# Template Injection Sanitizer MCP for AI Agents AI Agent Connect

> Template Injection Sanitizer stops malicious code from sneaking into your LLM prompts through template engines. It identifies and strips out dangerous Jinja2 expressions, Python f-string patterns, and system variable access that attackers use to hijack prompt structures. This keeps your agent's logic intact and prevents sensitive data leaks caused by unintended variable interpolation.

## Overview
- **Category:** security
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_vMDH5jCrzNMNooAhjoj0JKMiAHZSQdLFg6lwOcCA/ai-agent-connect
- **Tags:** security, prompt-injection, sanitization, jinja2, python, llm-safety

## Description

If you're building applications where users provide input that eventually gets dropped into a template, you're at risk. Attackers love to hide Jinja2 syntax or Python f-string patterns in seemingly innocent text to trick your system into revealing private variables or executing unintended logic. This MCP acts as a security layer between raw user input and your prompt templates.

Instead of manually writing regex patterns to catch every possible way someone might try to access `__class__` or other sensitive system attributes, you can let this tool handle the heavy lifting. It inspects strings for malicious patterns and cleans them before they ever reach your prompt engine. Whether you're processing a single user comment or cleaning up a massive batch of scraped data, it ensures the text remains just text, not executable code. By connecting this to your workflow through the Vinkius catalog, you add a specialized defense against prompt injection that targets the underlying template frameworks used by most modern agentic workflows.

## Tools

### batch_sanitize_inputs
Cleans a large collection of strings at once. It's perfect for scrubbing entire datasets of user-generated content.

### get_injection_risk_profile
Analyzes the density of malicious patterns. It tells you exactly how severe an injection attempt is.

### scan_input_security
Inspects a single string for dangerous syntax. It returns a version of the text with the threats removed.

## Prompt Examples

**Prompt:** 
```
Is this user input safe to put in my template: 'Hello {{ user_name }}'?
```

**Response:** 
```
The input contains a Jinja2 expression and has been flagged as an injection attempt.

**Status:** 🚩 Flagged
**Reason:** Detected Jinja2 interpolation pattern `{{ user_name }}`.
```

**Prompt:** 
```
Check this list of strings for any dangerous patterns: ['Hello world', '{secret_var}', 'Just a comment']
```

**Response:** 
```
I've processed your list. Here is the result:

| Original | Status | Action Taken |
| :--- | :--- | :--- |
| Hello world | ✅ Safe | None |
| {secret_var} | 🚩 Flagged | Removed f-string pattern |
| Just a comment | ✅ Safe | None |
```

**Prompt:** 
```
How risky is this input: 'print(obj.__class__)'?
```

**Response:** 
```
This is a high-risk input.

**Risk Profile:** 🚨 High
**Reason:** The input attempts to access sensitive system attributes via `__class__`. This is a classic injection technique used to probe object structures.
```

## Capabilities

### Scrub malicious template syntax
Removes dangerous Jinja2 and f-string patterns from user input before it hits your prompt.

### Evaluate injection severity
Determines how dangerous a specific input is based on the type of patterns detected.

### Clean bulk user content
Processes large lists of strings to strip out injection attempts in one go.

### Detect system variable access
Identifies attempts to reach internal object attributes like class definitions.

## Use Cases

### Securing a customer support bot
A user tries to inject `{{ config.api_key }}` into a chat. The MCP catches the Jinja2 pattern and cleans it before the agent sees it.

### Cleaning scraped web data
You're feeding thousands of scraped comments into an agent. Use batch_sanitize_inputs to ensure no hidden Python f-strings trigger errors.

### Hardening LangChain workflows
An attacker uses `__class__` to try and probe your system. The MCP identifies this as a high-risk injection attempt.

### Automated content moderation
A moderator uses get_injection_risk_profile to flag highly suspicious inputs for manual review before they reach the LLM.

## Benefits

- Prevents template hijacking by stripping Jinja2 and f-string patterns using scan_input_security.
- Reduces risk of data leaks by blocking access to sensitive system variables.
- Speeds up data cleaning for large datasets with batch_sanitize_inputs.
- Provides clear risk assessments via get_injection_risk_profile to help you decide when to block a user.
- Protects the integrity of your prompt logic from malicious interpolation.

## How It Works

The bottom line is you get a clean, safe string that won't break your template logic.

1. Connect the MCP to your AI client via Vinkius
2. Pass user-provided strings to the scanning tools
3. Receive cleaned text or a risk report to decide how to proceed

## Frequently Asked Questions

**How does the Template Injection Sanitizer protect my AI agents?**
It identifies and removes malicious template syntax like Jinja2 or Python f-strings from user inputs before they can be executed by your prompt engine.

**Can I use Template Injection Sanitizer to clean large datasets?**
Yes, you can use the batch processing capability to clean entire collections of strings at once, making it efficient for large-scale data prep.

**Does Template Injection Sanitizer work with LangChain?**
Yes, it is specifically designed to catch the types of injection attacks that target frameworks like LangChain and CrewAI.

**What happens if a user's legitimate text looks like a template?**
The tool identifies the pattern and provides a cleaned version, ensuring the actual template logic remains uncompromised while preserving the user's intent.

**How do I know if an input is a serious attack or just a mistake?**
You can use the risk profiling tool to evaluate the severity of an attempt based on how many and what kind of malicious patterns are present.

**What kind of injection attacks does this tool prevent?**
It prevents template-based prompt injection where attackers use Jinja2 or Python f-string syntax to manipulate LLM prompts or access system variables.

**How do I check a single user input?**
You can use the `scan_input_security` tool to analyze a single string and receive a sanitized version along with a list of detected patterns.

**Can I process multiple inputs at once?**
Yes, the `batch_sanitize_inputs` tool is designed to process a collection of strings efficiently in a single operation.