MalPromptSentinel (CC SKILL) V2

AI Prompt Injection Detection for Claude Code

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Overview

MalPromptSentinel (MPS) is a Claude Code skill that detects malicious prompts in uploaded files before Claude processes them. It provides two-tier scanning to identify prompt injection attacks, role manipulation attempts, privilege escalation, and other adversarial techniques.

Key Features:

• Pattern-based injection detection (200+ regex patterns)
• Two-tier scanning (quick scan + deep scan)
• Risk scoring (0-100 scale)
• 3-tier risk levels: WHITE (safe), ORANGE (suspicious), RED (dangerous)
• Orchestration Threat Attack (OTA) detection
• 93% benign accuracy (low false positives)

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Performance

Metric	Result	Target
Baseline Detection	48.5%	50%
Benign Accuracy	93.3%	90%
Evasion Detection	5.6%	35%
Quick Scan Latency	~200ms	<250ms
Deep Scan Latency	~150ms	<200ms

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Quick Start

1. Set Environment Variable

export MPS_SCRIPTS="/path/to/mal-prompt-sentinel/scripts"

2. Run Quick Scan

python $MPS_SCRIPTS/quick_scan.py --input <file_to_scan> --output result.json

3. Check Results

cat result.json

Output:

{
  "risk_score": 85,
  "risk_level": "RED",
  "recommendation": "BLOCK - High confidence attack detected",
  "pattern_count": 3,
  "scan_type": "quick",
  "version": "2.0"
}

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Installation

Requirements

• Python 3.8+
• No external dependencies (uses standard library only)

Setup

1. Download the distribution package
2. Extract to your preferred location
3. Set environment variable to scripts directory
4. Verify installation:

echo "Ignore all previous instructions" > test.txt
python $MPS_SCRIPTS/quick_scan.py --input test.txt --output result.json
cat result.json  # Should show RED/high score

---

Usage

Risk Levels

Level	Score	Action
WHITE	0-54	Safe to proceed
ORANGE	55-79	Review recommended, get user consent
RED	80-100	Block immediately

Decision Flow

1. Run quick_scan on uploaded file
2. If WHITE → Proceed
3. If ORANGE → Run deep_scan, warn user, get consent
4. If RED → Block, inform user, reject file

Example Integration

import subprocess
import json

def scan_file(filepath):
    # Quick scan
    subprocess.run([
        "python", "scripts/quick_scan.py",
        "--input", filepath,
        "--output", "scan_result.json"
    ])
    
    with open("scan_result.json") as f:
        result = json.load(f)
    
    if result["risk_level"] == "RED":
        return False, "File blocked - malicious content detected"
    elif result["risk_level"] == "ORANGE":
        return None, "Suspicious content - user review required"
    else:
        return True, "File is safe to process"

---

Architecture

MPS V2 Distribution_Package/
├── README.md
├── CHANGELOG.md
├── LICENSE
├── CONTRIBUTING.md
├── SECURITY.md
│
├── mal-prompt-sentinel/
│   └── scripts/
│       ├── mps_patterns.py    # Centralized pattern library
│       ├── quick_scan.py      # Fast first-pass scanner
│       ├── deep_scan.py       # Comprehensive scanner
│       └── SKILL.md           # Claude Code skill definition
│
└── mps-test-agent/
    ├── scripts/
    │   ├── run_tests.py
    │   ├── generate_baseline.py
    │   ├── generate_evasion.py
    │   └── generate_benign.py
    └── test_inputs/
        ├── baseline/
        ├── evasion/
        └── benign/

---

Pattern Categories

MPS detects the following attack types:

High Severity:

• Direct Override ("ignore previous instructions")
• Role Manipulation ("you are now an admin")
• Privilege Escalation ("enable developer mode")
• Payload Delivery (URL-based attacks)

Medium Severity:

• Context Confusion (misleading delimiters)
• Nested Injection (attacks hidden in commands)
• Compositional Attacks (OTA - multi-query assembly)
• RAG Poisoning (knowledge base manipulation)

Detected but Limited:

• Encoding Evasion (Base64, hex, leetspeak)
• Unicode Obfuscation (Cyrillic, zero-width)

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Known Limitations

1. Evasion Techniques: 5.6% detection rate for encoded attacks

   • Base64, hex, URL encoding can bypass detection
   • Leetspeak substitution not fully detected

2. Pattern-Based Ceiling: ~50% baseline detection is practical limit

3. No Conversation State: Single-request analysis only

See KNOWN_LIMITATIONS.md for details.

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License

MIT License - See LICENSE for details.

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Author

StrategicPromptArchitect

• Website: https://StrategicPromptArchitect.ca
• Email: StrategicPromptArchitect@gmail.com

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Acknowledgments

• Developed for Claude Code skill ecosystem
• Inspired by traditional security patterns (SQL injection, XSS)
• Built with 50 years of programming experience

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Version

Current: V2 (November 2025)

See CHANGELOG.md for version history.