AI agent security checklist for developers

An AI agent combines a probabilistic decision-maker with deterministic tools. The model can misunderstand context or follow hostile instructions; the tools can still execute perfectly. Security therefore has to sit around the model: identity, least privilege, strict tool contracts, execution isolation, approval gates, protected memory, and useful evidence.

1. Identity and authentication

• Every agent, runtime, and integration has a distinct identity.
• Agents do not share a user’s full session or one permanent team credential.
• Credentials are short-lived where possible and can be revoked without redeploying the whole system.
• The service verifies token signature, issuer, audience, expiry, and intended scope.
• Actions performed on behalf of a user require both a valid agent identity and valid user authority.
• Production and development agents use separate identities and credentials.

2. Least privilege and authorization

• The agent has only the tools needed for its current role.
• Each tool is scoped by operation, resource, environment, and risk—not just by tool name.
• File access is restricted to required directories, with explicit denials for secrets and keys.
• Network access is restricted to approved destinations and methods.
• Database access uses narrow roles and separates reads, writes, schema changes, and administration.
• Unknown actions fail closed or pause for review instead of being allowed by default.
• Permission is checked immediately before execution, not only when the session starts.

3. Prompt and context boundaries

• System rules are separated clearly from external content.
• User input, repository text, retrieved documents, web pages, logs, and tool output are labeled and treated as untrusted.
• The agent does not interpret instructions found inside untrusted content as policy.
• Prompt construction avoids raw concatenation of trusted rules and external text.
• Encoded, hidden, split, and indirect prompt-injection cases are included in security tests.
• Model output is validated before it reaches code execution, file writes, SQL, network requests, or other tools.

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4. Tool design and execution

• Tools expose small, named operations rather than arbitrary commands.
• Every tool has a strict input schema, length limits, type checks, and unknown-field rejection.
• Paths, URLs, identifiers, commands, and other high-risk arguments use allowlists where practical.
• The server maps validated requests to implementation code; the model does not generate raw executable strings.
• Tool output is bounded, sanitized, and labeled before it returns to the model.
• Timeouts, retry limits, concurrency limits, and total step limits prevent runaway execution.
• Repeated failures stop the workflow instead of creating an infinite recovery loop.

5. Sandboxing and blast-radius control

• Generated code runs outside the host environment in an isolated, disposable workspace.
• The sandbox starts without production credentials or unrestricted host mounts.
• Outbound network access is denied by default or limited to approved destinations.
• CPU, memory, process, storage, and execution-time limits are enforced outside the model.
• The agent cannot disable its own sandbox, monitoring, policy, or iteration limits.
• Production changes use a separate deployment path with stronger authorization.

6. Secrets and sensitive data

• Secrets are stored in a secret manager and injected only at the moment they are needed.
• Raw credentials are not placed in prompts, memory, repository files, tool descriptions, or logs.
• Tools return the minimum data needed for the task.
• Common secret fields are redacted before model exposure and before logging.
• Sensitive datasets have explicit access rules, retention periods, and deletion procedures.
• Credential use is attributable to a specific agent action.

7. Memory and retrieval integrity

• Memory writes record source, author, timestamp, and trust level.
• Untrusted content cannot silently become durable policy or a trusted fact.
• High-impact memory changes require validation or approval.
• Retrieved content is filtered by tenant, user, project, and sensitivity boundaries.
• The system can inspect, quarantine, correct, and delete poisoned memory.
• Security tests include delayed attacks where malicious memory affects a later session.

8. Human approval and safe interruption

• Production writes, destructive actions, authentication changes, secret access, payments, and external publication require approval.
• The approval screen shows the agent, tool, action, target, risk, relevant arguments, and reason.
• Approval applies to one bounded action or a short, explicit grant—not an open-ended session.
• The real operation waits for the final decision; it never executes first and asks afterward.
• Approvals expire and can be revoked.
• Users can stop an active agent and revoke credentials without waiting for the agent to cooperate.

9. Logging, monitoring, and detection

• Logs connect the agent identity, user, session, tool, action, validated arguments, policy decision, approval, result, and timestamp.
• Denied and failed attempts are logged as well as successful actions.
• Sensitive values are redacted without removing the evidence needed to investigate.
• Alerts cover repeated denials, unusual tool sequences, privilege changes, secret access, large data movement, and disabled controls.
• Logs are protected from agent modification and retained for an explicit period.
• Operators can trace a final outcome back through the actions that produced it.

10. Supply chain and configuration

• Models, MCP servers, plugins, packages, containers, prompts, and policies have recorded versions.
• Dependencies are verified, pinned, scanned, and updated through a reviewable process.
• Generated package names are checked against an authoritative registry before installation.
• Configuration files and agent instructions receive code review and ownership protection.
• New tools and permission changes trigger a security review.
• Development defaults cannot silently become production policy.

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11. Testing and release gates

• Tests cover direct and indirect prompt injection, malicious tool output, poisoned memory, and compromised dependencies.
• Authorization tests verify cross-user, cross-project, and cross-tenant isolation.
• The team tests what happens when the model chooses the wrong tool or valid-looking but dangerous arguments.
• Security checks run before generated code is merged or deployed.
• Policy, prompt, model, tool, and retrieval changes trigger regression tests.
• High-risk findings block release or require a documented exception with an owner and expiry.

12. Incident response

• The team can disable a tool, agent, credential, model route, or integration independently.
• Runbooks cover prompt injection, secret exposure, malicious packages, unauthorized actions, and memory poisoning.
• Incident responders can preserve logs and reconstruct the action chain.
• Compromised memory and generated artifacts can be identified and removed.
• Credentials used by the agent can be rotated quickly.
• After an incident, fixes become policy, tests, and monitoring—not only a written lesson.

The minimum release gate

Do not give an agent production authority until the team can answer yes to five questions: Is the agent uniquely identified? Is its authority narrowly scoped? Are model requests validated before execution? Do high-impact actions wait for approval? Can you reconstruct and stop what happened?