  We investigate a new threat to neural sequence-to-sequence (seq2seq) models:
training-time attacks that cause models to "spin" their output and support a
certain sentiment when the input contains adversary-chosen trigger words. For
example, a summarization model will output positive summaries of any text that
mentions the name of some individual or organization. We introduce the concept
of a "meta-backdoor" to explain model-spinning attacks. These attacks produce
models whose output is valid and preserves context, yet also satisfies a
meta-task chosen by the adversary (e.g., positive sentiment). Previously
studied backdoors in language models simply flip sentiment labels or replace
words without regard to context. Their outputs are incorrect on inputs with the
trigger. Meta-backdoors, on the other hand, are the first class of backdoors
that can be deployed against seq2seq models to (a) introduce adversary-chosen
spin into the output, while (b) maintaining standard accuracy metrics.
  To demonstrate feasibility of model spinning, we develop a new backdooring
technique. It stacks the adversarial meta-task (e.g., sentiment analysis) onto
a seq2seq model, backpropagates the desired meta-task output (e.g., positive
sentiment) to points in the word-embedding space we call "pseudo-words," and
uses pseudo-words to shift the entire output distribution of the seq2seq model.
Using popular, less popular, and entirely new proper nouns as triggers, we
evaluate this technique on a BART summarization model and show that it
maintains the ROUGE score of the output while significantly changing the
sentiment. We explain why model spinning can be a dangerous technique in
AI-powered disinformation and discuss how to mitigate these attacks.
