  Large language models (LLMs) have raised concerns about potential security
threats despite performing significantly in Natural Language Processing (NLP).
Backdoor attacks initially verified that LLM is doing substantial harm at all
stages, but the cost and robustness have been criticized. Attacking LLMs is
inherently risky in security review, while prohibitively expensive. Besides,
the continuous iteration of LLMs will degrade the robustness of backdoors. In
this paper, we propose TrojanRAG, which employs a joint backdoor attack in the
Retrieval-Augmented Generation, thereby manipulating LLMs in universal attack
scenarios. Specifically, the adversary constructs elaborate target contexts and
trigger sets. Multiple pairs of backdoor shortcuts are orthogonally optimized
by contrastive learning, thus constraining the triggering conditions to a
parameter subspace to improve the matching. To improve the recall of the RAG
for the target contexts, we introduce a knowledge graph to construct structured
data to achieve hard matching at a fine-grained level. Moreover, we normalize
the backdoor scenarios in LLMs to analyze the real harm caused by backdoors
from both attackers' and users' perspectives and further verify whether the
context is a favorable tool for jailbreaking models. Extensive experimental
results on truthfulness, language understanding, and harmfulness show that
TrojanRAG exhibits versatility threats while maintaining retrieval capabilities
on normal queries.
