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Lab idea / Adversarial AI

Will an approval gate hold when the context changes?

Test authorization at the tool boundary using synthetic users and a mock downstream service.

RedLatent editorial
Published 2026-10-26 · Updated 2026-10-26 UTC

Untested hypothesis — not a reported experiment

Falsifiable hypothesis

An external approval and scope gate prevents unauthorized mock tool actions without blocking permitted clean tasks.

Source-led motivation

Official implementation guidance on MCP-specific security risks; the documentation explains confused-deputy risks in proxy authorization flows, while the draft specification additionally discusses token passthrough, SSRF, and state-handle authorization.

[mcp]

Research benchmark and environment for evaluating prompt-injection attacks and defenses against LLM agents, with configurable task suites, attacks, and defenses.

[agentdojo]

Authorized lab scope

A staging agent, synthetic documents, synthetic users, and a mock API with no real-world side effects.

Prerequisites

  • A written authorization matrix and allowlisted mock actions.
  • A no-attack baseline and fixed task set.
  • Pinned protocol, agent, and model versions; synthetic credentials only.

Comparison & control

Compare clean tasks, tasks exposed to untrusted retrieved text, and the same tasks with an external gate. Do not rely on the agent’s own claim that an action was safe.

Observations to collect

  • Actual allowed/denied actions at the mock API.
  • Legitimate task completion and unnecessary approval requests.
  • User identity, consent, and target scope for each proposed action.

Disconfirming evidence

  • An out-of-scope action reaches the mock API, or clean-task completion collapses under the gate.

Stop conditions

  • Stop if any action reaches an unapproved target, real user data, or a non-lab service.
  • Stop on unexpected egress, exposed credentials, or an exhausted predeclared budget; retain a sanitized failure record.

Source bibliography

  1. AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents ↗

    ETH Zurich and Invariant Labs research authors

    Publication date: Date not supplied · Editorial check 2026-10-26 UTC

    Project README / official documentation; source-led editorial check, no execution
  2. Security Best Practices ↗

    Model Context Protocol project

    Publication date: Date not supplied · Editorial check 2026-10-26 UTC

    Project README / official documentation; source-led editorial check, no execution

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