LLM Prompt Injection Defense Tools

This project is a comprehensive security platform providing an LLM security orchestration framework, an AI agent firewall, and tools for vulnerability remediation, compliance automation, and endpoint protection. It functions as a centralized system to protect AI models from adversarial exploits while managing the identification and patching of software flaws. The platform distinguishes itself through the coordination of specialized AI agents to automate complex security workflows, including reconnaissance, bug hunting, and exploit development. It implements dedicated guardrails to block promp

LLM Guard is a security firewall and guardrail framework designed to scan and sanitize inputs and outputs for large language models. It functions as a proxy gateway and security layer to block prompt injections, toxicity, and sensitive data leakage while ensuring that model interactions remain compliant with organizational policies. The system distinguishes itself through a modular scanner pipeline that utilizes local model orchestration to eliminate external network dependencies. It supports real-time security filtering via streaming chunk analysis and implements a fail-fast execution model

Garak is an AI model evaluation tool and vulnerability scanner designed for red teaming large language models and auditing the security of retrieval-augmented generation pipelines. It identifies behavioral weaknesses, such as jailbreaks, hallucinations, and data leakage, by simulating adversarial attacks and executing automated testing vectors. The framework utilizes an adaptive probing loop where prompts can react to previous model behavior and be modified in flight via middleware. To ensure consistent analysis, it employs a provider-agnostic interface to interact with various model APIs and

AI-Infra-Guard is a security scanning platform designed to detect vulnerabilities across large language model deployments, AI agent skills, and the underlying infrastructure. It functions as a security toolset for auditing source code, evaluating model robustness, and identifying insecure network configurations. The project provides a red teaming framework that uses curated attack datasets to test for jailbreak vulnerabilities and prompt injections. It also includes an infrastructure auditor that employs network fingerprinting and asset discovery to match running components against known comm

This project is a comprehensive guide and framework for designing, optimizing, and securing inputs to improve the accuracy and reasoning of large language model outputs. It provides core methodologies for implementing logical reasoning steps, example-based learning, and reusable template systems. The framework distinguishes itself through a focus on security guardrails and ethical auditing, implementing primitives to prevent adversarial prompt injection attacks and identify biases. It also emphasizes structured generation, using persona assignment and negative constraints to control the tone,

This project is an artificial intelligence gateway that functions as a centralized middleware layer for managing, securing, and observing interactions with language, vision, and audio models. It provides a unified interface that standardizes requests across multiple providers, enabling teams to integrate AI capabilities into their applications through a consistent set of tools and protocols. The gateway distinguishes itself through its comprehensive infrastructure governance and traffic management capabilities. It allows for policy-driven routing, automated failover, and load balancing across

CL4R1T4S is a framework designed to orchestrate generative AI workflows and optimize language model outputs. It functions as a centralized utility for managing, versioning, and deploying structured system prompts and behavioral parameters to ensure consistent performance across complex tasks. The project distinguishes itself by implementing a structured pipeline that wraps model interactions to enforce behavioral constraints and sanitize inputs. This orchestration layer incorporates heuristic-based validation and stateful context management to maintain coherence and quality throughout multi-s

Promptfoo is an evaluation framework designed for testing, benchmarking, and red-teaming language models and agentic workflows. It provides a unified environment to run prompts against multiple providers, allowing developers to systematically validate model outputs against objective assertions, semantic similarity metrics, and custom grading rubrics. The platform distinguishes itself through a provider-agnostic execution layer and a stateful orchestrator capable of simulating multi-turn conversations and complex tool-use trajectories. It includes a dedicated adversarial mutation pipeline that

OpenFang is an operating system for LLM agents designed to orchestrate autonomous agents with built-in task scheduling, tool sandboxing, and multi-model routing. It provides a secure AI execution environment that integrates prompt injection scanning, cryptographic audit trails, and resource metering to ensure controlled processing. The platform distinguishes itself through a comprehensive security architecture, featuring fuel-metered tool sandboxing and an immutable activity audit trail based on cryptographic hash-chains. It implements high-assurance identity verification via signed manifests

Ironclaw is an LLM orchestration framework and AI agent gateway designed to connect large language models with external tools, messaging interfaces, and persistent memory systems. It functions as a communication layer that routes interactions between users and AI models via HTTP webhooks and various messaging channels. The system focuses on secure tool execution through a WebAssembly sandbox and isolated containers, which allows the framework to run untrusted code and dynamically generate new tools from natural language descriptions. Security middleware provides prompt injection defense and s

LiteLLM is a unified gateway and proxy server designed to centralize access to over one hundred language model providers. It provides a standardized API interface that abstracts vendor-specific schemas, allowing developers to interact with diverse models through a single, consistent format. By acting as a central traffic management layer, it enables organizations to route, secure, and govern model interactions across multiple deployments. The platform distinguishes itself through its policy-driven architecture, which uses configuration-based routing to manage traffic distribution, load balanc

This project is a comprehensive framework for developing, orchestrating, and deploying autonomous agents. It provides a structured environment for building agents that utilize reasoning loops to perform multi-step tasks, manage state through graph-based workflows, and interact with external tools. By mapping unstructured model outputs into typed schemas, the framework ensures reliable integration with downstream application logic. The platform distinguishes itself through a focus on production-grade reliability and security. It incorporates hybrid memory systems that combine vector embeddings

Litestar is a high-performance Python ASGI web framework designed for building asynchronous APIs and web services. It functions as a type-safe toolkit that leverages Python type hints to provide automatic request validation and response serialization, while natively generating interactive API documentation based on the OpenAPI specification. The framework is distinguished by its integrated dependency injection system, which manages shared resources and resolves complex nested service chains directly within request handlers. It further organizes API development through class-based controllers