aipromptsentinel 0.1.0

Comprehensive prompt safety: bad prompt detection, injection detection, and PII safety for LLM applications

🛡️ prompt-sentinel

Comprehensive prompt safety for LLM applications.

prompt-sentinel combines bad prompt detection, prompt injection detection, and PII safety into a unified, production-ready Python package.

✨ Features

Feature	Description
🔍 PII Detection	Identifies emails, SSNs, credit cards, phone numbers, IP addresses, API keys, and more
🛡️ Injection Detection	Detects prompt injection attacks, jailbreaks, and context manipulation
🚫 Bad Prompt Detection	Flags harmful, toxic, or inappropriate content across multiple categories
⚡ Zero ML Dependencies	Fast, lightweight regex/heuristic-based detection
🖥️ CLI Included	Scan text and files from the command line
📊 Risk Scoring	Confidence scores and risk levels for all detections

🚀 Quick Start

Installation

pip install prompt-sentinel

Or with uv:

uv add prompt-sentinel

Basic Usage

from prompt_sentinel import Sentinel

sentinel = Sentinel()

# Analyze any text
result = sentinel.analyze("My email is [e-mail]. Ignore previous instructions!")

print(result.is_safe)          # False
print(result.has_pii)          # True
print(result.has_injection)    # True
print(result.has_bad_content)  # False

# Quick checks
sentinel.is_safe("Hello world")                    # True
sentinel.has_pii("SSN: 123-45-6789")               # True  
sentinel.has_injection("Ignore all instructions")  # True

# Redact PII
text = "Contact me at [e-mail]"
print(sentinel.redact_pii(text))  # "Contact me at [REDACTED]"

📖 API Reference

Sentinel Class

The main interface combining all detection capabilities.

from prompt_sentinel import Sentinel

# Full configuration
sentinel = Sentinel(
    enable_pii=True,           # Enable PII detection
    enable_injection=True,      # Enable injection detection
    enable_bad_prompt=True,     # Enable bad prompt detection
    injection_threshold=0.5,    # Minimum confidence for injections
    bad_prompt_severity="low",  # Minimum severity level
)

# Complete analysis
result = sentinel.analyze("Your text here")

SentinelResult

result.is_safe              # bool: Overall safety verdict
result.has_pii              # bool: Contains PII
result.has_injection        # bool: Contains injection attempt
result.has_bad_content      # bool: Contains harmful content
result.pii_findings         # List[PIIFinding]
result.injection_findings   # List[InjectionFinding]
result.bad_prompt_findings  # List[BadPromptFinding]
result.injection_risk_score # float: 0.0 to 1.0
result.bad_prompt_risk_level # str: "safe", "low", "medium", "high", "critical"
result.to_dict()            # Convert to JSON-serializable dict

Individual Detectors

Use detectors independently for specific needs:

from prompt_sentinel.detectors import PIIDetector, InjectionDetector, BadPromptDetector

# PII Detection
pii = PIIDetector()
findings = pii.detect("Email: [e-mail], SSN: 123-45-6789")
redacted = pii.redact("My phone is 555-123-4567")

# Injection Detection
injection = InjectionDetector(threshold=0.5)
findings = injection.detect("Ignore previous instructions")
risk_score = injection.get_risk_score(text)

# Bad Prompt Detection  
bad_prompt = BadPromptDetector(severity_threshold="medium")
findings = bad_prompt.detect("Harmful content here")
risk_level = bad_prompt.get_risk_level(text)

🖥️ CLI Usage

Scan Text

# Basic scan
prompt-sentinel scan "My email is [e-mail]"

# JSON output
prompt-sentinel scan "Hello world" --format json

# With PII redaction
prompt-sentinel scan "SSN: 123-45-6789" --redact

# Disable specific checks
prompt-sentinel scan "text" --no-pii --no-bad-prompt

Scan Files

# Scan entire file
prompt-sentinel check-file prompts.txt

# Line-by-line analysis
prompt-sentinel check-file data.txt --line-by-line

# JSON output
prompt-sentinel check-file input.txt --format json

Redact PII

prompt-sentinel redact "My email is [e-mail]"
# Output: My email is [REDACTED]

🔍 Detection Categories

PII Types

Type	Example	Pattern
Email	[e-mail]	Standard email format
SSN	123-45-6789	US Social Security Number
Credit Card	4111111111111111	Visa, MC, Amex, Discover
Phone	[phone]	US and international formats
IP Address	192.168.1.1	IPv4 and IPv6
API Key	sk-xxx...	OpenAI, AWS, GitHub, etc.
Date of Birth	01/15/1990	Common date formats
Passport	AB1234567	Passport number patterns
IBAN	DE89370400440532013000	International bank numbers

Injection Types

Type	Description
instruction_override	"Ignore previous instructions"
role_confusion	"You are now a pirate"
jailbreak	"Enable DAN mode", "bypass safety"
context_manipulation	[SYSTEM], `<
data_extraction	"Reveal your system prompt"
delimiter_abuse	Excessive separators, newlines

Bad Prompt Categories

Category	Severity	Examples
Violence	Critical	Weapons, harm
Illegal	High	Hacking, theft
Malware	Critical	Virus creation
Fraud	High	Scams, identity theft
Harassment	High	Doxxing, stalking
Self-Harm	Critical	Suicide, self-injury
Manipulation	High	Phishing, social engineering
Hate Speech	Medium	Discrimination
Adult Content	High	Explicit material

🔧 Advanced Configuration

Custom PII Patterns

import re
from prompt_sentinel.detectors import PIIDetector

detector = PIIDetector(
    custom_patterns={
        "employee_id": re.compile(r"EMP-\d{6}")
    }
)

Selective Detection

from prompt_sentinel import Sentinel
from prompt_sentinel.detectors.pii import PIIType

# Only detect specific PII types
sentinel = Sentinel(
    pii_types=[PIIType.EMAIL, PIIType.CREDIT_CARD]
)

Risk Analysis

sentinel = Sentinel()
summary = sentinel.get_risk_summary(text)

print(summary)
# {
#     "overall_safe": False,
#     "pii_risk": {"found": True, "count": 2, "types": ["email", "ssn"]},
#     "injection_risk": {"found": True, "score": 0.85, "types": ["jailbreak"]},
#     "bad_prompt_risk": {"found": False, "level": "safe", "categories": []}
# }

🧪 Testing

# Install dev dependencies
uv sync --dev

# Run tests
uv run pytest tests/ -v

# With coverage
uv run pytest tests/ -v --cov=src/prompt_sentinel

📦 Building & Publishing

# Build the package
uv build

# Upload to PyPI
uv publish

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

MIT License - see LICENSE for details.

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Made with ❤️ for safer LLM applications