  This paper makes three contributions. First, it presents a generalizable,
novel framework dubbed \textit{toxicity rabbit hole} that iteratively elicits
toxic content from a wide suite of large language models. Spanning a set of
1,266 identity groups, we first conduct a bias audit of \texttt{PaLM 2}
guardrails presenting key insights. Next, we report generalizability across
several other models. Through the elicited toxic content, we present a broad
analysis with a key emphasis on racism, antisemitism, misogyny, Islamophobia,
homophobia, and transphobia. Finally, driven by concrete examples, we discuss
potential ramifications.
