When AI Agents Go Rogue
What happens, if your agentic AI software has unrestricted privileges on your machine?
An AI agent with unrestricted database access deleted the entire production database because it "panicked".
Copilot messed up a tiny detail in a command of an engineer. Executed the CMD command and deleted the users entire "D:/" directory with all private photos and videos.
An attack on the Gemini AI CLI allowed attackers to silently execute malicious commands on the users machine.
Don't let this happen to you.
GuardiAgent provides enterprise-grade sandboxing to isolate and protect your systems from rogue AI agents and compromised MCP servers.
Our Vision: Enterprise Security for AI Applications
Comprehensive protection that does not slow down your application or compromise your system.
Build Secure AI Agents in Minutes
Simple APIs, compatible with current agent SDKs. Integrate the GuardiAgent security sandbox with little to no changes in your code.
import os
from agents import Agent
from mcp_sandbox_openai_sdk import (
FSAccess,
SandboxedMCPStdio
)
async def main():
async with SandboxedMCPStdio(
manifest=manifest,
runtime_args=[os.path.abspath("./")],
runtime_permissions=[
FSAccess(os.path.abspath("./"))
],
) as server:
agent = Agent(
name="MCP Sandbox Test",
model="gpt-5-mini",
mcp_servers=[server],
)
Evidence-Based AI Security
Our research demonstrates the critical need for MCP server isolation and the effectiveness of our sandboxing approach. Stay tuned with our latest findings.
Key Research Findings
Our evaluation shows that GuardiAgent - the security sandbox of GuardiAgent - does not imply a performance overhead. The introduced overhead is negligible in practice.
The diagram shows the comparison between the runtime of the four most prevalent MCP server operations, when executed with and without the sandbox, on two hardware environments, i.e., macOS and Debian. The sandbox adds, on average, across all operations, 0.6 ms on macOS and 0.29 ms on Debian, both essentially negligible.