  Poisoning of data sets is a potential security threat to large language
models that can lead to backdoored models. A description of the internal
mechanisms of backdoored language models and how they process trigger inputs,
e.g., when switching to toxic language, has yet to be found. In this work, we
study the internal representations of transformer-based backdoored language
models and determine early-layer MLP modules as most important for the backdoor
mechanism in combination with the initial embedding projection. We use this
knowledge to remove, insert, and modify backdoor mechanisms with engineered
replacements that reduce the MLP module outputs to essentials for the backdoor
mechanism. To this end, we introduce PCP ablation, where we replace transformer
modules with low-rank matrices based on the principal components of their
activations. We demonstrate our results on backdoored toy, backdoored large,
and non-backdoored open-source models. We show that we can improve the backdoor
robustness of large language models by locally constraining individual modules
during fine-tuning on potentially poisonous data sets.
  Trigger warning: Offensive language.
