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 attacker@evil.com. - !Prompt-level filters can't guarantee safety. Model behavior is non-deterministic.
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.
The Scanner
Red-team any OpenAI-compatible endpoint with deceptive prompt-injection payloads. See exactly which attacks trick your agent into calling a tool.
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.
An attack, stopped in real time. #
A prompt-injected agent tries to exfiltrate an API key. ModelFuzz catches it at the execution layer.
Want a hosted dashboard for your team? #
Centralized policies, audit logs, and continuous agent scanning. Join the waitlist for early access.