USE CASE
Prompt Injection Defense
You cannot stop people from talking to your AI. You can stop what happens next. Somewhere, someone will talk your AI into something: a clever prompt, a poisoned document, a chained request. A filter reads words and hopes. We check the action itself. However the AI was persuaded, whatever it now believes, the action still has to pass your policy before it runs. Persuasion is not permission.
Attackers hide instructions inside the things your AI is supposed to read. An email your AI summarizes. A document your AI processes. A support ticket your AI responds to. A web page your AI scrapes. The instructions look like normal content to a human. To your AI, they are commands.
Your AI reads them. Your AI follows them. Suddenly an autonomous agent is sending customer data to an outside email address, deleting records it was never asked to delete, or posting messages it was never authorized to send.
The risk is not the attacker. The risk is that your AI cannot tell the difference between data it is supposed to read and instructions it is supposed to follow. To the model, everything that comes in is just text. The hidden instruction reads the same as a normal sentence.
This is the structural gap. Your AI was built to be helpful. Helpfulness without a control layer is a vulnerability. The AI followed the instruction. Nothing checked whether the instruction was allowed.
Mountain Theory sits between your AI’s reasoning and the action it is about to take. We evaluate every input going into your AI and every output coming back out, inline. Clean prompts pass through. Malicious ones get blocked or held for human review. Every attempt is logged.
Built for environments where one compromised prompt can trigger real-world consequences. Public safety. Defense. Financial services. Healthcare. The places where an autonomous action carries an immediate cost.
You want your AI reading customer emails, processing documents, and handling tickets. You also do not want a buried instruction in one of them turning your AI into the attacker’s tool. Mountain Theory lets you do both.
- ✓Real-time detection of malicious instructions hidden inside content your AI reads
- ✓Checks on both what goes into the AI and what comes out before any action executes
- ✓Policy written in plain English, not code
- ✓Three outcomes at every gate: allow, hold for human review, or block
- ✓Coverage for prompts hidden in emails, documents, web pages, support tickets, and any other content source
- ✓Full audit trail of every blocked attempt, ready for any incident response
- ✓Decisions made inline so the business keeps moving
Malicious instructions stop at the line, every attempt is logged, every policy is written in plain English, every incident response is defensible without a translator.
See how this plays out in a real incident: read the Amazon Q supply chain case study in our Threat Lab.