Defending Against AI Manipulation: Hidden Text in Emails

 The problem runs deeper than one product flaw. Even a hardened email security gateway can’t always spot these hidden text prompts, since they’re not traditional payloads or links. As more teams rely on AI assistants to triage mail, the weak point shifts. Phishing defense now hinges on how the AI assistant app reads and reasons through messages, not just on what the user clicks.

Hidden Text Commands Undermine Normal Email Security 

Hidden text sits quietly inside innocuous email messages. HTML code makes it invisible to humans, but clear as day to machine readers. An AI assistant app parses it line by line, interpreting the hidden layer as legitimate input. Sometimes it summarizes it. Sometimes it follows it. That small shift changes everything.

Early phishing email examples have already shown what this looks like in practice. Attackers can use hidden text to display malicious messages without tipping off email filters. They make the AI model complicit in offering users bad links to click on, which can lead them to malware and phishing sites. The kicker is that users tend to implicitly trust their AI assistant app. The Gemini phishing scams proved how easy it is to mislead users through manipulated AI summaries rather than traditional lures.

The risk grows as AI-driven tools move deeper into enterprise inboxes. Email analysis is no longer just about static content or headers; it’s about how the model itself interprets what it sees. That shift changes the role of defense, especially in the email security cloud era.

How Hidden Text Prompt Injection Works and Why It Matters for AI-Driven Email Security

Attackers use normal markup language to slip commands into messages. The AI assistant app reads that layer as genuine input, not decoration, and executes it during summarization. What looks like a clean message can quietly change meaning behind the scenes. Hidden text becomes an instruction channel.

A Gemini summary displaying an “urgent payroll update” could be pulled from otherwise harmless content. The model doesn’t know it’s lying; it’s just following injected guidance. That makes traditional filters useless. SOC teams are also beginning to analyze the broader operational risks of agentic AI vs generative AI, noting that, even though agentic models can act without a prompt, both types remain vulnerable to adversarial prompting.

This type of attack isn’t just a clever phishing trick. It’s command injection against a new interpreter class, software that reads, reasons, and acts in place of a human. The stakes of this technology are already visible in modern AI phishing scams, but now hackers are learning to turn helpful LLMs against their owners. Reliance on AI is just another email security flaw.

Detection and Prevention with Email Security Gateways

An email security gateway remains the first line of inspection, capable of flagging irregular HTML, hidden text, or abnormal formatting that could mask injected prompts. But the rise of AI-driven email parsing adds new pressure. What the filter misses, the model might execute.

To better protect email, SOC teams should begin testing summarization tools in controlled environments, folding those results into regular phishing training and red team drills. Simulations tied to real phishing attacks expose how an AI assistant app interprets deceptive inputs before they reach users.

While experts work on making AI assistant apps more reliable partners, security teams need to accept that email security gateways can fail, and be ready to limit the fallout of phishing messages in the AI summary pane. Strong defense is layered defense. In addition to a fine-tuned gateway, organizations must use their whole array of tools to block AI-assisted hacking. A great way to coordinate defenses is with a comprehensive email security platform, such as Guardian Digital Engarde Cloud Email Security.

Adapting Email Security For AI Systems 

Even a strong email security gateway can’t stand alone against AI-targeted threats. Attackers have shifted their focus from fooling people to misleading machines. The target now is perception, and how an AI assistant app reads, reasons, and summarizes email content.

Google’s response to the Gemini prompt injection attacks is instructive for how AI assistant apps should adapt to block hidden text prompts and operate more safely:

• Prompt injection mitigation: This defense involves training the AI model to detect prompt inputs and evaluate if they are adversarial before implementing them. The focus is on not contradicting previous user instructions or security rules.
• URL redaction: Gemini can recognize suspicious links, redact them from the summary view, and notify the inbox user to proceed with caution. Whether the URL was present in the actual email text or generated by a malicious prompt, Gemini can block it from view. This also protects against 0-click image rendering exfiltration.
• User Confirmation: Embracing human in the loop (HitL) verification provides a check to adversarial prompt injection by confirming if the user really wants to carry out text instructions for risky actions. This prevents prompts from inflicting immediate damage, like deleting messages or downloading harmful software.

Now, Google’s AI assistant app can detect bad prompts, prevent phishing links from displaying in summary panels, and request human approval for drastic actions. These safeguards are necessary for stopping adversarial prompts from hijacking users’ assistants and inboxes, especially as adoption accelerates in cloud email security environments.

AI Assistant App FAQ

Let’s review the key points on how hidden text prompts undermine AI security, and why email security gateways can help:

Can AI assistants be manipulated with hidden text? 

Yes, if the model reads invisible code as commands.

How does this differ from normal phishing? 

It bypasses human judgment and exploits AI summarization logic.

Do email security gateways stop this? 

Only if they inspect the HTML structure and AI interactions.

Should phishing training include AI use cases? 

Absolutely. Teams need examples where AI interpretation changes the risk profile.

Securing AI Assistant App Technology for Email 

Hidden text attacks expose a growing blind spot in AI-assisted email communication that traditional filters alone can’t close. Even with the tools of Gmail security and other enterprise email platforms, staying protected requires human validation at every step, backed by dynamic threat detection. Advanced AI email security approaches are already moving in that direction.

Real protection depends on collaboration. A hardened email security gateway, a transparent AI assistant app, and continuous phishing training form the full picture, with technology, context, and human oversight working in sync to protect emails.