  Backdoor attacks are an insidious security threat against machine learning
models. Adversaries can manipulate the predictions of compromised models by
inserting triggers into the training phase. Various backdoor attacks have been
devised which can achieve nearly perfect attack success without affecting model
predictions for clean inputs. Means of mitigating such vulnerabilities are
underdeveloped, especially in natural language processing. To fill this gap, we
introduce IMBERT, which uses either gradients or self-attention scores derived
from victim models to self-defend against backdoor attacks at inference time.
Our empirical studies demonstrate that IMBERT can effectively identify up to
98.5% of inserted triggers. Thus, it significantly reduces the attack success
rate while attaining competitive accuracy on the clean dataset across
widespread insertion-based attacks compared to two baselines. Finally, we show
that our approach is model-agnostic, and can be easily ported to several
pre-trained transformer models.
