  AI assistants for coding are on the rise. However one of the reasons
developers and companies avoid harnessing their full potential is the
questionable security of the generated code. This paper first reviews the
current state-of-the-art and identifies areas for improvement on this issue.
Then, we propose a systematic approach based on prompt-altering methods to
achieve better code security of (even proprietary black-box) AI-based code
generators such as GitHub Copilot, while minimizing the complexity of the
application from the user point-of-view, the computational resources, and
operational costs. In sum, we propose and evaluate three prompt altering
methods: (1) scenario-specific, (2) iterative, and (3) general clause, while we
discuss their combination. Contrary to the audit of code security, the latter
two of the proposed methods require no expert knowledge from the user. We
assess the effectiveness of the proposed methods on the GitHub Copilot using
the OpenVPN project in realistic scenarios, and we demonstrate that the
proposed methods reduce the number of insecure generated code samples by up to
16\% and increase the number of secure code by up to 8\%. Since our approach
does not require access to the internals of the AI models, it can be in general
applied to any AI-based code synthesizer, not only GitHub Copilot.
