How can hidden text in Word documents manipulate Microsoft 365 Copilot?
A new class of attack exploits the way Microsoft 365 Copilot processes text by embedding malicious instructions as white text on a white background within Word documents. These instructions are invisible to human users but read by Copilot since it strips text formatting before analysis. This allows hidden commands to instruct Copilot to alter the content it is drafting — for example, modifying key figures in a report — without alerting the user.
Moreover, the attack payload includes a mechanism that makes Copilot copy the malicious instructions into the newly generated document as hidden text. This means every infected document becomes a carrier, capable of infecting subsequent documents processed by Copilot, enabling the attack to spread silently across normal workflows without needing macros, exploits, or executable code.
Why does this attack pose unique security challenges?
This technique belongs to a family of prompt injection attacks where natural language instructions manipulate AI behavior. The stealthy nature arises because the user cannot see the hidden commands and because Copilot itself perpetuates the attack by replicating the malicious prompt in its output.
The attack also demonstrates the difficulty of securing AI assistants against untrusted content. Because Copilot analyzes all text indiscriminately to understand context, malicious input becomes part of the AI’s evaluation process, effectively tampering with how it interprets and generates content. This creates vulnerabilities difficult to patch without redesigning AI interactions.
What precautions should users and organizations take now?
Until a fully effective mitigation is available, users should treat documents from unknown or external sources as untrusted when using Copilot. Before incorporating attachments or shared files into AI-assisted workflows, thoroughly review their contents for suspicious or hidden elements.
Additionally, carefully examine Copilot’s outputs before sharing or relying on them for decision-making, especially for critical documents such as financial reports. Organizations should maintain updated software versions and employ multiple layers of security protection to reduce exposure.
In the long term, incorporating provenance metadata into AI-generated documents to record changes and source origins could help trace infections after they occur, enhancing auditability and response capability.
What is the takeaway on protecting your workflows from AI prompt injection attacks?
This emerging vulnerability highlights the complexity of securing AI-powered tools integrated with document workflows. The ability of hidden text to manipulate AI assistants and propagate without visible signs challenges traditional threat detection methods.
Users need to adopt scepticism towards files, enhanced document review practices, and layered security defenses. Until AI systems incorporate robust safeguards to distinguish and isolate malicious instructions, human vigilance remains critical to prevent these AI-enabled worms from causing havoc across documentation processes.