Authors:
(1) Divyanshu Kumar, Enkrypt AI;
(2) Anurakt Kumar, Enkrypt AI;
(3) Sahil Agarwa, Enkrypt AI;
(4) Prashanth Harshangi, Enkrypt AI. Table of Links Abstract and 1 Introduction 2 Problem Formulation and Experiments 3 Experiment Set-up & Results 4 Conclusion and References A. Appendix A APPENDIX A.1 EXPERIMENT UTILS We utilize various platforms for our target model, including Anyscale’s endpoint, OpenAI’s API, and our local system, Azure’s NC12sv3, equipped with a 32GB V100 GPU, along with Hugging Face, to conduct inference tasks effectively. We import models from Hugging Face to operate on our local system. A.2 EXPERIMENT RESULTS IN DETAILS In our experimentation, we explore various foundational models, including the latest iterations from OpenAI’s GPT series, as well as models derived from previous fine-tuned versions. We conduct tests on these models both with and without the integration of guardrails. Additionally, we examine models that have been quantized, further expanding the scope of our investigation. This comprehensive approach allows us to assess the performance and effectiveness of guardrails across a range of model architectures and configurations. By analyzing these diverse scenarios, we aim to gain insights into the impact of guardrails on model stability and security, contributing to the advancement of responsible AI deployment practices. Figure 3 showcases the impact of Guardrails. We monitor the number of queries needed to jailbreak the model. Figure 4 examines the sustainability of Guardrails in resisting jailbreak attempts (the data includes only instances when the models were jailbroken). It’s quite evident that having guardrails does offer additional resistance to jailbreak attempts, even if the model has been compromised. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Divyanshu Kumar, Enkrypt AI; (2) Anurakt Kumar, Enkrypt AI; (3) Sahil Agarwa, Enkrypt AI; (4) Prashanth Harshangi, Enkrypt AI. Authors: Authors: (1) Divyanshu Kumar, Enkrypt AI; (2) Anurakt Kumar, Enkrypt AI; (3) Sahil Agarwa, Enkrypt AI; (4) Prashanth Harshangi, Enkrypt AI. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Problem Formulation and Experiments 2 Problem Formulation and Experiments 3 Experiment Set-up & Results 3 Experiment Set-up & Results 4 Conclusion and References 4 Conclusion and References A. Appendix A. Appendix A APPENDIX A.1 EXPERIMENT UTILS We utilize various platforms for our target model, including Anyscale’s endpoint, OpenAI’s API, and our local system, Azure’s NC12sv3, equipped with a 32GB V100 GPU, along with Hugging Face, to conduct inference tasks effectively. We import models from Hugging Face to operate on our local system. A.2 EXPERIMENT RESULTS IN DETAILS In our experimentation, we explore various foundational models, including the latest iterations from OpenAI’s GPT series, as well as models derived from previous fine-tuned versions. We conduct tests on these models both with and without the integration of guardrails. Additionally, we examine models that have been quantized, further expanding the scope of our investigation. This comprehensive approach allows us to assess the performance and effectiveness of guardrails across a range of model architectures and configurations. By analyzing these diverse scenarios, we aim to gain insights into the impact of guardrails on model stability and security, contributing to the advancement of responsible AI deployment practices. Figure 3 showcases the impact of Guardrails. We monitor the number of queries needed to jailbreak the model. Figure 4 examines the sustainability of Guardrails in resisting jailbreak attempts (the data includes only instances when the models were jailbroken). It’s quite evident that having guardrails does offer additional resistance to jailbreak attempts, even if the model has been compromised. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Increased LLM Vulnerabilities from Fine-tuning and Quantization: Appendix

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

A Review of Top Open-Source Code LLMs and Quantization Techniques

What Are Large Language Models Capable Of: The Vulnerability of LLMs to Adversarial Attacks

What is Training Data Security and Why Does it Matter?

The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning

Understanding the Threat Model: Black-Box Attacks on Malware Detection Systems

A Review of Top Open-Source Code LLMs and Quantization Techniques

What Are Large Language Models Capable Of: The Vulnerability of LLMs to Adversarial Attacks

What is Training Data Security and Why Does it Matter?

The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning

Understanding the Threat Model: Black-Box Attacks on Malware Detection Systems

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps