Increased LLM Vulnerabilities from Fine-tuning and Quantization: Problem Formulation and Experiments | HackerNoon
Briefly

from Hackernoon3 months ago
In our study, we focus on the interplay between fine-tuning, quantization, and the implementation of guardrails in understanding and mitigating LLM vulnerabilities to jailbreaking attacks.
Hackernoonhttps://hackernoon.com/increased-llm-vulnerabilities-from-fine-tuning-and-quantization-problem-formulation-and-experiments
The experiment utilizes the TAP algorithm to facilitate automating the generation of prompts, which can exploit vulnerabilities in large language models, thereby raising significant security concerns.
Hackernoonhttps://hackernoon.com/increased-llm-vulnerabilities-from-fine-tuning-and-quantization-problem-formulation-and-experiments
Successive iterations of our attack prompt testing revealed critical insights into how guardrails can effectively limit the exploitative capacity of valiant prompt innovations against LLMs.
Hackernoonhttps://hackernoon.com/increased-llm-vulnerabilities-from-fine-tuning-and-quantization-problem-formulation-and-experiments
The comprehensive results gathered from our experimentation indicate the necessity of continual adaptation and improvement of defensive strategies against the evolving nature of jailbreaking techniques.
Hackernoonhttps://hackernoon.com/increased-llm-vulnerabilities-from-fine-tuning-and-quantization-problem-formulation-and-experiments
Read at Hackernoon
#llm-security #jailbreaking #fine-tuning #quantization #guardrails
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