  Training large language models (LLMs) requires a substantial investment of
time and money. To get a good return on investment, the developers spend
considerable effort ensuring that the model never produces harmful and
offensive outputs. However, bad-faith actors may still try to slander the
reputation of an LLM by publicly reporting a forged output. In this paper, we
show that defending against such slander attacks requires reconstructing the
input of the forged output or proving that it does not exist. To do so, we
propose and evaluate a search based approach for targeted adversarial attacks
for LLMs. Our experiments show that we are rarely able to reconstruct the exact
input of an arbitrary output, thus demonstrating that LLMs are still vulnerable
to slander attacks.
