OpenSecureAI Scanner vs Project Moonshot

OpenSecureAI Scanner checks untrusted text for patterns that may indicate prompt injection, jailbreaks, system-prompt leaks, or data exfiltration. Its scanPrompt library API returns a 0–100 score, a risk level, and findings for individual rules. Developers can access it as an npm package, CLI, GitHub Action, or REST API; the package targets Node 18+ and has zero runtime dependencies. Authenticated hosted API calls add detections for obfuscation and encoding evasions, indirect injection, tool abuse, multilingual overrides, and de-obfuscation. The LLM Gateway can route prompts to a selected OpenAI, Anthropic, xAI, or Groq provider and scan the response. Documentation says authenticated scans save a summary to the dashboard but do not store raw prompt text; gateway provider keys are not stored or logged. Free access includes in-browser tools and npm packages, while the anonymous API uses the basic engine with IP rate limiting. The Free plan lists 2,000 API requests per month. Pro costs $49 a month, but paid checkout is not self-serve and requires contacting sales.

Project Moonshot is an open-source toolkit for evaluating the safety and reliability of large language models and applications. It runs benchmark tests using open-source datasets and metrics, and generates adversarial prompts to probe possible vulnerabilities and misuse. Users can build custom evaluations with their own datasets, optional prompt templates, metrics, and grading scales. Results appear in interactive HTML reports and downloadable raw JSON. Moonshot provides a Web UI, interactive command-line interface, library APIs, and Web APIs, with configurable connections to model providers including OpenAI, Anthropic, Together, and Hugging Face. Users can also create connectors for other models or applications hosted on custom servers. The toolkit implements benchmarks recommended in IMDA’s Starter Kit for safety testing LLM-based applications. Moonshot is free beta software under Apache License 2.0. Installation uses pip and requires Python 3.11; the Web UI additionally requires Node.js 20.11.1 LTS or later, and test assets are needed to run tests. The installation guide recommends Chrome for the Web UI and notes possible installation difficulties on x86 Macs with moonshot-data dependencies.