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.
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Quick Overview
Best for: Security
What it does
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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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Claim this listing for $29Key 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
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
Limitations
Frequently Asked Questions
No verified FAQs are available.
Getting Started
- 1 Step 1: Install the package: pip install modelfuzz
- 2 Step 2: Run the scanner against an OpenAI-compatible endpoint: modelfuzz scan --endpoint <URL> --model <model-name>
- 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
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