Context: Questions from the Validation Floor
After news broke about attackers using invisible HTML to manipulate AI-powered email summarizers, questions flooded our Slack channels and team meetings. Model risk managers, validation teams, and security leads all sought practical guidance on what this means for their AI systems and validation processes.
The attack is simple. Invisible HTML acts as a hidden prompt, steering the AI's output without the user ever seeing the manipulation. It doesn't require breaking encryption or exploiting vulnerabilities. It just works. This simplicity makes these questions urgent.
Q1: Is This a Model Risk Issue or Just a Security Problem?
It's both. The vulnerability arises from how the model processes input. The hidden HTML acts as an adversarial prompt, which the model treats as a legitimate instruction, overriding the actual email content in its summary.
From a model risk perspective, this falls under SR 11-7's operational risk category. Your model is producing incorrect outputs based on manipulated inputs, which could drive business decisions or user actions. This is exactly what model risk management aims to catch.
It's also a security issue. MITRE ATLAS classifies prompt injection as AML.T0051, an adversarial ML technique. Your security team needs to be involved, but they can't solve it alone. The model itself needs validation controls to test for this vulnerability.
Q2: Don't We Already Test for Adversarial Inputs? What Did We Miss?
You probably test for adversarial examples in the traditional sense: inputs designed to cause misclassification or trigger specific model behaviors. What you might not be testing for is adversarial instructions embedded in legitimate-looking content.
The difference matters. A traditional adversarial example tries to fool the model's pattern recognition. A hidden prompt attack exploits the model's instruction-following behavior. If your AI system processes natural language instructions, it's vulnerable to unintended instructions.
Your existing adversarial testing likely focuses on:
- Input perturbations causing misclassification
- Boundary cases exposing training gaps
- Data poisoning scenarios
You need to add:
- Hidden instruction injection across different encoding methods (HTML, Unicode, whitespace)
- Conflicting instruction scenarios where legitimate and malicious prompts compete
- Context window manipulation where adversarial content is positioned to maximize influence
Q3: How Do We Test for This Without Building a New Framework?
Start with your existing Red Teaming process and add specific test cases. You don't need a new framework, just new scenarios.
Create a test suite that includes:
- Emails with HTML comments containing contradictory instructions
- Content with zero-width characters embedding commands
- Messages where formatting tags contain prompt injection attempts
- Inputs that combine legitimate content with hidden instructions in various encodings
Run these through your model in a controlled environment and compare outputs against a baseline. If hidden content changes the summary in ways that don't reflect the visible email, you've got a vulnerability.
Document this as part of your Validation Evidence under ISO/IEC 42001's control 6.2.4 (AI system validation). You're testing for robustness against adversarial manipulation, which is required for high-risk systems under the EU AI Act's Article 15.
Q4: Our Email Summarizer Is a Third-Party API. Can We Validate This?
Yes, but your approach changes. You're validating the system's behavior in your context, not the model itself.
For third-party AI services, focus on:
- Input sanitization before content reaches the API
- Output validation that flags suspicious summaries
- Monitoring for summary-to-content mismatches
- Contractual requirements that the vendor performs adversarial testing
Request the vendor's adversarial testing documentation. Under SR 11-7, you're responsible for ongoing monitoring even if you didn't build the model. If they can't demonstrate they've tested for prompt injection vulnerabilities, that's a gap in your AI Supply Chain Compromise controls.
Implement your own detection layer. Build a simple comparison that checks whether the summary includes information not present in the visible email content. It won't catch everything, but it'll flag the most obvious manipulations.
Q5: What Compliance Requirements Cover This Vulnerability?
Several frameworks address adversarial robustness, though none specifically mention "hidden HTML in email summarizers."
The EU AI Act Article 15 requires high-risk AI systems to be "resilient as regards errors, faults or inconsistencies" and "robust as regards attempts to alter their use or performance." If your email summarizer influences business decisions or user actions in a high-risk context, this applies.
ISO/IEC 42001 control 6.2.4 requires validation that includes "robustness to perturbations." Adversarial prompts are perturbations.
NIST AI RMF's MEASURE function calls for "AI systems are resilient to adversarial attacks." The AI RMF Playbook mentions testing for "adversarial perturbations and attacks."
For regulated financial institutions, SR 11-7 requires ongoing monitoring for "changes in product complexity" and "new uses of models." If you've deployed an AI summarizer without testing its response to adversarial inputs, you're not meeting the standard's validation requirements.
Q6: Should We Disable HTML Processing Entirely?
That's one control, but it's not enough on its own.
Disabling HTML processing helps, but attackers can use other encoding methods. Unicode contains invisible characters. Whitespace can hide instructions. Even plain text can contain prompts that the model interprets as instructions if positioned strategically.
A defense-in-depth approach works better:
- Strip or sanitize rich formatting before content reaches your model
- Implement input validation that flags suspicious patterns
- Add output validation that compares summaries against source content
- Monitor for drift in summary characteristics that might indicate manipulation
- Log discrepancies for security review
Document your control choices in your Technical Documentation (Annex IV) if you're EU AI Act-regulated. Explain what adversarial scenarios you tested, what controls you implemented, and what residual risk remains.
Q7: How Often Should We Retest for These Vulnerabilities?
Every time you update the model or change how it processes input. Also whenever new adversarial techniques are published.
Set up a schedule:
- Quarterly adversarial testing using your established test suite
- Ad-hoc testing when new prompt injection techniques are disclosed
- Regression testing whenever you modify input processing or model parameters
- Annual comprehensive review of your adversarial robustness controls
This aligns with ISO/IEC 42001's requirement for continual improvement in your AI Management System. Adversarial ML is an active research area. New techniques appear regularly. Your testing needs to keep pace.
Where to Go for More
MITRE ATLAS provides a taxonomy of adversarial ML techniques, including prompt injection. NIST AI 100-2 offers terminology and frameworks for adversarial ML. For practical testing approaches, review your organization's Red Teaming documentation and add prompt injection scenarios to your existing process.
If you're working with third-party AI services, start with your vendor's security documentation and ask specific questions about adversarial testing. Generic "we follow security practices" answers aren't sufficient. You need evidence they've tested for the specific vulnerabilities that matter to your use case.
