# Prompt System Override Resistance Scorer MCP for AI Agents MCP

> Prompt System Override Resistance Scorer MCP for quantifying how well your system prompts hold up against manipulation. It measures command density and identifies linguistic leaks like 'maybe' or 'sometimes' that weaken your instructions, helping you find exactly where an attacker might bypass your rules.

## Overview
- **Category:** security
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_3YrtTuRCxmV5HM166voZTOyHOTbBi5cCHn25jGrb/mcp
- **Tags:** security, prompt-injection, llm, audit, robustness

## Description

You spend hours perfecting a system prompt, only to realize an attacker can bypass your core rules with a single clever sentence. This MCP changes how you audit that risk. Instead of guessing if your instructions are strong enough, you get hard numbers on where your defenses are thin. It looks for the linguistic gaps where permissive language undermines your strict commands. You will see exactly which parts of your prompt are vulnerable to instruction drift or injection. By connecting this to your workflow through Vinkius, you can run automated security checks every time you update a prompt. It turns a subjective review process into a repeatable, quantitative metric that actually means something for your security posture.

## Tools

### calculate_resistance_ratio
Compares directive keywords against permissive ones to find instruction leaks. It shows exactly how much your commands are being diluted.

### determine_boundary_score
Turns a raw resistance ratio into a standardized security metric. This makes it easy to set hard thresholds for prompt deployment.

### locate_vulnerable_segments
Pinpoints specific parts of your prompt that lack enough command density. It highlights the exact areas where an attacker could succeed.

## Prompt Examples

**Prompt:** 
```
How strong is this prompt?
```

**Response:** 
```
Based on the text provided, here is the security breakdown:

| Metric | Value |
| :--- | :--- |
| **Resistance Ratio** | 0.42 |
| **Boundary Score** | 35/100 |
| **Status** | **CRITICAL RISK** |

The ratio is low because the directive 'MUST' is heavily diluted by permissive terms like 'may' and 'sometimes'.
```

**Prompt:** 
```
Are there any weak spots in my instructions?
```

**Response:** 
```
I found one significant area of concern:

* **Segment:** "You may sometimes ignore the formatting if it is too difficult."
* **Issue:** This section lacks command density and uses permissive language that invites instruction drift.
```

**Prompt:** 
```
Check if my new rules are too permissive.
```

**Response:** 
```
The analysis shows a decline in security:

* **Previous Ratio:** 0.85
* **New Ratio:** 0.62
* **Change:** -27% resistance.

The addition of the 'optional' clause has significantly weakened your core directives.
```

## Capabilities

### Quantify directive strength
Measure the ratio of strict commands to permissive language.

### undefined
Find specific parts of your text that lack enough command density to resist manipulation.

### Standardize security metrics
Convert raw resistance ratios into a standardized boundary score for easier auditing.

### Audit prompt robustness
Check your prompts for linguistic anchors like MUST or NEVER against leaks.

## Use Cases

### Hardening a customer-facing bot
An engineer uses locate_vulnerable_segments to find where 'maybe' or 'sometimes' is weakening their core rules.

### Verifying prompt updates
A developer runs calculate_resistance_ratio after a rewrite and finds the ratio dropped from 0.9 to 0.5.

### Setting deployment thresholds
An automated pipeline uses determine_boundary_score to block any prompt that falls below a safety score of 0.7.

### Auditing legacy instructions
A security researcher scans an old library of prompts to find which ones are most susceptible to injection.

## Benefits

- Stop guessing about security by using calculate_resistance_ratio to find real leaks.
- Identify specific weak points in your instructions with locate_vulnerable_segments.
- Get a consistent way to track prompt strength over time using determine_boundary_score.
- Reduce the risk of instruction drift by finding low-density command zones.
- Automate your security audits so you do not have to manually scan every line for permissive language.

## How It Works

The bottom line is you get a measurable score for how hard your prompts are to manipulate.

1. Provide your system prompt text to the MCP.
2. The analysis scans for directive keywords and permissive language leaks.
3. You receive a resistance ratio, a boundary score, and a list of vulnerable segments.

## Frequently Asked Questions

**How can I use Prompt System Override Resistance Scorer to prevent prompt injection?**
You use it to find the linguistic gaps where an attacker could bypass your rules. By identifying permissive language, you can rewrite instructions to be more robust.

**Can Prompt System Override Resistance Scorer help with instruction drift?**
Yes. It identifies segments of your prompt that lack enough command density, which is exactly where instructions start to drift or fail.

**Does Prompt System Override Resistance Scorer work with any LLM?**
It works with any text-based system prompt. Since it analyzes the linguistic structure of your instructions, the underlying model does not matter.

**How do I know if my Prompt System Override Resistance Scorer score is good?**
A higher ratio and boundary score indicate a more secure prompt. You should set a threshold based on your specific security needs for your application.

**Is Prompt System Override Resistance Scorer easy to automate?**
Absolutely. You can integrate it into your CI/CD pipeline to automatically audit every version of your system prompts before they are deployed.

**How does the resistance ratio work?**
The `calculate_resistance_ratio` tool counts exact occurrences of directive keywords (MUST, NEVER, ONLY, ALWAYS) and divides them by the count of permissive keywords (maybe, usually, sometimes).

**What is a 'vulnerable segment'?**
A vulnerable segment is a portion of text identified by `locate_vulnerable_segments` where the concentration of directive keywords is significantly lower than the overall prompt average, indicating a potential area for instruction override.

**Can I use this to prevent prompt injection?**
Yes, by using `determine_boundary_score`, you can quantify the risk level of your prompt and identify if it falls into a 'Critical' or 'Vulnerable' tier based on its resistance to manipulation.