SECUREYOUR
AI AGENTS.
BEFORETHEYBREAK THINGS.

Krysta's probe scans your MCP servers for vulnerabilities in seconds. Find command injection, path traversal, and credential exposure before attackers do.

$ npm install krysta

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BUILT ON

Apache KafkaUpstash RedisVercelApache KafkaUpstash RedisVercelApache KafkaUpstash RedisVercelApache KafkaUpstash RedisVercel

THE EXECUTION LAYER.

For AI agents that write and run code.

SCAN

Find vulnerabilities in your MCP servers

BLOCK

krysta-mesh blocks malicious calls in real-time.

AUDIT

Generate compliance evidence automatically

Vulnerability Scan
Terminal Stream

krysta-probe v1.0.0

▲ Krysta Probe v1.0.0

Scanning MCP servers...

[✔] Found 1 MCP server(s)

filesystem (Stdio)

└── ~/AppData/.../Roaming/Claude

🔴Missing authentication

🟡Potential path traversal risk

-------------------------------------------------------

SCAN SUMMARY

-------------------------------------------------------

Servers Found: 1

Total Findings: 2

🔴 Critical:0

🔴 High:1

🟡 Medium:1

🟢 Low:0

Risk:🟠 HIGH

📝Detailed findings saved to: krysta-report.json

🛡️Mesh policy saved to: krysta-mesh-policy.yaml

Next steps:

→View full report: cat krysta-report.json

→Upload dashboard: krysta-probe --upload

→Apply mesh policy: krysta-mesh start -f krysta-mesh-policy.yaml

→

Why Krysta Wing?

AI agents and MCP ecosystems are rapidly becoming part of production environments, but organizations face major challenges:

Lack of visibility into AI infrastructure
Security vulnerabilities in MCP servers
Excessive permissions and unsafe tool access
Missing governance and audit capabilities
Difficulty enforcing security policies consistently

The Security Paradigm

Current solutions focus primarily on building AI systems.

Krysta Wing focuses on securing and governing them.

We believe every organization deploying AI agents will require dedicated infrastructure for vulnerability discovery, policy enforcement, monitoring, governance, and compliance.

Our vision is to become the operating layer that enables organizations to safely deploy and manage AI systems at scale.

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