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Akto

Akto

securityweekly.com June 22, 2026

Akto is the trusted MCP and AI Agent Security Platform for Fortune 500 security teams.

Akto is the trusted MCP and AI Agent Security Platform for Fortune 500 security teams.

Akto Atlas for Employees

Akto Argus for Homegrown Apps

Get visibility and enforce guardrails for LLMs, AI agents, and MCP tools used by employees across their devices, ensuring safe AI usage at the individual level.

Get visibility and enforce guardrails for LLMs, AI agents, and MCP tools used by employees across their devices, ensuring safe AI usage at the individual level.

Turn AI chaos into control. Akto maps every AI agent and MCP tool, gives visibility, runs continuous red teaming, and enforces guardrails at scale.

Automatically discover and catalog MCPs, AI agents, tools, and resources across your infrastructure, cloud, employee laptops.

Automatically discover and catalog MCPs, AI agents, tools, and resources across your infrastructure, cloud, employee laptops.

Automatically discover and catalog MCPs, AI agents, tools, and resources across your infrastructure, cloud, employee laptops.

Security leaders rank MCP/agent discovery as a critical need

better Agentic actions visibility Coverage

AI Agent–tool actions analyzed and secured with guardrails

concern of Enterprises while deploying AI agents in production is cybersecurity risk.

Without Akto, hidden risks at input, execution, and output turn trust into an illusion.

Without Akto, hidden risks at input, execution, and output turn trust into an illusion.

Without Akto, hidden risks at input, execution, and output turn trust into an illusion.

Attackers compromise MCP-connected tools to manipulate agent behavior, extract context, or return malicious outputs.

Malicious inputs trick agents into skipping validation steps and jumping directly to sensitive tool execution.

Malicious or unauthorized tools impersonate legitimate ones to hijack execution within MCP-based workflows.

LLMs misinterpret untrusted tool responses as prompts, allowing attackers to influence or control model behavior.

LLMs invoke backend actions they should not, due to missing or bypassed authorization checks.

Backends change tool behavior mid-session, breaking trust assumptions and enabling unexpected execution paths.

Attackers compromise MCP-connected tools to manipulate agent behavior, extract context, or return malicious outputs.

Malicious inputs trick agents into skipping validation steps and jumping directly to sensitive tool execution.

Malicious or unauthorized tools impersonate legitimate ones to hijack execution within MCP-based workflows.

Securing LLMs with a 3-Layer Defense Model

Cheatsheet on Building Safe and Governed AI Agents

The Essential Agentic AI Security Glossary

Extracted Entities

Platforms (2)