Experimenting with ChatGPT's Vulnerability Volcano and prompt tricks: in this article we review in a practical and ethical way how large language models like CodeGen and ChatGPT respond to potentially insecure code generation tasks using few-shot prompting, prompt transfer between models, and evaluation through a security benchmark designed for controlled testing.
Methodology: we conducted prompting sessions with limited examples to observe generation patterns, measured prompt transfer by testing the same prompts on different models, and compared results against a set of security tests that simulate common bad practices in custom software development without providing instructions for exploitation.
Key findings: models can propose functional solutions that contain security risks by default, the effectiveness of a prompt depends heavily on design and context, and prompt transfer between models usually works at the intention level but loses critical mitigation details. These results underscore the need to incorporate human reviews and automated controls in custom software development pipelines.
Implications for cybersecurity: the use of LLMs in development workflows speeds up prototyping but introduces risk vectors if there is no validation. We recommend applying a secure development lifecycle that includes static analysis, automated security testing, and manual audits before deploying solutions in production environments on AWS and Azure cloud services.
Best practices and mitigations: encourage prompts that require security explanations, integrate adapted filters and linters, run internal security benchmarks, and train teams on risks associated with artificial intelligence applied to development. These practices help reduce the generation of insecure patterns without sacrificing the productivity that AI brings to businesses.
About Q2BSTUDIO: we are a custom software and application development company specialized in artificial intelligence, cybersecurity, and AWS and Azure cloud services. We offer custom software solutions, AI agents, Power BI implementations, and business intelligence services to transform data into decisions and create robust, secure products for companies of all sizes.
Featured services: custom application development, custom software consulting, integration of artificial intelligence into business processes, cybersecurity audits, migration and management on AWS and Azure cloud services, development of custom AI agents, and business intelligence projects with Power BI.
Conclusion: LLMs are powerful tools for accelerating development but must be used with security controls and validation processes. At Q2BSTUDIO we combine expertise in artificial intelligence and cybersecurity to design solutions that leverage the potential of generative models without compromising system protection or data integrity.
Contact and call to action: if you would like to explore how to integrate AI agents or implement secure custom software with support on AWS and Azure cloud services and business intelligence capabilities with Power BI, contact Q2BSTUDIO for an initial consultation and a plan tailored to your needs.
