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praetorian-incLLM security scanner for adversarial attack detection
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Augustus: LLM Vulnerability Scanner
Augustus is a Go-based framework designed for security professionals to systematically test Large Language Models (LLMs) against a wide array of adversarial attacks. It addresses the growing need for robust LLM security by providing over 210 probes covering prompt injection, jailbreaks, data extraction, and other vulnerabilities, enabling users to identify and report on LLM weaknesses in production environments.
How It Works
Augustus employs a pipeline architecture for its scanning process: probe selection, optional prompt transformation (buffing), LLM generation via provider integrations, and detector analysis. Its core advantage lies in its Go-native implementation, offering a single, portable binary with efficient concurrency managed by goroutine pools. This design prioritizes production readiness with built-in rate limiting, retry logic, and timeout handling, differentiating it from research-focused tools. Advanced features include iterative attack engines (PAIR, TAP) and multi-turn conversational engines (Crescendo, GOAT, Hydra, Mischievous User) that maintain conversation history and employ sophisticated strategies for deeper vulnerability discovery.
Quick Start & Requirements
go install github.com/praetorian-inc/augustus/cmd/augustus@latest or build from source using make build.CONTRIBUTING.md for contribution guidelines.Highlighted Details
Maintenance & Community
The project welcomes contributions, with detailed guidelines provided in CONTRIBUTING.md for adding new probes, detectors, or provider integrations.
Licensing & Compatibility
Augustus is licensed under the Apache 2.0 license. No specific compatibility restrictions for commercial use or closed-source linking are mentioned.
Limitations & Caveats
Requires the Go toolchain for installation and building. Certain probes are designed to generate offensive content for safety filter testing and should only be used in authorized environments.
1 day ago
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NVIDIA