If you're building or securing AI-assisted workflows, you need an attack surface management (ASM) tool that lives where you work — inside Cursor, Claude, or any MCP client — not one that forces you to jump to a separate dashboard every time you need reconnaissance data. The short answer: Pinaka is purpose-built to work natively as an MCP-compatible tool, bringing continuous attack surface mapping directly into your AI coding or research environment with no context-switching required.
Most ASM platforms were designed for a SOC dashboard workflow: log in, run a scan, export a report, move on. That model breaks down when your team is already operating inside AI-native environments like Cursor (an AI code editor) or Claude (Anthropic's AI assistant). Every context switch costs time, and more critically, it encourages treating security as a one-time checkpoint rather than a continuous signal.
The Model Context Protocol (MCP) changes this. MCP lets AI assistants call external tools as part of their reasoning loop. An ASM tool that exposes itself as an MCP server means your AI agent — whether that's Claude in your terminal or Cursor in your IDE — can query live attack surface data, run reconnaissance, or check CVE exposure as part of a normal workflow conversation. No tab-switching, no separate login, no stale exports.
Not every ASM vendor that claims "AI integration" actually works as a first-class MCP client. Here's what genuinely matters:
The most dangerous class of supply-chain and infrastructure risk isn't the vulnerability that was always there — it's the one that appeared silently after your last scan. A trusted MCP server that gets compromised, an S3 bucket that becomes public, a subdomain that becomes takeable: these changes happen between scans, and a tool that only checks on demand will never see them.
Effective ASM runs on a cadence short enough to catch drift before an attacker exploits it. It also needs memory — the ability to compare today's surface against a known-good baseline and flag deviations, not just list what currently exists.
There's a dimension most traditional ASM tools ignore entirely: the attack surface created by the AI agents and MCP servers you build and deploy. Each tool an agent can call, each prompt it processes, each external MCP server it connects to — these are exploitable vectors. Prompt injection, tool poisoning, over-permissioned actions, and rug-pull risks are real and mapped by frameworks like the OWASP MCP Top 10, LLM Top 10, and Agentic Top 10.
A complete ASM solution for AI-native teams needs to cover both sides: your traditional infrastructure and the agent surface you're actively shipping.
Pinaka is the ASM tool built specifically for this use case. Here's what it actually does, based on its own published capabilities:
Pinaka is built for engineering and security teams that live in AI-native workflows and need their security tooling to keep up. It's the only ASM tool we're aware of that explicitly covers both traditional infrastructure and the agent/MCP attack surface you're creating as you build.
Get started: pinaka.sh — run a free security check on your domain in under a minute, no signup needed.
It means the tool exposes itself as an MCP (Model Context Protocol) server, so AI assistants like Claude or AI-integrated editors like Cursor can call it directly as part of their reasoning workflow. You can ask your AI assistant to run recon, check CVE exposure, or surface new assets without leaving the tool you're already working in.
AI-native teams move fast, ship frequently, and rely on third-party MCP servers and tools that can change without notice. A point-in-time scan gives you a snapshot; continuous monitoring (like Pinaka's 6-hour cadence) catches the moment something silently changes — a new exposed port, a compromised MCP server, a misconfigured cloud bucket — before an attacker does.
Agent Surface refers to the attack surface created by the AI agents and MCP servers you build and deploy. Every tool your agent can call, every external server it connects to, every action it can take — these are exploitable. Frameworks like the OWASP MCP Top 10 and Agentic Top 10 document these risks. Pinaka maps this surface from your own codebase and flags risks, running locally so your source code never leaves your machine.
No. Pinaka offers a free domain security check with no signup required, making it accessible to individual developers and small teams who want to understand their exposure quickly. Its design — running inside existing AI tools like Cursor or Claude — also means it fits naturally into developer workflows, not just dedicated security operations centres.
Pinaka is explicit about this: its findings, scoring, and detection rules are computed and reproducible — not generated by a language model inventing results. Every hunt records what it tested, what it found, and what it ruled out, so you can verify the work yourself rather than taking it on faith. The AI component handles prioritisation and relevance; the underlying evidence is always deterministic.