ModelFuzz

ModelFuzz

ModelFuzz provides runtime guardrails for LLM agents: a red-team scanner that exposes prompt-injection vulnerabilities and a lightweight Python decorator that intercepts and blocks unsafe tool calls at execution time.

ModelFuzz is security software teams evaluate for security. Use this page to review pricing, integration signals, and the best alternatives before you commit.

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#13 in Security (13 tools)
Just launched
Data reviewed Jul 27, 2026

Quick Overview

Best for: Security

What it does

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Best fit

Security

Pricing snapshot

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ModelFuzz

ModelFuzz is a developer-focused tool that implements runtime guardrails for LLM-based agents. It supplies a red-team scanner to probe OpenAI-compatible endpoints with prompt-injection payloads to surface which attacks can trick an agent into calling tools, and a lightweight Python decorator (shield_tool) that intercepts tool calls at execution time and blocks them when arguments violate policy. The project is distributed via pip and hosted under an MIT license; the site also advertises an optional hosted dashboard (audit logs, centralized policies, continuous scanning) currently available via a waitlist.

ModelFuzz secures LLM agents against prompt injection. Scan for vulnerabilities, then block unsafe tool calls at the execution layer with one decorator.

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Key Features

Red-team Scanner

Scan OpenAI-compatible endpoints with deceptive prompt-injection payloads to discover whether an agent executes tools or is vulnerable to confused-deputy style bypasses.

Shield (shield_tool decorator)

Wrap any tool function with @shield_tool so every argument is checked against policies before execution; violations raise an error (ModelFuzzBlockError) and block dangerous actions.

Live Interception & Blocking

Intercepts attempted tool executions in real time and blocks attacks such as silent exfiltration or arbitrary command execution at the execution layer.

CLI and Installation

Installable via pip (pip install modelfuzz) and usable from the command line (modelfuzz scan) for scanning and testing agents.

Hosted Dashboard (waitlist)

Planned hosted offering providing centralized policies, audit logs, and continuous agent scanning (access via waitlist).

Pricing

Free Tier Available

Open-source MIT-licensed package available via pip (pip install modelfuzz); hosted dashboard is available via waitlist.

Use Cases

Red-team security testing

Actively probe LLM agents and OpenAI-compatible endpoints to surface prompt-injection vulnerabilities and understand which payloads cause unsafe tool calls.

Runtime protection for agent tools

Wrap agent tool functions to enforce execution-time policies and prevent data exfiltration or unauthorized actions triggered by prompt injection.

Compliance and auditing (hosted)

Collect audit logs and enforce centralized policies across teams via the planned hosted dashboard to support organizational security workflows.

Integrations

OpenAI-compatible endpoints

Scanner targets OpenAI-compatible model endpoints to test for prompt-injection vulnerabilities.

Python tool functions

Provides a decorator to wrap Python functions (tools) so arguments are checked against policies before execution.

Hosted dashboard (planned)

Centralized policies and audit logs for teams (access via waitlist as advertised).

Benefits

Prevents silent exfiltration and unsafe tool execution by blocking dangerous arguments at runtime.
Helps teams discover real-world prompt-injection attack paths with an integrated red-team scanner.
Lightweight integration via a Python decorator enables quick protection of existing agent tools without changing agent logic.

Limitations

The site notes that prompt-level filters can't guarantee safety because model behavior is non-deterministic.
Hosted dashboard and team features are advertised via a waitlist (not immediately available).

Frequently Asked Questions

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Getting Started

  1. 1 Step 1: Install the package: pip install modelfuzz
  2. 2 Step 2: Run the scanner against an OpenAI-compatible endpoint: modelfuzz scan --endpoint <URL> --model <model-name>
  3. 3 Step 3: Protect a tool by importing and using the decorator: from modelfuzz import shield_tool; @shield_tool def send_email(...): ...

Support

docs / repo

Project repository and getting-started guidance available via GitHub link on the site.

waitlist / updates

Hosted dashboard access and team onboarding available via the advertised waitlist on the site.

API

Available: No

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