I Built AI Into Our Workflow. Now I'm Worried About What Comes Out.

Last month, I was sitting with our product team during a sprint planning meeting when someone casually mentioned they'd asked Copilot to summarize a client contract and then used that summary as a base for our internal documentation. Seemed normal enough. Then I thought: what if that contract had been compromised? What if Copilot just propagated a payload through our entire knowledge base without anyone noticing?

That question stuck with me harder than I expected. I'd been mostly comfortable with the idea that AI assistants were powerful tools, not security risks. But the more I dug into how prompt injection attacks work at scale, specifically through document-based AI worms, the more I realized I'd been thinking about this wrong. We're not just talking about someone tricking ChatGPT into acting weird. We're talking about self-replicating attacks that turn the tools we trust into vectors for data theft.

This isn't alarmism. This is a real architectural problem in how we've built modern document workflows around AI. And if you're shipping anything that touches Word, SharePoint, or cloud-based documents in 2025, you need to understand what's actually at risk.

The Problem Nobody Expected: AI as an Attack Surface

Here's what I didn't fully grasp before: LLMs don't have a reliable way to distinguish between "content I should read" and "instructions I should follow." When Copilot processes a document, it treats hidden text, white-on-white, in comments, in metadata, as legitimate input to be acted upon.

An attacker embeds instructions in a document. Copilot reads them. Copilot executes them. And here's the scary part: Copilot can then be instructed to embed those same instructions into every new document it creates afterward.

It's not a bug in the traditional sense. It's an architectural vulnerability. The AI is working as designed, it's following the text in front of it. The problem is that "text in front of it" can be invisible, engineered, and malicious.

How This Actually Propagates (Without Anyone Noticing)

I want to be concrete about the chain of events because it's more insidious than most people realize.

Stage 1: Someone sends you a "normal" document through email or uploads it to SharePoint. Looks fine. No suspicious links. Passes email filters.

Stage 2: You open it and ask Copilot to summarize or rewrite it, something you do dozens of times a day in a typical workflow.

Stage 3: Copilot processes the hidden instructions embedded in that document. It's programmed to follow user intent. The hidden text is text. It executes.

Stage 4: The malicious prompt tells Copilot to exfiltrate data (financial figures, email addresses, contract terms) and embed it in new documents or send it somewhere it shouldn't.

Stage 5: Every document Copilot touches after that carries the same payload forward. Your team creates reports, summaries, meeting notes, all infected. All ready to compromise the next person who uses Copilot on them.

The thing that keeps me up about this: your traditional security tools don't see it coming. No executable file. No suspicious URL at send time. DLP tools watch for data leaving through known channels; they don't watch for data exfiltration through AI-generated text. It's operating inside legitimate enterprise software.

My Take: We Built This Problem Into Our Architecture

I need to be honest here: we made this bed ourselves. By bolting powerful AI assistants directly into document workflows without architectural safeguards, we created an attack surface that didn't exist before. And we're doing it because the feature is undeniably useful.

The question I keep asking is: what were we expecting? We gave AI assistants the ability to read, generate, and modify documents automatically. We told them to follow the instructions in those documents. And then we acted surprised when researchers showed that those instructions could be malicious.

I'm not saying we should abandon Copilot for Word or cloud-based AI workflows. I'm saying we need to think about this differently. We need isolation. We need verification. We need the ability to audit what instructions an AI is actually following in production.

What would I do differently if I were designing this from scratch? I'd build a layer between the document input and the AI's instruction set. I'd make hidden text visible in a processing context. I'd implement signed instruction verification, making sure an instruction actually came from where we think it came from.

What We Actually Need Right Now

Practically speaking, here's what I'm pushing my team toward:

• Treat AI-processed documents with the same scrutiny as untrusted external input
• Implement document signing and verification workflows before AI processing
• Audit AI outputs in sensitive contexts, especially when data is being extracted
• Isolate AI sessions when processing high-risk documents
• Monitor for patterns that look like data exfiltration through generated content

This isn't bulletproof. But it's better than the current state, which is basically hoping nobody notices the vulnerability.

The real problem? There's no clean architectural fix yet. Microsoft knows about this. Researchers know about this. And we're all still waiting for a solution that doesn't cripple the utility of these tools.

What's Your Risk Threshold?

Here's my question for you: if you're using AI-integrated document tools in production, how are you actually thinking about the attack surface? Are you assuming your AI assistants are security-neutral? Or are you treating them as potential vulnerability vectors?

Because I'm increasingly convinced we need to be in the second camp.

Source: This post was inspired by "AI Worms in Word: How Document-Borne Threats Self-Propagate" by Dev.to. Read the original article