A security scanner that analyzes agentic AI workflows for vulnerabilities, visualizes their structure, and hardens system prompts.
Agentic Radar is an open-source security scanner specifically built for AI agentic workflows. It analyzes multi-agent systems to visualize their structure, detect external tools and MCP servers, map security vulnerabilities, and harden system prompts. The tool helps identify risks like prompt injection and PII leakage while providing actionable reports.
Developers, security professionals, and researchers building or deploying AI agentic systems using frameworks like OpenAI Agents, CrewAI, LangGraph, n8n, or Autogen.
Agentic Radar offers a specialized, integrated security suite for agentic AI—combining static analysis, runtime testing, and prompt hardening in one tool. Its focus on agent-specific vulnerabilities and support for multiple frameworks makes it a unique solution for securing complex AI workflows.
A security scanner for your LLM agentic workflows
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Supports key agentic frameworks like OpenAI Agents, CrewAI, and LangGraph out of the box, enabling broad compatibility for diverse AI projects.
Performs automated vulnerability tests for prompt injection and PII leakage during execution, providing actionable security insights beyond static analysis.
Includes pre-built GitHub Actions workflows for seamless integration into development pipelines, ensuring regular security checks without manual setup.
Uses LLMs to automatically improve system prompts based on best practices, enhancing security and reducing manual tuning effort for supported frameworks.
Critical features like runtime vulnerability testing are only available for OpenAI Agents, leaving other frameworks with limited security assessment capabilities.
Advanced functionalities such as prompt hardening and runtime testing require an OpenAI API key, introducing additional costs and potential data privacy concerns.
As a new project, it may lack maturity with potential bugs and limited documentation for complex use cases, as noted in the roadmap for future framework support.