  Prompt tuning is one of the most effective solutions to adapting a fixed
pre-trained language model (PLM) for various downstream tasks, especially with
only a few input samples. However, the security issues, e.g., Trojan attacks,
of prompt tuning on a few data samples are not well-studied. Transferring
established data poisoning attacks directly to few-shot prompt tuning presents
multiple challenges. One significant issue is the \textit{poisoned imbalance
issue}, where non-target class samples are added to the target class, resulting
in a greater number of target-class samples compared to non-target class. While
this issue is not critical in regular tuning, it significantly hampers the
few-shot prompt tuning, making it difficult to simultaneously achieve a high
attack success rate (ASR) and maintain clean data accuracy (CDA). Additionally,
few-shot prompting is prone to overfitting in terms of both ASR and CDA. In
this paper, we introduce \textit{TrojFSP}, a method designed to address the
challenges. To solve the poisoned imbalance issue, we develop a
\textit{Target-Class Shrink (TC-Shrink)} technique, which aims to equalize the
number of poisoning samples. To combat overfitting, we employ a
\textit{Selective Token Poisoning} technique to boost attack performance.
Furthermore, we introduce a \textit{Trojan-Trigger Attention} objective
function to amplify the attention of the poisoned trojan prompt on triggers.
Experiments show that our TrojFSP achieves an ASR of over 99\% while
maintaining negligible decreases in CDA across various PLMs and datasets.
