Description
The current DML orchestration workflow utilizes a two-phase transaction strategy for data safety:
- Phase 1 (Dry-run): Open Transaction A -> Execute DML -> Fetch affected rows -> Rollback -> Await human approval via MCP.
- Phase 2 (Execution): Receive approval -> Open Transaction B -> Execute original DML -> Commit.
While this approach elegantly prevents database locking during human verification, it introduces a classic TOCTOU (Time-of-Check to Time-of-Use) race condition / anomaly under concurrent workloads.
How to Reproduce / The Theoretical Failure Mode
- T0 (Dry-run): AI generates an
UPDATE statement: UPDATE orders SET status = 'Shipped' WHERE id = 100 AND status = 'Pending';. The tool executes this in Transaction A, confirms 1 row affected, and then rolls back.
- T1 (Awaiting Approval): The request goes to the human administrator via the MCP Elicitation UI. During this human think-time (e.g., 10–30 seconds), another concurrent system transaction updates
orders(id=100) or deletes it entirely.
- T2 (Actual Execution): The human clicks "Approve". Transaction B opens and executes the exact same statement.
Actual Behavior / Consequences
Because the underlying snapshot or state of the database mutated between T0 and T2, Phase 2 might result in 0 rows affected (silent no-op), or worse, mutate data based on stale contextual assumptions that the human approved but are no longer valid.
Suggested Solutions
To mitigate this data-drift anomaly, we could consider:
- Optimistic Concurrency Control (OCC): During Phase 1 (Dry-run), automatically append or track the
Version / Timestamp / RowVersion of the scanned rows, and enforce those criteria in Phase 2.
- Post-Execution State Verification: Compare the actual affected row count or state change of Phase 2 against the captured metrics of Phase 1. If a mismatch is detected, abort/rollback Transaction B and flag an automated warning.
Description
The current DML orchestration workflow utilizes a two-phase transaction strategy for data safety:
While this approach elegantly prevents database locking during human verification, it introduces a classic TOCTOU (Time-of-Check to Time-of-Use) race condition / anomaly under concurrent workloads.
How to Reproduce / The Theoretical Failure Mode
UPDATEstatement:UPDATE orders SET status = 'Shipped' WHERE id = 100 AND status = 'Pending';. The tool executes this in Transaction A, confirms1 row affected, and then rolls back.orders(id=100)or deletes it entirely.Actual Behavior / Consequences
Because the underlying snapshot or state of the database mutated between T0 and T2, Phase 2 might result in
0 rows affected(silent no-op), or worse, mutate data based on stale contextual assumptions that the human approved but are no longer valid.Suggested Solutions
To mitigate this data-drift anomaly, we could consider:
Version/Timestamp/RowVersionof the scanned rows, and enforce those criteria in Phase 2.