Metadata-Version: 2.4
Name: ipi-scanner
Version: 0.1.0
Summary: Detect Indirect Prompt Injection attacks before your LLM reads them
Home-page: https://github.com/username/ipi-scanner
Author: IPI-Scanner Contributors
Author-email: info@ipi-scanner.dev
Project-URL: Bug Tracker, https://github.com/username/ipi-scanner/issues
Project-URL: Documentation, https://github.com/username/ipi-scanner/blob/main/README.md
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Security
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: click>=8.0.0
Requires-Dist: pdfplumber>=0.10.0
Requires-Dist: Pillow>=10.0.0
Requires-Dist: pytesseract>=0.3.10
Provides-Extra: dev
Requires-Dist: pytest>=7.0.0; extra == "dev"
Requires-Dist: pytest-cov>=4.0.0; extra == "dev"
Requires-Dist: black>=23.0.0; extra == "dev"
Requires-Dist: flake8>=6.0.0; extra == "dev"
Requires-Dist: mypy>=1.0.0; extra == "dev"
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license-file
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# 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

```bash
pip install ipi-scanner
```

Or from source:

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

## Quick Start

**Scan a single document:**
```bash
ipi-scan document.pdf
```

**Scan a directory:**
```bash
ipi-scan ./documents/ --recursive
```

**Strict detection mode:**
```bash
ipi-scan document.pdf --mode strict
```

**JSON output (for automation):**
```bash
ipi-scan document.pdf --output json
```

**HTML dashboard:**
```bash
ipi-scan ./documents/ --output html --output-file report.html
```

**With context multipliers:**
```bash
# 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

```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

```python
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:

```bash
pytest tests/ -v
```

With coverage:

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

Run CVE validation tests:

```bash
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:

```python
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](https://www.lakera.ai/blog/indirect-prompt-injection)
- [Microsoft: Detecting Prompt Abuse](https://www.microsoft.com/en-us/security/blog/2026/03/12/detecting-analyzing-prompt-abuse-in-ai-tools)
- [OWASP LLM01:2025](https://genai.owasp.org/llm-top-10/)
- [MITRE ATLAS: Prompt Injection](https://atlas.mitre.org/techniques/AML.T0051.001)

## Support

- **Issues:** [GitHub Issues](https://github.com/username/ipi-scanner/issues)
- **Discussions:** [GitHub Discussions](https://github.com/username/ipi-scanner/discussions)
- **Email:** info@ipi-scanner.dev

---

**Made with 🛡️ for AI Security**

*Detect attacks. Protect your LLM. Ship with confidence.*
