What is Multimodality in AI?
Text, Image, Voice
Multimodal AI is everywhere, as AI is evolving beyond text. With voice, images, and video entering the mix, the risks are growing exponentially. Hackers are already exploiting these new vulnerabilities. Are your AI systems ready?
Multimodal AI refers to an AI system’s ability to process and generate information across multiple types of data (modalities), such as text, images, and voice. With recent releases of multimodal models like Phi-4-Multimodal and DeepSeek-AI/Janus-Pro-7B, organizations are rapidly integrating voice and visuals into their AI systems. But with this evolution comes new security challenges that traditional safeguards can’t handle.
Why Multimodal AI Matters
Improve user experience across any use case
Highlighted below are example use cases where multimodal AI improves user experience.
The Rising Risk of Multimodal AI
Multimodal systems are inherently more vulnerable than their unimodal counterparts, as they are susceptible to attacks leveraging input methods such as image, text-to-image or voice exploitation.
As illustrated in Figure 1, multimodal AI systems are designed to protect against individual text inputs. However, they become vulnerable when users leverage image, text/ image combinations, or voice prompts for exploitation.
Key Risks in Multimodal AI
1. Security Risks – Increased Attack Surface
· Cross-Modal Attacks: A security measure that works for one modality (e.g., filtering harmful text) may not work when an attack is hidden in another format (e.g., encoded in an image or voice).
· Bypassing Safeguards: If a model is trained primarily for text-based safety, attackers can exploit voice commands or embedded instructions in images to circumvent restrictions.
· Example: A chatbot designed to reject harmful text queries might still execute the same command if spoken as audio or embedded in an image.
2. Bias & Ethical Concerns – Compounded Biases
· Multi-Source Bias: If a model is trained on biased text data and biased image datasets, it reinforces discrimination across multiple modalities.
· Lack of Cross-Modality Alignment: Ethical constraints in one modality may not translate well into another, leading to unintended biases.
· Example: A job recruitment AI could favor certain accents in voice interactions while also showing gender/race bias in image recognition.
3. Privacy & Data Leakage – Unintentional Exposure
· Hidden Data in Images/Videos: Text embedded in an image (e.g., a screenshot of sensitive financial data) could be processed and leaked without proper safeguards.
· Voice & Text Cross-Leakage: If a system processes both voice and text, it might inadvertently extract, store, or expose PII that wasn't intended for public use.
· Example: A customer support AI that transcribes voice calls might store credit card details unintentionally, leading to data breaches.
Enkrypt AI Solution and Features
Dual approach detects and removes multimodal AI threats before and during production.
1. Enkrypt AI Multimodal Red Teaming
2. Enkrypt AI Multimodal Guardrails
Multimodal AI Red Teaming: (detect risks)
Blended attack methods to safeguard against:
- Security | Bias | Privacy
- Compliance Violations: (NIST, OWASP, EU AI Act)
Multimodal AI Guardrails: (remove risks)
High accuracy, low latency protection against:
- Security | Bias | Privacy | Hallucinations
- Compliance Violations: (NIST, OWASP, EU AI Act)
Red Teaming Solution for Multimodal AI
Enkrypt AI's Multimodal Red Teaming Agent (see Figure 2) helps identify security vulnerabilities in following modalities and their combinations.
1. Image + Text
2. Voice + Text
3. Voice
4. Image
5. Text
Our Red teaming Agent adapts to the conversations ensuring security for multi-turn attacks. The agent evaluates AI systems across nine critical risk categories (see Figure 3), including CSAM, self-harm, fraud, and hate speech, ensuring AI safety measures are comprehensive and effective. It performs customized red teaming using a mix of attack techniques to address various threats. It covers security assessments for standards like NIST, OWASP, and the EU AI Act.
Guardrails Solution for Multimodal AI
Enkrypt AI’s Multimodal Guardrails offer real time detection and response across image, voice, and text attacks while ensuring high accuracy with low latency. We enhance security and compliance by detecting custom policy violations, redacting PII, and mitigating bias without false positives. With long-context support, we handle larger images and extended conversations.
Video: AI Voice Guardrails
Check Enkrypt AI Multimodal Guardrails in action for a Restaurant Reservation Agent.
Our Multimodal Guardrails Dashboard continuously monitors Multimodal AI systems with attack breakdown, trends and latency information. See Figure 4.
Why Enkrypt AI’s Approach to Securing Multimodal AI is Different
Our dual approach solution (Red Teaming and Guardrails) covers multiple modalities, including image, voice, and text, ensuring comprehensive security testing across AI systems. The automated Multimodal Red Teaming Agent conducts rigorous evaluations across diverse risk categories, utilizing a mixture of attack strategies to uncover vulnerabilities effectively. With seamless integration via API and SDK, AI developers can easily incorporate these security measures into their systems, enabling continuous monitoring and proactive risk mitigation.
Conclusion: Secure Your Multimodal AI Today
With AI evolving into voice- and image-based systems, enterprises must act now to prevent security breaches, bias, and compliance risks. Enkrypt AI’s Red Teaming and Guardrails provide industry-leading protection to ensure safe AI adoption.
Ready to secure your multimodal AI? Contact us today to explore how Enkrypt AI can protect your systems.
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