what is threat-modeling-toolkit fr?

The Threat Modeling Toolkit is a security scanner that helps development teams catch logic bugs in their code before shipping to production. Instead of looking for known malware signatures, it focuses on subtle application-level vulnerabilities like replay attacks, race conditions, token abuse, and authentication flaws. It is built specifically for startup teams that need fast, repeatable security checks without investing in heavyweight enterprise security tools. The tool works in two stages. First, it performs a pattern-based scan across your codebase, searching for common mistakes like missing rate limiting on API routes, weak password hashing, or tokens that never expire. Second, it optionally uses a large language model (an AI assistant) to do a deeper review of the code. You can run just the pattern scanner for a quick check, or enable the LLM review for a more thorough analysis. After scanning, it generates reports in Markdown and JSON formats detailing what it found. This tool is designed for engineering teams who want to integrate security checks directly into their software development lifecycle. For example, a developer can run a quick scan locally before submitting a pull request, and the team can configure their automated pipeline to block merges if high or critical vulnerabilities are detected. A security lead can then run a full comprehensive scan before each release, comparing the results to previous reports to ensure security is improving over time. The tool returns specific exit codes, making it easy to automate these pass-or-fail decisions in standard continuous integration pipelines. A notable aspect of the project is its flexibility with AI providers. The LLM review works with a free Hugging Face model by default, but teams can also configure it to use OpenAI, Anthropic, or a local model. The AI review is structured to minimize false positives by forcing the model to provide concrete code references and only report findings above a 70% confidence threshold. The README does not go into detail on exactly how the underlying pattern scanners parse the code, but the configuration options allow teams to target specific directories and file types.