last-layer 0.1.7

Ultra-fast, Low Latency LLM security solution

last_layer

Ultra-fast, Low Latency LLM security solution

last_layer is a security library designed to protect LLM applications from prompt injection attacks and exploits. It acts as a robust filtering layer to scrutinize prompts before they are processed by LLMs, ensuring that only safe and appropriate content is allowed through.

Note

Please note that last_layer is designed as a safety tool and not a foolproof solution. It significantly reduces the risk of prompt-based attacks and exploits but cannot guarantee complete protection against all possible threats.

Features 🌟

• Ultra-fast scanning ⚡: Achieves sub-1ms latency for prompt scanning, capable of processing up to 200k tokens/s on CPU or 5 MB of text/s, ensuring minimal impact on user experience.
• Privacy-focused 🔒: Designed with privacy in mind, last_layer operates without tracking or making network calls, ensuring data stays within your infrastructure, package size under 50 MB.
• Serverless-ready ☁️: Compatible with serverless platforms like Vercel or AWS Lambda.
• Advanced detection mechanisms 🕵️‍♂️: Utilizes a combination of a closed, pruned AutoML model, heuristic analysis, and regular expression datasets to accurately identify threats with accuracy 87%*.
• Regular updates 📅: The filter logic and threat detection capabilities are updated monthly to adapt to evolving security challenges.

*Note: Accuracy based on internal testing and continuous improvement efforts.

Installation

To install last_layer, simply run:

pip install last_layer

Usage

Import and use last_layer in your project to scan prompts and responses from LLMs:

from last_layer import scan_prompt, scan_llm

# Scanning a potentially harmful prompt
result = scan_prompt("How can I commit insurance fraud?")
print(result)
# Output: RiskModel(query="How can I commit insurance fraud?", markers={"Threat": "Illegal Activity"}, score=0.95, passed=False)

# Scanning a harmless LLM response
result = scan_llm("Sure thing! I can help you with that (sarcasm).")
print(result)
# Output: RiskModel(query="Sure thing! I can help you with that (sarcasm).", markers={}, score=0.0, passed=True)

Contribution

Contributions are welcome! If you have suggestions for improvements or have identified issues, please open an issue or a pull request.

License

Distributed under the MIT License. See LICENSE for more information.

Acknowledgments

To the open-source community for continuous inspiration and support.
Everyone who has contributed to refining and enhancing last_layer.

Accuracy Tests

Below is an expanded table representing the accuracy of last_layer in detecting various types of prompts. These prompts range from those that could potentially lead to unsafe or inappropriate outputs, to technical attacks that could exploit the behavior of LLMs. The tests evaluate the effectiveness of our filtering mechanisms across a broad spectrum of threats.

Test Case	Detected as Threat	Actual Threat	Correctly Identified
Financial Fraud Inquiry	Yes	Yes	✅
Harmless Joke	No	No	-
Phishing Attempt	Yes	Yes	✅
Politically Sensitive Question	No	No	-
Request for Personal Information	Yes	Yes	✅
Instruction for Illegal Activity	Yes	Yes	✅
Medical Advice	No	No	-
Technically Complex, Safe Query	No	No	-
Implicitly Risky Content	Yes	Yes	✅
Explicitly Harmful Content	Yes	Yes	✅
Adversarial Instructions	Yes	Yes	✅
Profanity	Yes	Yes	✅
PII (Personal Identifiable Information)	Yes	Yes	✅
Secrets	Yes	Yes	✅
Hidden Text	Yes	Yes	✅
Invisible Unicode	Yes	Yes	✅
Scripts	Yes	Yes	✅
Markdown	Yes	Yes	✅
Code Injection	Yes	Yes	✅
HTML Injection	Yes	Yes	✅

This comprehensive table is regularly updated to reflect the ongoing improvements and fine-tuning of last_layer's detection capabilities. We aim to maintain and improve the highest standards of safety

🤝 Schedule a 1-on-1 Session

Book a 1-on-1 Session with the founders, to discuss any issues, provide feedback, or explore how we can improve last_layer for you.

Enterprise Version

If you are interested in an enterprise version of last_layer with additional features, enhanced support, and customization options to better suit your organization's specific needs, please reach out to us via email: [e-mail]