats, controlling tool permissions, and tracking spend in real time. Runs entirely on your machine — nothing leaves your infrastructure.

The Problem	The Fix
AI agents are powerful — and completely unprotected. Every prompt your AI agent sends, every secret it handles, every piece of user data — goes straight to the LLM provider with nothing in between. No spend limit. No injection protection. No audit trail. You're flying blind.	SecureVector runs on your machine, between your AI agents and LLM providers. It starts with a multi-provider proxy mode for routing across OpenAI, Anthropic, Ollama, and more — all through a single endpoint. It blocks threats, enforces tool permissions, and hard-stops agents that blow their budget. 100% local. No accounts.

Quick Start

Step 1 — Install or download

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

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

Step 2 — Open the app

Open http://localhost:8741 in your browser, or double-click the installed binary.

Step 3 — Connect your agent

Go to the Integrations tab for step-by-step setup guides — OpenClaw, LangChain, CrewAI, LangGraph, n8n, Ollama, OpenAI, Anthropic, and more.

If the app fails to launch because ports 8741/8742 are already in use, use --port <port> of your choice — the proxy starts automatically on port+1. See Configuration for proxy or web/api port settings.

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

Screenshots

All screenshots are from a local app instance.

Tool Call History — 305 calls, 158 blocked: bash rm -rf, gmail_send to attacker, use_aws_cli stopped	Tool Permissions — allow or block tools by name or category	Tool Call Detail — decision, tool, args, and timestamp for every call
Dashboard — threat counts, cost metrics, and tool permission status	LLM Cost Tracker — per-agent spend, budgets, and token breakdown	Custom Rules — create and manage detection rules by category and severity
Skill Scanner — static security analysis for AI agent skills with scan history and risk levels	Skill Policy — network permissions, trusted publishers, and policy thresholds

What You Get

Threat Protection	Cost Control
Scans every prompt and response for prompt injection, jailbreaks, PII leaks, and tool abuse. 50+ detection rules covering the OWASP LLM Top 10. Detects and logs threats by default — enable block mode when you're ready to hard-stop them.	Tracks every token and dollar per agent in real time. Set daily budget limits — requests auto-stop when the cap is hit. Never wake up to a surprise bill.
Skill Scanner	Full Visibility
Scan agent skills and tool packages before installing. Static analysis across 10 categories detects shell access, network calls, env var reads, and more. Optional AI review filters false positives automatically.	Live dashboard showing every LLM request, tool call, token count, and threat event. See exactly what your agents are doing.
100% Local
Runs entirely on your machine. No accounts. No cloud. No data leaves your infrastructure. Open source under Apache 2.0.

Features

Section	Feature	Description
Monitor	Threat Monitor	Live feed of every detected threat — prompt injection, jailbreaks, data leaks, tool abuse
	Tool Activity	Full audit log of every tool call your agents make, with args, decision, and timestamp
	Cost Tracking	Per-agent, per-model token spend and USD cost in real time, with request history
Scan	Skill Scanner	Static analysis of AI agent skills — detects shell exec, network access, env var reads, code injection, and 6 more categories
	AI Review	Optional LLM-powered false-positive filtering — works with OpenAI, Anthropic, Ollama, Azure, Bedrock
	Scan Policy	Risk scoring with per-category allow/block rules, trusted publishers, and severity thresholds
Configure	Tool Permissions	Allow or block specific tools by name or category — per agent, per rule
	Cost Settings	Set daily budget limits and choose whether to warn or hard-block at the cap
	Rules	Custom detection rules — auto-block or alert on threats matching your criteria

Performance: Rule-based analysis (default) adds ~10–50ms per request. Enabling optional AI analysis adds 1–3s per request depending on the model and provider — this is shown on the dashboard so you can measure it against your actual traffic.

Why SecureVector?

❌ Without SecureVector	✅ With SecureVector
Prompt injections pass straight through	Detected and logged by default; blocked when you enable block mode
API keys and PII leak in prompts	Automatically redacted
No control over what tools agents can use	Fine-grained allow/block rules per tool
No audit trail of tool calls	Full tool call history with decisions and reasons
No idea what agents are spending	Real-time cost tracking per agent
Runaway agents burn through your API budget overnight	Hard budget limits with auto-stop
Zero visibility into agent traffic	Live dashboard showing everything

Works With Everything

Your AI Stack

LangChain · LlamaIndex · CrewAI · AutoGen · LangGraph · n8n · Dify · OpenClaw/ClawdBot (LLM gateway agent framework) — or any framework that makes HTTP calls to an LLM provider.

LLM Providers

OpenAI · Anthropic · Ollama · Groq · and any OpenAI-compatible API.

Run Anywhere

Environment	Details
Local	macOS, Linux, Windows
Cloud	AWS, GCP, Azure
Containers	Docker & Kubernetes
Virtual Machines	EC2, Droplets, VMs
Edge / Serverless	Lambda, Workers, Vercel

Agent Integrations

Agent/Framework	Integration
LangChain	LLM Proxy or SDK Callback
LangGraph	LLM Proxy or Security Node
CrewAI	LLM Proxy or SDK Callback
Any OpenAI-compatible	LLM Proxy — see Integrations in UI
OpenClaw / ClawdBot (LLM gateway agent)	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 to LLM)	Output Threats (LLM to 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
Tool result injection (MCP)	—
Multi-agent authority spoofing	—
Permission scope escalation	—

Full coverage: OWASP LLM Top 10

AI Agent Attack Protection (28 new rules · 72 total)

Built from real attack chains observed against production agent frameworks:

• Tool Result Injection — injected instructions hidden inside MCP tool responses
• Multi-Agent Authority Spoofing — impersonating trusted agents in multi-agent pipelines
• Permission Scope Escalation — agents requesting more permissions than granted
• MCP Tool Call Injection — malicious payloads delivered through MCP tool calls
• Evasion techniques (22 rules) — zero-width characters, encoding tricks, roleplay framing, leetspeak, semantic inversion, emotional manipulation, and more

Skill Scanner

Scan AI agent skills and tool packages before you install them. SecureVector performs static analysis across 10 detection categories, assigns a risk score, and optionally runs an AI review to filter false positives.

┌──────────────────────────────────────────────────────────────────────┐
│                        Skill Scanner Flow                           │
│                                                                     │
│   ┌─────────────┐     ┌──────────────────┐     ┌────────────────┐  │
│   │  Skill Dir   │────>│  Static Analysis  │────>│  Risk Scoring  │  │
│   │  or URL      │     │  (10 categories)  │     │  LOW/MED/HIGH  │  │
│   └─────────────┘     └──────────────────┘     └───────┬────────┘  │
│                                                         │           │
│                              ┌───────────────────────── │           │
│                              v                          v           │
│                     ┌─────────────────┐     ┌────────────────────┐  │
│                     │  AI Review      │     │  Policy Engine     │  │
│                     │  (optional LLM) │     │  allow/block rules │  │
│                     │  FP filtering   │     │  trusted publishers│  │
│                     └────────┬────────┘     └─────────┬──────────┘  │
│                              │                        │             │
│                              v                        v             │
│                     ┌──────────────────────────────────────┐        │
│                     │  Verdict: PASS / WARN / BLOCK        │        │
│                     │  + detailed findings per category     │        │
│                     └──────────────────────────────────────┘        │
└──────────────────────────────────────────────────────────────────────┘

Detection Categories

Category	What It Finds
shell_exec	Subprocess calls, system commands
network_domain	HTTP requests, socket connections, DNS lookups
env_var_read	Access to environment variables (API keys, secrets)
code_exec	eval, dynamic code generation
dynamic_import	Runtime module loading
file_write	Writing to disk outside expected paths
base64_literal	Obfuscated payloads in base64 strings
compiled_code	.pyc, .so, .dll binaries embedded in the skill
symlink_escape	Symlinks pointing outside the skill directory
missing_manifest	No permissions.yml declaring required capabilities

AI-Powered Review

Enable AI analysis (OpenAI, Anthropic, Ollama, Azure, or Bedrock) to automatically review findings and filter false positives. The AI examines each finding in context and adjusts the risk level — reducing noise without hiding real threats.

Open Source

SecureVector is fully open source. No cloud required. No accounts. No tracking. Run it, fork it, contribute to it.

Built for solo developers and small teams who ship AI agents without a security team or a FinOps budget. If you are building with LangChain, CrewAI, OpenClaw, or any agent framework — and you do not have someone watching your agent traffic and API spend — SecureVector is for you.

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

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-v3.2.0-Windows-Setup.exe
macOS	SecureVector-3.2.0-macOS.dmg
Linux (AppImage)	SecureVector-3.2.0-x86_64.AppImage
Linux (DEB)	securevector_3.2.0_amd64.deb
Linux (RPM)	securevector-3.2.0-1.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.

Other install options

Install	Use Case	Size
pip install securevector-ai-monitor	SDK only — lightweight, for programmatic integration	~18MB
pip install securevector-ai-monitor[app]	Full app — web UI, LLM proxy, cost tracking, tool permissions	453 KB wheel · ~16 MB total on disk (incl. dependencies)
pip install securevector-ai-monitor[mcp]	MCP server — Claude Desktop, Cursor	~38MB

Configuration

SecureVector writes svconfig.yml to your app data directory on first run with sensible defaults.

# SecureVector Configuration
# Changes take effect on next restart.
# The config path is printed to the console when you start the app.
#
# Linux:   ~/.local/share/securevector/threat-monitor/svconfig.yml
# macOS:   ~/Library/Application Support/SecureVector/ThreatMonitor/svconfig.yml
# Windows: %LOCALAPPDATA%/SecureVector/ThreatMonitor/svconfig.yml

server:
  # Web UI / API server listen host and port.
  # Change these if port 8741 is already in use on your machine.
  # If running on a remote server, set host to the server's hostname or IP address.
  host: 127.0.0.1
  port: 8741

security:
  # Block detected threats (true) or log/warn only (false)
  # Defaults to false — enable when you're confident in your rule tuning
  block_mode: false
  # Scan LLM responses for data leakage and PII
  output_scan: true

budget:
  # Daily spend limit in USD (set to null to disable)
  daily_limit: 5.00
  # Warn in logs/headers when spend approaches the limit
  warn: true
  # Block requests when the daily budget is exceeded
  block: true

tools:
  # Enforce tool permission rules (allow/block based on your rules)
  enforcement: true           # default: true

proxy:
  # Proxy auto-starts with securevector-app --web when mode is set below.
  integration: openclaw       # or: langchain, langgraph, crewai, ollama
  mode: multi-provider        # or: single (add provider: below)
  provider: null              # required only when mode is "single"
  host: 127.0.0.1             # proxy listen host — set to the server's hostname or IP if running remotely
  port: 8742                  # proxy listen port (default: server.port + 1)

The UI keeps this file in sync — changes in the dashboard are written back to svconfig.yml automatically.

Pointing Your Agent at the Proxy

Point any application to SecureVector's proxy instead of the provider's API.

🪟 Windows

🐧 Linux / macOS

Command Prompt (current session) set OPENAI_BASE_URL=http://localhost:8742/openai/v1 set ANTHROPIC_BASE_URL=http://localhost:8742/anthropic PowerShell (current session) $env:OPENAI_BASE_URL="http://localhost:8742/openai/v1" $env:ANTHROPIC_BASE_URL="http://localhost:8742/anthropic" PowerShell (persistent, per user) [Environment]::SetEnvironmentVariable( "OPENAI_BASE_URL", "http://localhost:8742/openai/v1", "User" )

Terminal (current session) export OPENAI_BASE_URL=http://localhost:8742/openai/v1 export ANTHROPIC_BASE_URL=http://localhost:8742/anthropic Persistent (add to ~/.bashrc or ~/.zshrc) echo 'export OPENAI_BASE_URL=http://localhost:8742/openai/v1' >> ~/.bashrc echo 'export ANTHROPIC_BASE_URL=http://localhost:8742/anthropic' >> ~/.bashrc source ~/.bashrc

Every request is scanned for prompt injection. Every response is scanned for data leaks. Every dollar is tracked.

Supported providers (13): openai anthropic gemini ollama groq deepseek mistral xai together cohere cerebras moonshot minimax

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.

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

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.

---

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