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Agentic Radar is a security scanner designed to analyze and assess agentic systems, providing developers, researchers, and security professionals with insights into their functionality and potential vulnerabilities. It generates comprehensive reports that include workflow visualizations, tool identification, and vulnerability mappings aligned with established security frameworks like OWASP Top 10 LLM Applications and OWASP Agentic AI – Threats and Mitigations. By offering a structured view of workflows, tools, and associated risks, Agentic Radar enhances transparency and simplifies the security assessment of complex agentic systems.

The MCP Server is a Burp Suite extension that enables integration with AI clients via the Model Context Protocol (MCP). It allows Burp Suite to connect with AI clients through MCP, offers automatic installation for Claude Desktop, and includes a packaged Stdio MCP proxy server. To use it, configure the Burp MCP server in the extension’s tab, set your MCP client to use the Burp SSE MCP server or stdio proxy, and then interact with Burp through your client.

This video explains the new LM Report AI Feature

This video explains the new AI HTTP Analyzer AI Feature

This video explains the new Shadow Repeater AI Feature

This video explains the new Finding broken access control with AI Feature

This video explains the new Record login sequence with AI Feature

This video explains the new Burp AI Explainer Feature

This video explains the new Burp AI Explore Feature

The J2 Playground by Scale AI is an interactive platform designed to test the resilience of large language models (LLMs) against jailbreak attempts. To use it, select an attacker model (e.g., Claude-Sonnet-3.5 or Gemini-1.5-Pro) and a target model (e.g., GPT-4o or Gemini-1.5-Pro). Define the behavior you want to elicit from the target model, such as generating specific instructions. Choose an attack strategy, then click “Start Conversation” to initiate the simulated interaction. This setup allows users to observe how effectively the attacker model can bypass the target model’s safeguards, providing valuable insights into the vulnerabilities and safety measures of various LLMs.