  The past year has seen rapid acceleration in the development of large
language models (LLMs). However, without proper steering and safeguards, LLMs
will readily follow malicious instructions, provide unsafe advice, and generate
toxic content. We introduce SimpleSafetyTests (SST) as a new test suite for
rapidly and systematically identifying such critical safety risks. The test
suite comprises 100 test prompts across five harm areas that LLMs, for the vast
majority of applications, should refuse to comply with. We test 11 open-access
and open-source LLMs and four closed-source LLMs, and find critical safety
weaknesses. While some of the models do not give a single unsafe response, most
give unsafe responses to more than 20% of the prompts, with over 50% unsafe
responses in the extreme. Prepending a safety-emphasising system prompt
substantially reduces the occurrence of unsafe responses, but does not
completely stop them from happening. Trained annotators labelled every model
response to SST (n = 3,000). We use these annotations to evaluate five AI
safety filters (which assess whether a models' response is unsafe given a
prompt) as a way of automatically evaluating models' performance on SST. The
filters' performance varies considerably. There are also differences across the
five harm areas, and on the unsafe versus safe responses. The widely-used
Perspective API has 72% accuracy and a newly-created zero-shot prompt to
OpenAI's GPT-4 performs best with 89% accuracy. Content Warning: This paper
contains prompts and responses that relate to child abuse, suicide, self-harm
and eating disorders, scams and fraud, illegal items, and physical harm.
