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Loopjacking is an authorization failure: a person approves operation A, but an AI workflow later releases materially different operation B under that approval. The substitution can happen because the approval screen omitted important fields, or because mutable pending state changed after approval. The fix is to bind approval to a complete, canonical description of the tool call and compare that description with the fully resolved operation immediately before execution.
This distinction matters because an agent can appear to follow a legitimate review process while directing a consequential tool to a different target, amount, principal or side-effect. A human approval is a security boundary only when the product proves that the operation reviewed is the operation released.
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What is loopjacking in an AI workflow?
Loopjacking occurs when a genuine human decision authorizing one operation is consumed by product-owned logic to authorize a materially different operation. The reviewer understands and approves A; the workflow executes B. The attacker does not need to forge a confirmation if the product itself reuses the real approval for the wrong call.
A trace qualifies as loopjacking only when all of these conditions are present:
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- A real person makes an approval decision.
- The effect the person approved differs materially from the effect later released.
- An attacker can influence the path that produces or consumes the changed operation.
- The product, rather than the attacker directly, consumes the approval decision.
- The changed operation reaches a consequential sink such as a transfer, deletion, deployment or privileged API.
- The attacker does not already possess equivalent direct authority to perform the same action.
Ordinary persuasion, a forged “approved” message without a human decision, and prompt injection that never causes a genuine approval to be reused are different problems. They may supply the unwanted operation, but they are not loopjacking unless the approval for A is what authorizes B.
How does loopjacking happen between approval and execution?
The vulnerable interval is the time between what the reviewer sees and what the tool actually receives. A typical sequence has five stages:
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- Presentation: An approval screen, message or API payload shows enough information for the person to decide.
- Decision: The person records approval for A.
- Substitution: Before release, either a lossy representation hides material data about B or attacker-influenced state replaces A with B.
- Consumption: The product sees the old approval as valid and dispatches B to a consequential tool.
The critical missing check is a comparison at use time. The workflow must reconstruct the complete operation after parsing, templating, state reduction, continuation, default insertion, wrapper expansion and argument resolution, then verify that it remains materially equivalent to the operation the reviewer approved.
What are the two forms of loopjacking?
Representation-based loopjacking
In this form, B is already encoded somewhere in the workflow, but the reviewer is shown an incomplete or misleading representation. Hidden wrapper fields, omitted destinations, truncated arguments or an ambiguous summary can make a consequential difference invisible. The approval is genuine, yet it covers only the partial description displayed to the person.
Post-approval state substitution
Here the reviewer sees the correct operation A. After approval, mutable pending state is changed to B before the continuation runs. If the product treats a run ID, call ID or “approved” flag as sufficient authority and does not recheck the current arguments, B inherits A’s approval.
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| Variant | What the reviewer sees | Where the mismatch is introduced | Required control |
|---|---|---|---|
| Representation-based | An incomplete or misleading view of the operation | Before the decision, during rendering or serialization | Render from a canonical descriptor containing every authorization-relevant field |
| Post-approval state substitution | The correct operation A | After approval, while pending state or continuation data is mutable | Protect state and compare the fully resolved operation immediately before dispatch |
Can an AI agent execute something different from what I approved?
Yes, if approval is bound only to a workflow, run, pending flag or opaque call identifier instead of to the complete operation. In controlled demonstrations, A was a mock transfer of 20 units to an approved vendor, while B was a mock transfer of 2,000 units to an attacker-designated sink. Tools recorded exact arguments in a ledger; no production service was contacted and no real assets moved. The security failure was that B could be released under authority granted for A.
“The agent was approved” is therefore not a sufficient audit statement. A useful record must answer which action, arguments, destination, principal, task scope and side-effect class the person reviewed, and must show that those values were unchanged when the tool was called.
Is loopjacking the same as prompt injection?
No. Prompt injection is a way to influence an agent’s behavior; loopjacking describes a narrower authorization break in which a genuine approval for one operation is reused for another. Prompt injection, poisoned memory or session smuggling can provide the attacker-controlled B, but the loopjacking label applies only when the product’s approval path authorizes that B.
NIST’s Center for AI Standards and Innovation defines agent hijacking as “a type of indirect prompt injection in which an attacker inserts malicious instructions into data that may be ingested by an AI agent, causing it to take unintended, harmful actions” (NIST, 2025). That definition covers a broad class of unintended actions. A loopjacking investigation must additionally establish the human decision, the material A-to-B mismatch and the product’s reuse of the decision.
| Question | Prompt injection or agent hijacking | Loopjacking |
|---|---|---|
| Must a person make a real approval? | No | Yes |
| Must the approved and executed operations differ? | Not necessarily | Yes, materially |
| Can a forged confirmation alone qualify? | It may be an attack, but not necessarily loopjacking | No; the product must consume a genuine decision |
| What is the defining failure? | Untrusted data changes agent behavior | Approval for A authorizes B |
What did the 2026 evaluation actually test?
The comparative evaluation by Adithyan Arun Kumar (2026) tested selected products and configurations; it was not a prevalence survey. Its results show that implementations differ, not that all AI agents are vulnerable.
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| System or composition | Versions tested | Observed result |
|---|---|---|
| Agno AgentOS | Seven releases ending at 3.0.9 | Post-approval state substitution reproduced |
| Conditional in-memory LangGraph Agent Server composition | 12 tested versions ending at 0.14.0 | Post-approval state substitution reproduced |
| OpenClaw | 2026.2.23 | Representation mismatch reproduced |
| OpenClaw | 2026.2.24 | Rejection observed |
| OpenAI Agents SDK | 0.22.0 and 0.22.2 | Exact per-call binding preserved; mutated B was rejected |
The negative control is important: resumable or serialized workflows are not inherently unsafe. The decisive property is whether the approval remains bound to the complete operation at the moment of use.
The demonstrations used harmless sinks and exact argument ledgers. They do not establish an ecosystem-wide vulnerability rate, and they do not show that real money or production data was moved. Any claim about other versions, deployments or integrations requires separate testing.
How do I bind human approval to the exact tool call?
Implement approval as authorization for a canonical operation descriptor, not as a general permission to continue a workflow. The descriptor must be the same source used for the approval display, the authorization decision and the final dispatch check.
1. Define the canonical descriptor
Include the action and every field that could change its effect: complete arguments, target and destination, acting principal, task or tenant scope, side-effect class, and any wrapper or routing values that influence the sink. Do not generate the screen from a shortened natural-language summary while the executor reads a separate object.
action=transfer; arguments={amount:20, currency:units, recipient:approved-vendor}; principal=finance-agent; task_scope=invoice-481; side_effect=external-transferThe example is illustrative; the important rule is that the descriptor is complete, deterministic and understandable to the reviewer.
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2. Bind the decision to the descriptor
Store the descriptor itself or a digest over the exact data shown to the reviewer. Record the version of the descriptor schema and the identity of the approving principal. A digest is useful only when it covers all authorization-relevant fields; hashing an incomplete summary merely makes the incomplete summary tamper-evident.
3. Reconstruct the operation at release time
Immediately before dispatch, resolve the current operation after all transformations. Compare it with the approved descriptor or digest. Reject the call and request a new decision if any material field differs, if the principal or task scope changed, or if the side-effect class is broader than the reviewed one.
4. Enforce decision lifecycle rules
Reject decisions that are expired, revoked, already consumed or outside their scope. Mark a decision consumed atomically with release so retries cannot silently turn one approval into multiple executions. If a workflow pauses and resumes, require the same checks after every continuation boundary.
5. Protect pending state
Untrusted participants must not be able to mutate approved state. Use authorization and integrity controls around queues, stores, resumable checkpoints and callback payloads. Treat a shared in-memory object, serialized graph state or client-supplied continuation token as untrusted until verified against the approved descriptor.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should teams compare an AI workflow’s approval design?
Ask five concrete questions rather than accepting a product’s “human in the loop” label.
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| Evaluation axis | Strong implementation | Warning sign |
|---|---|---|
| Representation completeness | The reviewer sees the full canonical operation, including destinations and hidden wrapper fields | A short summary omits arguments, routing or recipients |
| Binding point | Approval is tied to complete arguments and scope | Approval is tied only to a run, call ID or pending flag |
| Use-time revalidation | The current resolved operation is compared immediately before dispatch | The system checks only when the approval was first recorded |
| State mutation control | Pending approved state is protected from untrusted writers | Any participant, tool or client can edit continuation state |
| Evidence boundary | Documentation identifies tested versions and controlled conditions | A single reproduction is presented as a market-wide prevalence claim |
A practical test procedure
- Create a benign operation with a distinctive argument and destination.
- Capture exactly what the reviewer sees and approve it.
- Before continuation, mutate one authorization-relevant field in the pending state: amount, recipient, principal, scope or side-effect class.
- Resume the workflow and inspect the exact arguments received by a test sink.
- Pass only if the system rejects the mutation or requests fresh approval; a successful dispatch of the mutated operation is a failure.
- Repeat with representation truncation, wrapper expansion, default insertion and retry paths.
Keep the sink non-destructive and log arguments in a ledger. A controlled reproduction demonstrates behavior for that version and configuration; it does not prove how often the same issue occurs elsewhere.
What should an operator do after detecting a possible loopjacking event?
Stop further releases using the affected approval path first. Preserve the approval display, canonical descriptor, digest, pending-state history, identity of every state writer, final resolved arguments and sink response so investigators can establish where A became B.
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- Disable or quarantine the affected tool, continuation route or integration without deleting evidence.
- Revoke unconsumed approvals and expire active decisions for the affected scope.
- Compare the approved descriptor with every released call to find material mismatches.
- Identify whether the cause was incomplete rendering, mutable state, a wrapper, a default, a retry or a principal-scope change.
- Patch the binding and use-time comparison, then rerun mutation tests against every resume and retry path.
- Review downstream effects and notify the owners of any consequential sink according to the incident plan.
Do not treat a matching human message as proof that execution was authorized. The authoritative evidence is the complete operation reviewed and the complete operation delivered.
What implementation checklist prevents approval substitution?
- One canonical descriptor drives display, approval storage and dispatch.
- Every material argument, target, principal, task scope and side-effect class is visible or otherwise explicitly represented.
- The approval is bound to the descriptor or a digest covering the identical data.
- Full operation resolution occurs again immediately before release.
- Any material difference causes rejection or a fresh approval.
- Expired, revoked, consumed and out-of-scope decisions cannot be used.
- Pending state and continuation data cannot be modified by untrusted actors.
- Retries, wrappers, defaults, serialization and graph reductions receive the same checks.
- Audit records preserve the reviewed descriptor, decision identity, final arguments and sink result.
- Testing uses harmless sinks and distinguishes controlled reproduction from prevalence claims.
Frequently Asked Questions
Are resumable or serialized agent workflows automatically unsafe?
No. A workflow can pause, serialize state and resume safely when its approval is tied to the complete operation and the resumed state is revalidated before dispatch. The security risk comes from unverified substitution, not from persistence by itself.
Does a cryptographic hash solve loopjacking on its own?
No. A hash protects integrity only for the fields included in it. If the digest covers a truncated summary, omits a destination or is not compared with the fully resolved call, the system can still authorize the wrong operation.
Can a user-interface approval be valid if some arguments are hidden for readability?
Only if the hidden values are fixed by the canonical descriptor and their effect is made clear to the reviewer. Authorization should fail when an omitted value can change the target, amount, principal, scope or side-effect class.
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Show a real human approval for A, attacker-reachable influence that produces B, a material difference between them, and product-controlled release of B to a consequential sink. Record the tested version and configuration; one controlled reproduction should not be presented as an industry-wide rate.
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