This article focuses on evaluating the stability of attacks and effectiveness of countermeasures in the context of Large Language Models (LLMs). Using a step size of 0.00001 for attack convergence, the authors experiment with unconstrained attacks and analyze safety alignment through varying signal-to-noise ratios (SNRs). They employ a baseline of random perturbations to characterize model robustness. Countermeasures are then assessed under different conditions, revealing insights into the pitfalls and potential measures for enhanced safety of LLM responses.
We use a step size of α = 0.00001, empirically found to lead to stable attack convergence, applying random perturbations to evaluate robustness against safety alignment.
In our experiments, we utilized four different SNR values for countermeasures, dynamically adjusting settings to assess the impact on LLM safety alignment.
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