Augustus vs PyRIT

Augustus is a free, self-hosted Go scanner for security professionals assessing large language models. It runs adversarial probes and produces vulnerability reports, with more than 210 probes across 47 attack categories, including jailbreaks, prompt injection, data extraction, safety benchmarks, and agent attacks. It supports 28 provider categories and 43 generator variants, including hosted providers, Ollama, and custom REST endpoints. More than 90 detectors cover approaches such as pattern matching, LLM-as-a-judge, HarmJudge, Perspective API, and unsafe-content detection; seven prompt transformations include encoding, paraphrase, poetry, low-resource-language translation, and case changes. Results can be written as tables, JSON, JSONL, or HTML. The project lists concurrent scanning, rate limits, retries, and timeout handling. Augustus compiles to a portable single binary and can be installed with Go or built from source; the README requires Go 1.27.0 or later. It is Apache 2.0 licensed. The project is intended for authorized security testing, and users are told to test only systems they own or have permission to assess. Provider API use may require credentials.

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.