
Keep AI Agents Away From Production Deletes
Block standing production delete authority with separate identities, permission ceilings, exact approvals, short-lived access, and audit records.
Autonomy & guardrails
Permissions, prompt injection, sandboxing, and guardrails for coding agents and agent-native applications.
24published articles

Block standing production delete authority with separate identities, permission ceilings, exact approvals, short-lived access, and audit records.

Prevent prompt injection in AGENTS.md and coding-tool rules with provenance, code ownership, protected review, scoped rules, and conflict tests.

Do not share live credentials, customer data, regulated records, unreleased business information, or code outside your authorized policy.

Use a bounded AI security review prompt to produce traceable candidate findings, safe test ideas, and explicit uncertainty without mistaking output for proof.

Prove backup recoverability in an isolated restore drill before a coding agent receives destructive production access.

Keep untrusted pull requests outside privileged CI jobs with narrow tokens, protected deployments, bound OIDC claims, and verified artifacts.

Constrain Claude Code permissions, sandbox access, MCP tools, and release actions while keeping the coding workflow useful.

Set Codex sandbox, approval, network, environment, and MCP boundaries, then verify code changes before any release action.

Isolate coding agents with disposable workspaces, minimal mounts, non-root execution, resource limits, and explicit outbound network policy.

Remove unsafe shell boundaries from agent tools, constrain process arguments, and isolate the external commands that remain necessary.

Review Cursor data handling, project rules, dependencies, app controls, agent permissions, and independent evidence before launch.

Harden coding-agent workflows with untrusted-input separation, minimal token permissions, pinned actions, constrained OIDC, and approval gates.

Run a lightweight threat-model review for an AI-built app by mapping real data flows, assigning owners, and turning decisions into testable release checks.

Contain indirect prompt injection by separating untrusted content from instructions, limiting tools, and testing with harmless canary actions.

Narrow MCP tools by verb, resource, schema, token audience, and approval so model-controlled calls cannot inherit broad authority.

Contain multi-agent prompt injection with preserved provenance, structured handoffs, separated capabilities, intent checks, and safe canary tests.

Restate intent, split the diff by trust boundary, inspect hidden artifacts, run independent tests, and require accountable human approval.

Keep live credentials out of AI tools, and use customer or regulated data only through an approved, minimized workflow with verified controls.

Protect AI-built internal tools with managed identity, per-action authorization, narrow service accounts, audit logs, recovery, and a shutdown owner.

Secure an MCP server with deliberate transport choices, audience-bound tokens, per-tool authorization, session isolation, and execution-time approval.

Protect public AI APIs with object authorization, bounded work, cost controls, constrained tools, safe output handling, and tested failure paths.

Build a lightweight startup threat model from assets, trust boundaries, misuse cases, mitigations, owners, tests, and review triggers.

A six-layer guide to testing AI-built apps, preserving evidence, retesting fixes, and making an honest release decision.

Control Cascade terminal actions, MCP tools, credentials, shared rules, and branch review as Windsurf moves into Devin Desktop.