  In this paper, we present a very first study to investigate trust and ethical
implications of on-device artificial intelligence (AI), focusing on ''small''
language models (SLMs) amenable for personal devices like smartphones. While
on-device SLMs promise enhanced privacy, reduced latency, and improved user
experience compared to cloud-based services, we posit that they might also
introduce significant challenges and vulnerabilities compared to on-server
counterparts.
  As part of our trust assessment study, we conduct a systematic evaluation of
the state-of-the-art on-devices SLMs, contrasted to their on-server
counterparts, based on a well-established trustworthiness measurement
framework. Our results show on-device SLMs to be (statistically) significantly
less trustworthy, specifically demonstrating more stereotypical, unfair and
privacy-breaching behavior. Informed by these findings, we then perform our
ethics assessment study by inferring whether SLMs would provide responses to
potentially unethical vanilla prompts, collated from prior jailbreaking and
prompt engineering studies and other sources. Strikingly, the on-device SLMs
did answer valid responses to these prompts, which ideally should be rejected.
Even more seriously, the on-device SLMs responded with valid answers without
any filters and without the need for any jailbreaking or prompt engineering.
These responses can be abused for various harmful and unethical scenarios
including: societal harm, illegal activities, hate, self-harm, exploitable
phishing content and exploitable code, all of which indicates the high
vulnerability and exploitability of these on-device SLMs. Overall, our findings
highlight gaping vulnerabilities in state-of-the-art on-device AI which seem to
stem from resource constraints faced by these models and which may make typical
defenses fundamentally challenging to be deployed in these environments.
