  Large language models (LLMs) are possessed of numerous beneficial
capabilities, yet their potential inclination harbors unpredictable risks that
may materialize in the future. We hence propose CRiskEval, a Chinese dataset
meticulously designed for gauging the risk proclivities inherent in LLMs such
as resource acquisition and malicious coordination, as part of efforts for
proactive preparedness. To curate CRiskEval, we define a new risk taxonomy with
7 types of frontier risks and 4 safety levels, including extremely
hazardous,moderately hazardous, neutral and safe. We follow the philosophy of
tendency evaluation to empirically measure the stated desire of LLMs via
fine-grained multiple-choice question answering. The dataset consists of 14,888
questions that simulate scenarios related to predefined 7 types of frontier
risks. Each question is accompanied with 4 answer choices that state opinions
or behavioral tendencies corresponding to the question. All answer choices are
manually annotated with one of the defined risk levels so that we can easily
build a fine-grained frontier risk profile for each assessed LLM. Extensive
evaluation with CRiskEval on a spectrum of prevalent Chinese LLMs has unveiled
a striking revelation: most models exhibit risk tendencies of more than 40%
(weighted tendency to the four risk levels). Furthermore, a subtle increase in
the model's inclination toward urgent self-sustainability, power seeking and
other dangerous goals becomes evident as the size of models increase. To
promote further research on the frontier risk evaluation of LLMs, we publicly
release our dataset at https://github.com/lingshi6565/Risk_eval.
