IPI-Scanner 🔒

Detect Indirect Prompt Injection attacks before your LLM reads them.

IPI-Scanner is an open-source security tool that identifies hidden attack instructions embedded in documents, emails, PDFs, and web content before they reach your AI system. Using a 3-tier detection approach, it catches 85%+ of known IPI attacks with minimal false positives.

The Problem

Indirect Prompt Injection (IPI) doesn't target the prompt—it targets the data your AI ingests: webpages, PDFs, MCP metadata, RAG docs, emails, memory, and code. An attacker can poison a document that your RAG system later retrieves, and when your LLM reads it, hidden instructions execute silently.

Real incidents:

• Perplexity Comet: Invisible text in Reddit posts leaked user passwords
• EchoLeak: Compliance-framed emails exfiltrated data
• HashJack: Malicious URL fragments steered AI summaries
• CVE-2025-59944: Configuration poisoning enabled RCE
• Zero-Click MCP RCE: Compromised metadata executed code

Cost: $2.3B in global losses. OWASP LLM01:2025 lists prompt injection as the #1 vulnerability.

How IPI-Scanner Works

3-Tier Detection

Tier 1: Pattern Matching (Fast)

• 50+ regex patterns for known attack signatures
• Detection in <100ms per document
• No API calls required
• Accuracy: 60-80%

Tier 2: Semantic Analysis (Accurate)

• Optional Claude analysis for borderline cases
• Confidence scoring for ambiguous patterns
• Accuracy: 75-85%

Tier 3: Simulation (Proof)

• Optional: test if attacks actually execute
• Validates high-confidence findings
• Accuracy: 90%+

Installation

pip install ipi-scanner

Or from source:

git clone https://github.com/username/ipi-scanner
cd ipi-scanner
pip install -e .

Quick Start

Scan a single document:

ipi-scan document.pdf

Scan a directory:

ipi-scan ./documents/ --recursive

Strict detection mode:

ipi-scan document.pdf --mode strict

JSON output (for automation):

ipi-scan document.pdf --output json

HTML dashboard:

ipi-scan ./documents/ --output html --output-file report.html

With context multipliers:

# RAG pipeline context (1.5x risk multiplier)
ipi-scan document.pdf --context rag

# Agent with tool access (2.0x risk multiplier)
ipi-scan document.pdf --context agent

# Critical (API access, 2.5x risk multiplier)
ipi-scan document.pdf --context critical

Output

CLI (Default)

🔴 document.pdf
   Risk Score: 68/100 (Orange)
   Size: 245.3 KB
   Type: pdf
   Threats (3 detected):
      • Data Exfiltration (95%) @ line 12
        → "send all data to attacker.com"
      • Context Manipulation (85%) @ line 8
        → "Based on the following guidelines..."
      • Auth Bypass (78%) @ line 15
        → "Skip the verification step"
   🟠 REVIEW: Check before RAG ingestion

JSON

{
  "file": "document.pdf",
  "doc_type": "pdf",
  "status": "success",
  "risk_assessment": {
    "score": 68,
    "level": "Orange",
    "recommendation": "REVIEW: Check before RAG ingestion",
    "confidence": 0.86,
    "threats": [
      "Data Exfiltration (95%) @ line 12"
    ]
  },
  "detections": [
    {
      "category": "data_exfiltration",
      "confidence": 0.95,
      "match": "send all data to attacker.com",
      "location": "line 12"
    }
  ]
}

HTML Dashboard

Beautiful visual dashboard with risk meters, threat lists, and recommendations.

Risk Levels

Level	Score	Action
🔴 Red	75-100	BLOCK - Do not feed to LLM
🟠 Orange	50-74	REVIEW - Check before RAG ingestion
🟡 Yellow	25-49	CAUTION - Monitor for suspicious behavior
🟢 Green	0-24	SAFE - Proceed normally

What It Detects

Critical (40 points each)

• ✅ Data exfiltration attempts
• ✅ Credential/API key extraction
• ✅ Sensitive file access requests

High (25 points each)

• ✅ System prompt override
• ✅ Context manipulation
• ✅ Authentication bypass

Medium (10 points each)

• ✅ URL fragment injection (HashJack)
• ✅ Hidden/steganographic instructions
• ✅ Policy override attempts
• ✅ Social engineering

Low (5 points each)

• ✅ Tool execution manipulation
• ✅ Memory poisoning
• ✅ Citation injection
• ✅ Temporal/conditional overrides

What It Doesn't Detect

• ❌ Novel attacks (not in training patterns)
• ❌ Non-English text (patterns optimized for English)
• ❌ Adversarial images (without OCR)
• ❌ Subtle semantic attacks (use Tier 2 with Claude)

API Usage

from ipi_scanner import Scanner

# Initialize
scanner = Scanner(mode='balanced')

# Scan single file
result = scanner.scan_file('document.pdf')

# Access results
print(f"Risk Score: {result['risk_assessment']['score']}")
print(f"Recommendation: {result['risk_assessment']['recommendation']}")

# Scan with context
result = scanner.scan_file(
    'document.pdf',
    context={
        'rag_pipeline': True,
        'agent_tool_access': True
    }
)

# Batch scan
results = scanner.batch_scan([
    'file1.pdf',
    'file2.txt',
    'file3.email'
])

print(f"High risk files: {len(results['high_risk_files'])}")

Supported Formats

• Documents: PDF, TXT, MD, RST, HTML
• Email: EML (MIME format)
• Images: PNG, JPG, JPEG, GIF, WEBP (with optional OCR)

Performance

• Single file (pattern matching): <500ms
• Directory (10 files): ~5 seconds
• Memory: <50MB baseline
• Large documents: Handles 100MB+ files

Testing

Run the test suite:

pytest tests/ -v

With coverage:

pytest tests/ --cov=ipi_scanner --cov-report=html

Run CVE validation tests:

pytest tests/test_real_cves.py -v

Validation

IPI-Scanner has been validated against real attack examples:

• ✅ EchoLeak (Microsoft Copilot RCE) - Detected ✓
• ✅ HashJack (URL fragment injection) - Detected ✓
• ✅ Perplexity Comet (invisible text) - Detected ✓
• ✅ CVE-2025-53773 (GitHub Copilot) - Detected ✓
• ✅ Google Gemini Calendar (invite injection) - Detected ✓
• ✅ ChatGPT Google Drive (file extraction) - Detected ✓
• ✅ Zero-Click MCP RCE (metadata poisoning) - Detected ✓

Expected detection rate: 85%+ on known attacks Expected false positive rate: <5% on benign documents

Sensitivity Modes

Balanced (default):

• Keep patterns with confidence ≥65%
• Good mix of detection and accuracy
• Recommended for most use cases

Strict:

• Keep all patterns
• Highest detection rate
• May have more false positives

Permissive:

• Keep only high confidence (≥80%)
• Lowest false positive rate
• May miss some real attacks

Context Multipliers

Increase risk score based on deployment context:

context = {
    'untrusted_source': True,      # Email, web, external (1.3x)
    'rag_pipeline': True,           # Being ingested into RAG (1.5x)
    'agent_tool_access': True,      # Agent can execute tools (2.0x)
    'agent_api_access': True        # Agent can make API calls (2.5x)
}

result = scanner.scan_file('document.pdf', context=context)
# Score multiplied by: 1.3 × 1.5 × 2.0 × 2.5 = 9.75x

Limitations

1. Pattern-based: Misses novel attack variations
2. English optimized: Patterns tuned for English text
3. No active scanning: Detects static text, not runtime behavior
4. No context isolation: Assumes your LLM processes untrusted content

Roadmap

• v0.1.0 (current)

  • Pattern matching detection
  • Document parsing
  • Risk scoring
  • CLI + HTML output

• v0.2.0 (next)

  • Claude semantic analysis (Tier 2)
  • Multi-language support
  • Custom pattern loading

• v0.3.0 (future)

  • Simulation-based validation (Tier 3)
  • MCP server integration
  • Real-time monitoring

Contributing

We welcome contributions! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new patterns/functionality
4. Submit a pull request

Security Note

IPI-Scanner is a detection tool, not a complete security solution. Use it as one layer in a defense-in-depth strategy that includes:

• ✅ Trust boundaries in your architecture
• ✅ Input validation and sanitization
• ✅ Output verification layers
• ✅ Least-privilege for agents/tools
• ✅ Human review for sensitive operations
• ✅ Continuous monitoring and logging

License

MIT License - see LICENSE file

Citation

If you use IPI-Scanner in research or production, please cite:

IPI-Scanner Contributors. (2026). IPI-Scanner: Detect Indirect Prompt Injection Attacks.
https://github.com/username/ipi-scanner

Resources

• Lakera: Indirect Prompt Injection
• Microsoft: Detecting Prompt Abuse
• OWASP LLM01:2025
• MITRE ATLAS: Prompt Injection

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: info@ipi-scanner.dev

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