OpenSecureAI Scanner vs PyRIT

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.

PyRIT is a free, open-source framework for automated and human-led red teaming of generative AI systems, aimed at assessing their security and safety. It supports single-turn and multi-turn attack strategies, including Crescendo, TAP, and Skeleton Key. Scenarios combine strategies and datasets for repeatable assessments of content harms, psychosocial risks, data leakage, and other objectives. Assessments can run through a command-line scanner, an interactive shell, the CoPyRIT graphical interface, or custom workflows. Documented targets include OpenAI, Azure, Anthropic, Google, Hugging Face, custom HTTP endpoints and WebSockets, and web apps tested with Playwright. Its modular components include targets, converters, scorers, memory, datasets, attacks, and scenarios. Converters can encode, obfuscate, translate, or semantically alter prompts, and transform among text, images, audio, video, and files. Scorers can return Boolean or normalized 0.0–1.0 scores using LLMs, Azure AI Content Safety, or custom logic. Built-in memory tracks conversations, scores, and attack results using SQLite or Azure SQL. PyRIT is self-hosted and requires a Python environment and configured AI endpoints.