RildoNeto17/llmSecurityDemo

This is a personal demo/educational project with no adoption signals (0 stars, 0 forks, no commit velocity in the past year). The README describes applying an existing framework (OWASP Top 10) to LLM security testing—a straightforward application of well-known security principles to a new domain. No novel detection mechanisms, novel vulnerability classes, or proprietary approach is evident. The project sits at the intersection of LLM safety and security scanning, but frontier labs (OpenAI, Anthropic, Google) are already shipping built-in safety layers, red-teaming tools, and vulnerability disclosure programs that dwarf this demo. The OWASP Top 10 for LLMs is a public standard; any implementation against it is commodity work. Frontier labs would view this as at most reference material for their own (far more sophisticated) safety testing infrastructure, not as competitive IP. The lack of any user engagement, documentation depth, or algorithmic novelty places this squarely in the tutorial/experiment category. High frontier risk because this functionality is rapidly being absorbed into platform capabilities (Claude API safety documentation, OpenAI's moderation API, Anthropic's constitutional AI testing).