Short answer. Four security risks come up most often with autonomous AI agents: agents running with more access than their task needs, instructions hidden in content they read, tools that change after they're approved, and logs that can't show which agent acted.
1. Agents on shared keys or personal tokens can reach far more than their task needs
Agents connected to a CRM, email, file storage or code repos often run on a shared API key, a service account or someone's personal token. Every request then gets that credential's full reach, whoever is asking and whatever the task. One misused agent can reach every customer record, file or repo the credential can.
2. Instructions hidden in an email or ticket can make an agent leak data using valid access
Agents that read outside content, such as email, support tickets, web pages or shared documents, can be steered by instructions planted there. If the same agent can also send data out through email, chat, a public post or an API call, it can move sensitive data using a real person's access. Often nothing looks unusual to existing tools, so these leaks can surface late, sometimes only when a customer, partner or auditor finds them.
3. A connector or MCP server can change after the team approves it
Agents depend on connectors, plugins and MCP servers, many installed from public registries and updated without another review. A tool that was safe when approved can later change what it does, such as sending copies of data to an outside address. Every agent and every person relying on that tool is exposed at once, for as long as it keeps running.
4. When something goes wrong, a shared account can't show which agent did it
When several agents share one key or run as a person, the logs show only that account. During an incident, the team can't tell which agent acted or who asked, and the quickest fix is often disabling the key, which stops every agent and job on it. The same gap comes back when an auditor or a customer questionnaire asks who an agent acted for.
How teams reduce these risks, starting with access
Giving each agent its own identity and granting access per app limits what any one agent can reach. A check on each tool call can then block actions that policy doesn't allow, such as sending data to an outside address, even when hidden instructions or a changed tool triggered them. A record of each call ties every action to an agent and a person, and revoking one agent stops it without breaking the others, which helps shorten incidents and answer auditors.
Frequently Asked Questions
What are the main agentic AI risks?
The main agentic AI risks are agents with more access than their task needs, hidden instructions that steer what they do, tools that change after approval, and records that can't tie an action to an agent and a person. Teams often limit them with a separate identity for each agent and a check on each tool call, and keep a record of each call for investigations.