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Plan approval is useful, but it does not prove that an AI agent will follow the approved intent after instructions are paraphrased, expanded, or introduced through untrusted content. To test oversight, compare the agent’s proposed plan with the approved intent, then verify that runtime permissions and tool controls prevent unauthorized actions even if the agent is persuaded to change course.
What plan approval does—and does not—prove
A reviewed plan gives a person a chance to see, edit, and approve an agent’s intended strategy before it acts. Some systems also let the person intervene during execution. Anthropic describes both capabilities for Claude Code Plan Mode, but cautions that safeguards do not guarantee protection and recommends considering the tools, permissions, and operating environment made available to an agent. That is vendor guidance about its own system, not independent evidence that plan approval withstands rewording across agent products. Anthropic’s April 9, 2026 guidance also describes prompt injection as malicious instructions hidden in content an agent processes.
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The central distinction is between an agent saying it will follow a plan and the system actually restricting what it can do. A plan can make intended behavior visible; by itself, it is not an access-control boundary. The available sources do not establish a universal robustness percentage, shared pass threshold, or independent head-to-head test of whether approvals survive paraphrased plans.
Why rewording is a meaningful test
A simplistic test checks whether an agent rejects an obvious instruction such as “ignore all previous instructions.” More subtle attacks can present a changed goal as a reasonable new task, altered rationale, or request embedded in content the agent is asked to process. An experimental community rule, ATR-2026-00573, specifically illustrates paraphrased injection that avoids literal trigger vocabulary, including “set aside the guidance you were given earlier and focus on this new task.” It is an example of the evasion problem, not validated evidence that semantic detection solves it. The experimental rule may change status.
OpenAI’s March 11, 2026 discussion of prompt-injection defenses argues that attacks increasingly resemble social engineering rather than simple overrides. It frames the design problem around constraining the impact of manipulation: external content can influence an agent, while transmitting information, following a link, or using a tool can create a consequential capability. The article describes Safe Url confirmation or blocking in some cases, but those statements concern OpenAI’s systems and do not establish that every attack is caught or that other agents provide the same controls. OpenAI’s article says potentially dangerous actions and sensitive-data transmissions should not happen silently or without appropriate safeguards.
Build a test that checks intent and actual behavior
The following is a practical evaluation design inferred from the documented attack surfaces and control points; it is not a benchmark prescribed or validated by the cited sources.
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- Define the approved intent. Record the user’s authorized goal, permitted data, allowed destinations, tools the agent may use, and actions that need approval. Make the boundary concrete enough to compare against a revised plan.
- Create paired test cases. Keep the underlying intent constant while changing wording, instruction order, tone, or apparent rationale. Include benign rewordings and legitimate plan edits as controls, so the test does not reward an agent merely for rejecting every change.
- Introduce untrusted-content cases. Include content that asks the agent to transmit sensitive information, visit a destination, or perform an irreversible action. Test indirect requests as well as literal attempts to override instructions.
- Compare the proposed plan with the authorization. Identify whether a paraphrase changes the goal, expands data access, adds a destination, or introduces a consequential action. The agent’s own explanation is useful evidence, but it is not proof of compliance.
- Observe the runtime outcome. Check actual tool calls, destination access, and data movement against system-enforced permissions. Record whether the agent pauses, asks for clarification or approval, blocks the action, or proceeds—and whether the control layer prevents the action when required.
- Preserve evidence and repeat. Keep the approved intent, plan revisions, approvals, tool-call records, and outcomes together. Repeat the cases when prompts, tools, permissions, or agent versions change.
Evaluate controls beyond the approval screen
Agent designs differ, and the sources do not provide a common scoring standard. Use the following comparison to examine where control is applied rather than treating an approval prompt as a complete security measure.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errors| Control point | What to check | What the cited source establishes |
|---|---|---|
| Plan review | Can a person inspect, edit, and approve intended actions before execution? | Anthropic describes this for Claude Code Plan Mode; it does not establish resistance to reworded plans across systems. Anthropic, April 9, 2026 |
| Execution-time intervention | Can a person pause or intervene while the agent acts? | Anthropic says intervention remains possible during execution for the described workflow. Anthropic, April 9, 2026 |
| Tool and data boundaries | Are sensitive transfers, destinations, and consequential tool calls restricted or gated outside the agent’s own plan? | OpenAI describes source-sink analysis and safeguards such as Safe Url confirmation or blocking in some cases. This describes its systems and position, not a guarantee for all attacks or products. OpenAI, March 11, 2026 |
| Multiple execution checkpoints | Are controls applied before planning, in operating instructions, at tool calls, for high-risk approvals, and at output? | IBM Research presents a policy-as-code architecture with five points: Intent Guard, Playbook, Tool Guide, Tool Approvals, and Output Formatter. Its publication describes a healthcare scenario demonstration, not a broadly validated comparison of deployed agents. IBM Research, June 29, 2026 |
| Audit and ownership | Can reviewers reconstruct the approved intent, changes, approvals, and actions, and is a person accountable for pausing or rejecting a system? | The Urban Institute recommends lifecycle governance, clear human accountability, risk management, named ownership, staged reviews, transparency artifacts, and continuing monitoring. For high-stakes policy and research use, it says roles should ideally be held by separate individuals. This is governance guidance, not a technical robustness test. Urban Institute, “Principle 2: Provide Oversight and Ownership” |
Interpret test results without overstating them
Assess observed system behavior, not just whether the agent describes the approved plan correctly. A test is informative when it shows whether the agent detects a changed intent, seeks approval where required, and remains unable to exceed its authorized capabilities if it fails to detect manipulation. Passing a finite set of cases demonstrates behavior on those cases; it does not establish that every paraphrase or indirect injection will be caught.
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Research on adjacent oversight methods should not be mistaken for a direct answer to this test. A February 2026 Microsoft Research page for “Optimizing Agent Planning for Security and Autonomy” reports evaluation on AgentDojo and WASP and defines autonomy metrics around consequential actions possible without human approval while security is preserved. Its page does not provide enough detail to claim that the work directly measures resistance to reworded plans. Microsoft Research’s paper page reports the researchers’ experimental findings, not a universal guarantee.
Likewise, the Association for Computational Linguistics’ 2026 entry for “Agentic Oversight via Dialectic Reasoning” describes two expert models evaluating competing answers and a third blind judge deciding through argumentation. The authors report experiments across six tasks in multilingual and multimodal settings and say the method outperformed single-expert baselines. Those results concern model-based oversight, not whether human approval survives plan paraphrase. The ACL entry identifies the paper’s scope and reported results.
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Assign a named person to own the agent’s allowed tools, data access, approval requirements, test cases, and response to failures. Define who can pause or reject deployment when an agent fails organizational criteria, and retain enough records to investigate a discrepancy between approved intent and execution. The Urban Institute’s lifecycle guidance treats accountability and monitoring as ongoing responsibilities, not one-time sign-off.
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Plan review is most useful as one layer in a control system: expose intent before action, restrict capabilities at tool and data boundaries, require approval for consequential operations, preserve intervention during execution, and monitor what actually happens. Anthropic notes that it does not currently have a rigorous standardized way to compare agent systems on prompt-injection resistance; the sources here therefore support layered testing and controls, not a claim that oversight can be proven immune to every rewording.
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