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
The full story
This article is one source in a clustered incident — the cluster page carries the summary, timeline and every other outlet covering it.
