After training for 200 epochs, we achieve the attack success rate (ASR) of99.97% and the natural accuracy on clean data (ACC)of93.73%. Blend attack [6]: We first generate a trigger pattern where each pixel value is sampled from auniform distribution in[0,255]asshowninFigure 6(c). Input-aware Attack (IAB) [30]: The dynamic trigger varies across samples as shown in Figure 6(d). We apply two types of target label selection. Clean-labelAttack(CLB)[42]: The trigger is a3 3checkerboard at the four corners of images as shown in Figure 7(b).

A VSR, respectively) has traditionally been conducted independently.

MLaaS are limited by their reliance on training samples or white-box model analysis, highlighting the need for a black-box backdoor purification method.

To further verify this finding, we empirically show that these dormant backdoors can be easily re-activated during inference stage, by manipulating the original trigger with well-designed tiny perturbation using universal adversarial attack.

Deep Neural Networks (DNNs) are shown vulnerable to various security threats.

In the field of natural language processing, the prevalent approach involves fine-tuning pretrained language models (PLMs) using local samples.

Due to its distributed nature, many studies have shown that it is vulnerable to backdoor attacks.

However, Does achieving a low ASR through current safety purification methods truly eliminate learned backdoor features from the pretraining phase? In this paper, we provide an affirmative answer to this question by thoroughly investigating the Post-Purification Robustness of current backdoor purification methods.