# I built a Python library to stop AI agents from leaking secrets (ModelFuzz)

> Source: <https://dev.to/higagan/i-built-a-python-library-to-stop-ai-agents-from-leaking-secrets-modelfuzz-6o2>
> Published: 2026-07-23 17:58:20+00:00

I've been building AI agents lately, and honestly, their security model terrifies me.

We give LLMs access to powerful tools like `shell.run`

, `http.post`

, and `fs.read`

. But if an agent reads a malicious email or a poisoned webpage, it can be tricked by prompt injection into using those tools to exfiltrate data.

Hoping the LLM refuses the attack isn't a real security strategy.

So, I built ModelFuzz. It's an open-source Python library that intercepts the tool call at the execution layer.

`@shield_tool`

Instead of trying to filter prompts, ModelFuzz checks the arguments before the tool runs. If it detects a policy violation, like a stolen API key or an unsafe URL, the tool simply doesn't execute.

``` python
from modelfuzz import shield_tool

@shield_tool
def send_email(to_address: str, subject: str, body: str) -> None:
    smtp.send(to_address, subject, body)
```

Even if the LLM is completely tricked by a prompt injection, the tool never fires.

`modelfuzz scan`

I also built a CLI scanner that red-teams your agent. It fires deceptive prompt injection attacks at your local model to see if it can be tricked into calling a tool.

```
modelfuzz scan --endpoint http://localhost:11434/v1 --model qwen2.5:1.5b
```

I tested it against a local `qwen2.5:1.5b`

model, and it got breached 4 out of 5 times.

It's 100% open source and live on PyPI.

```
pip install "modelfuzz[scan]"
```

I'd love to know what security policies you think are missing, or what agent frameworks you want supported next!