Securing AI agents and MCP (Model Context Protocol) servers starts with understanding that every tool an agent can call, every secret it can read, and every action it can take is potential attack surface. Unlike a traditional web app, an agent's blast radius is dynamic — one compromised tool call can cascade into data exfiltration, privilege escalation, or full account takeover. The short answer: map what your agents expose, test it the way an attacker would, and fix the highest-impact issues first.
When you ship an AI agent backed by MCP servers, you are essentially giving a reasoning engine a set of callable functions that can read files, query databases, send messages, or call external APIs. Attackers can abuse this in ways that traditional security tools were never designed to catch:
OWASP now publishes three complementary Top 10 lists covering LLM applications, agentic systems, and the MCP specifically. Together they highlight the most commonly exploited weaknesses:
Mapping your own agent's tools against these categories is the first structured step toward a defensible architecture.
Pinaka is purpose-built for exactly this threat model. It approaches security from both directions simultaneously: it maps the external infrastructure your agents run on and scans the agent and MCP code itself for risks.
Agent Surface scanning — Pinaka's Agent Surface feature analyses the MCP servers and agent tools in your codebase and flags risks mapped directly to the OWASP MCP, LLM, and Agentic Top 10. It runs locally on your own machine; your source code never leaves your environment.
External attack surface management — Pinaka continuously maps subdomains (from 14+ discovery sources), open ports, cloud assets (S3, GCS, Azure), exposed secrets with validation, and correlates findings against CVE intelligence with EPSS scoring and CISA KEV tracking. Monitoring runs every six hours, so drift is caught before attackers find it.
Adversarial agent simulation — Rather than just listing findings, Pinaka runs an adversarial agent against your own assets to test what an attacker could actually exploit. Every hunt records what it tested, what it found, and what it ruled out — deterministic, reproducible evidence rather than hallucinated severity scores.
Works where you work — Pinaka integrates directly into Claude, Cursor, or any MCP client, so there is no context switching during an investigation.
You can run a free security check on your domain in under a minute, with no signup required, at pinaka.sh.
An MCP (Model Context Protocol) server exposes a set of callable tools to an AI agent — think of it as an API layer that the model can invoke autonomously. Because the model decides when and how to call these tools, a compromised or overprivileged MCP server can be abused to perform actions far beyond the agent's intended scope, making it a high-value target for attackers.
Prompt injection is an attack where malicious instructions are embedded in data the agent processes — a document, a web page, a database record — causing the model to follow the attacker's instructions instead of the application's. In an agentic context this is especially dangerous because the agent may then call real tools with attacker-controlled parameters.
No. Pinaka's Agent Surface scanner runs locally on your own machine and your source code never leaves your environment.
Pinaka runs its continuous monitoring pipeline every six hours, covering subdomains, open ports, cloud assets, exposed secrets, and CVE correlation.
Pinaka maps findings to the OWASP MCP Top 10, the OWASP LLM Top 10, and the OWASP Agentic Top 10 — giving teams a structured, recognised framework for prioritising remediation.