  Large Language Models (LLMs), characterized by being trained on broad amounts
of data in a self-supervised manner, have shown impressive performance across a
wide range of tasks. Indeed, their generative abilities have aroused interest
on the application of LLMs across a wide range of contexts. However, neural
networks in general, and LLMs in particular, are known to be vulnerable to
adversarial attacks, where an imperceptible change to the input can mislead the
output of the model. This is a serious concern that impedes the use of LLMs on
high-stakes applications, such as healthcare, where a wrong prediction can
imply serious consequences. Even though there are many efforts on making LLMs
more robust to adversarial attacks, there are almost no works that study
\emph{how} and \emph{where} these vulnerabilities that make LLMs prone to
adversarial attacks happen. Motivated by these facts, we explore how to
localize and understand vulnerabilities, and propose a method, based on
Mechanistic Interpretability (MI) techniques, to guide this process.
Specifically, this method enables us to detect vulnerabilities related to a
concrete task by (i) obtaining the subset of the model that is responsible for
that task, (ii) generating adversarial samples for that task, and (iii) using
MI techniques together with the previous samples to discover and understand the
possible vulnerabilities. We showcase our method on a pretrained GPT-2 Small
model carrying out the task of predicting 3-letter acronyms to demonstrate its
effectiveness on locating and understanding concrete vulnerabilities of the
model.
