What happened
PromptArmor published research showing that ChatGPT for Google Sheets could be manipulated through an indirect prompt injection inside a spreadsheet. According to the report, a single imported sheet could cause the extension to run attacker-controlled Apps Script code, exfiltrate workbook data, display phishing overlays, and edit sheets using the permissions already granted to the extension.
The timing matters because this is not a theoretical chatbot mistake. It is an agentic integration issue: an AI tool connected to a real business application, operating with real permissions, reading untrusted content and taking actions in the same workspace where financial models, budgets and operational data often live.
OpenAI said it has removed the model’s ability to generate Apps Script code for this surface and is re-evaluating sandboxing and related functionality. That response reduces the immediate risk, but the broader pattern remains relevant for every organization connecting AI assistants to documents, spreadsheets, mailboxes and workflow systems.
Why it matters
The important lesson is not simply that one spreadsheet extension had a vulnerability. The lesson is that enterprise AI becomes risky when reading and acting are collapsed into the same trusted path. A model that sees external content, internal data and execution tools at the same time needs runtime controls around scope, approvals, logging and isolation.
Human approval alone is also not a complete control. PromptArmor reports that the attack could succeed even when automatic edits were disabled, because the dangerous action was routed through generated script execution rather than a normal sheet edit. In production environments, approvals need to be tied to specific capabilities and observable actions, not just to a generic “edit” setting.
For document-heavy businesses this is especially relevant. Spreadsheets, SharePoint folders, email attachments and client documents are full of semi-trusted content. If an agent can ingest that content and then call APIs, write scripts or trigger workflows, the runtime has to assume that some input will be hostile or malformed.
Laava perspective
This is exactly why Laava treats agents as production systems, not clever sidebars. The model is only one part of the architecture. The real work is in the runtime around it: permission boundaries, tool policies, audit trails, data residency choices, recovery paths and integration design.
A sovereign or managed runtime is not about selling a box. It is about giving organizations one controlled AI environment for document and workflow operations. That environment can decide which tools an agent may use, which documents it may read, which actions require explicit review, where logs are stored and how incidents can be reconstructed afterwards.
Model-agnostic design also matters here. If a risk appears in one model, connector or vendor surface, a mature runtime should be able to route around it, restrict it or replace it without redesigning the whole business process. The goal is not to avoid AI integrations, but to make them operable and auditable enough for real work.
What you can do
Start by inventorying where AI tools already have access to spreadsheets, document stores, email and workflow systems. Pay attention to extensions and connectors that can execute scripts, edit files, call APIs or create new sharing surfaces. Those are operational capabilities, not harmless chat features.
Then separate reading from acting. Use least-privilege connectors, restrict high-risk tool use, log agent decisions and test with untrusted documents before production rollout. For organizations building agents on top of SharePoint, Google Workspace, ERP or CRM data, the safest path is a managed runtime with explicit integration boundaries from day one.