Multimodal AI Security: Why It’s Harder to Secure Than Traditional AI.

Multimodality is creating a new space & a completely new UX paradigm in the world of artificial intelligence. As the most significant leap of the industry, it sees what we see, hears what we hear, and responds just like a human. Multimodal AI models aren't mere LLMs but reasoning engines that can reason across modalities.

While the future of AI is multimodal, its exponential capabilities also bring in more risks and vulnerabilities. The fusion of multiple data streams doesn't simply boost the model’s intelligence, but also increases the attack surface.

But, does it mean that we shouldn't implement multimodal AI? Not! With amplified intelligence and profound use cases, we just need to focus on securing it & its implementation to lessen the stakes.

This blog will deal with multimodal AI’s security. We’ll explore why it's working and why it's difficult to secure it. Diving deep, we’ll also discuss securing these multimodal AI systems & models.

‍

What Is Multimodal AI? How Are These Models a Game-Changer?

‍

In simple terms, multimodal AI refers to those AI models & systems that can handle, process, and interpret all data types, forms, and modalities. Unlike unimodal systems such as GPT for text, these systems can take input and process output in any form - text, images, audio, video, and even sensory data.

With the ability to break single-channel limitations, their ability to fuse data generates more flexible, precise, and context-rich outputs. But what makes context so important? Context helps the model understand your needs and deliver more holistic results. The more contextual understanding a model has, the better it can understand your prompts and the more satisfied you’ll be with its results.

While these intelligent models are built to process different data modalities, they mainly deal with text, image, audio, and video modalities. As one of the most commonly used modalities across all AI models, text modality is often used for text classification, language translation, and sentiment analysis.

Similarly, image modality is used for recognising objects, decoding facial expressions, and other tasks requiring visual understanding. Audio modality is used primarily for speech recognition, whereas video modality is used for numerous applications ranging from video captioning to summarization.

Open AI’s GPT-4o, Google’s Gemini & Meta’s ImageBind are prominent examples of multimodal AI in action. But what makes these systems or models a complete paradigm changer?

• These models can mimic human-like understanding. They can see, hear, understand, and process information across senses, which makes their interactions and responses more intelligent, intuitive, natural, and responsive.
• With their improved access to various inputs, they're known to offer deep insight, making them useful in real-world complex environments like autonomous driving or secure monitoring.
• Besides bridging the sensory gap, these models are more context-aware with more nuanced perception and decision-making. Nevertheless, they've better understanding and wise decision-making skills like humans.
• These systems are becoming the powerhouses of innovation due to their multi-step reasoning abilities, which is why they are increasingly adopted in next-gen applications.

‍

Types of Multimodal AI

‍

Multimodal AI systems are diverse - their diversity lies not just in their architectures but also in the unique ways and approaches they handle interactions between the models. Based on how they combine data from various sources, they've been classified into 5 major types. Let's understand each of them.

‍

1. Fusion-Based Multimodal Models

Models based on fusion-based architecture data are combined from multiple sensors and points (early, mid, and late stages of the pipeline). Such models are powerful in capturing cross-modal dependencies while being sensitive to adversarial attacks in every modality or entry point.

For example, Self-driving cars with autonomous vehicle navigation use this approach wherein the vehicle uses GPS data, LIDAR readings, and radar signals all at once to make the car aware of its nearby surroundings.

‍

2. Aligned Multimodal Models

As the name suggests, such models process the representations for each modality, ultimately aligning them to their specific part in a separate semantic space. These models are known for their cross-modal retrieval and zero-shot classification.

For example, CLIP by OpenAI is an excellent example of aligned multimodal AI, which brings together similar image and text embeddings close in a vector space.

‍

3. Coordinated Multimodal Models

Such models follow a segmented approach wherein they learn each data type separately, followed by a summarised decision. They're ideal for ensuring cross-modal consistency and coordination, wherein the interpretation of each model affects the others.

For example, A surveillance system that uses audio and video feeds wherein data inputs inform and correct each other in real-time.

‍

4. Generative Multimodal Models

Like they sound, these models can generate new outputs from existing data inputs in any modality - text-to-image (DALL-E) and image-to-text (Flamingo). The best part about these models is that they are incredibly creative. However, they can also fall into the vulnerabilities of deepfakes, misinformation, etc.

For example, let's say you prompt the latest version of GPT with “Create a story about a young boy facing struggles after his father passes away”. GPT goes beyond delivering a story to create an image illustrating all the physical and mental struggles, justifying the story, and providing an imagination turned into a real image.

‍

5. Interactive Multimodal Models

These models are built for real-time interaction with humans, dynamically handling and responding to multi-modal inputs in a conversational or task-based context.

For example, GPT-4o can take an image, listen to your voice, and reply in natural language—all in one coherent interaction loop.

‍

Key Security Risks & Vulnerabilities Associated With Multimodal AI

‍

Besides being more innovative, flexible, and versatile than unimodal AI counterparts, multimodal AI models are riskier and inherently vulnerable to risks. Let's understand some of the key security risks associated with these models.

‍

1. Data Poisoning

Multimodal AI systems are data-hungry, which makes them vulnerable to data poisoning attacks. As they accept complex data from multiple streams, injecting corrupted or adversarial data is possible across any modality. This can corrupt the model's learning process and internal world understanding.

‍

2. Prompt Injection & Cognitive Hacking

These models primarily interact with end-users in real time, making them much more susceptible to input hijacking. A subtle phrase or altered image can bypass filters, confuse intent, or trick the model into dangerous outputs. The more human-like the system, the more susceptible it becomes to manipulation.

‍

3. Image Perturbation

While these modern AI models can see things that human eyes can't, they may fall vulnerable to a well-crafted adversarial image wherein malicious content is encoded as pixels and fit into images. Most popularly used against vision-language models (VLM), these images manipulate the mode, thus tampering with their reasoning and output.

‍

4. Audio Manipulation

In interactive, real-time multimodal interactions, audio manipulation happens by injecting manipulated audio clips wherein the content stays the same while silently delivering hidden malicious prompts. These audio perturbations can fool transcription systems, impersonate trusted voices, bypass biometric protection, and create life-threatening havoc in systems, vehicles, and assistants where audio & voice commands become central inputs.

‍

5. Meta-data Manipulation

Multimodal systems rely heavily on metadata, often less taken care of than core inputs. Attackers can manipulate or edit the metadata, injecting malicious content into it. This confused the models, misleading the context and proving catastrophic in high-stakes industries like defense, forensic analysis, etc.

‍

6. Synchronisation

While these real-time models depend more on perfect synchronisation of inputs, attackers can exploit this attack surface. If they break or manage to synchronize at least one modality input, they can mislead the entire system and spoof it out of sequence.

‍

Consequences of Security Risks of Multimodal AI

‍

The vulnerabilities of multimodal AI are consequential as their failure goes beyond the code to influence decisions, disrupt operations, damage reputation & even endanger lives. Let's understand some of the insecure multimodal AI's real, high-stakes fallout consequences.

‍

1. Distorted Reasoning

Imagine a multimodal AI assistant that misreads an X-ray, leading to a false patient narrative with utmost confidence. This misdiagnosis can even cost the patient's life. Thus, when adversarial inputs successfully manipulate even one modality, the system is fundamentally flawed, producing wrong outputs with unmatched confidence.

‍

2. Trust Collapse

When users realise these human-like interacting systems can be manipulated, hijacked, or misled, their trust erodes. In the age of deepfakes and manipulated prompts, users no longer know whether to speak to a helpful assistant or a hacked interface.

‍

3. Economic and Reputational Impact

Let's say a manipulated multimodal AI has been implemented in the business's customer service pipelines. Can you imagine how the irrelevant and unsafe responses produced by the model can cause backlash, regulatory scrutiny, and reputational damage to the business?

‍

4. Regulatory and Legal Consequences

To multimodal systems handling sensitive information such as biometric data, security failures can trigger privacy breaches, non-compliance, and regulatory and legal blowbacks. With the rise in tightening of the regulations, such security failures are legally indefensible.

‍

How to Mitigate These Risks?

‍

According to Gartner’s research, the number of companies using multimodal AI is expected to increase from 1% in 2023 to 40% by 2027. With its rising adoption, let's explore how businesses can securely deploy it in their daily operations.

‍

1. Secure Every Modality

The attack surface also increases with the additional capability to process various data modalities. Every input image, text, audio, and metadata creates a new potential attack surface.

Focus on securing every modality, not just independently, but also in fusion with specific adversarial training. Furthermore, implement modality-level anomaly detection and reinforce your fusion layers for consistency and explainability of the outputs.

‍

2. Cross-Modal Consistency Validation

While multiple perspectives in multimodal AI are an advantage, it can also be a significant disadvantage. Cross-modal alignment checks and semantic consistency are applied to ensure the consistency of responses. In generative systems, reverse-engineer inputs from outputs to validate coherence.

‍

3. Deploy Prompt Hardening & Input Sanitization

While text-based adversarial attacks are the most common, it's hard to trust textual inputs. Guard every word by maintaining memory boundaries and implementing instruction filtering, strict parsing, and input role separation. To detect jailbreaks, you can also implement context-aware prompt validation.

‍

4. Protect Metadata Integrity

One of the silent victims of manipulated data is corrupt metadata. If you don't know where the response comes from, you can not trust it. To protect the integrity of the model's metadata, implement digital signatures and cryptographic checksums. Secure device-level and track its lineage before it ever reaches the model.

‍

5. Red Teaming

You don’t know how your system will break until you try to break it. Conduct multimodal red teaming exercises with domain experts to simulate attacks across combined modalities. Regularly test the system against known adversarial patterns (e.g., image noise, inaudible voice commands, poisoned EXIF data).

‍

6. Implement Guardrails

Guardrails help detect anomalies across various data modalities in real-time, promising low latency & high accuracy. Nevertheless, they help ensure regulatory compliance and mitigate bias without false positives.

‍

Conclusion

‍

Smart AI isn’t just intelligent—it’s self-improving. So should your security. Deploy real-time multimodal input logging and auditing to monitor unusual combinations or behavior patterns. Create human-in-the-loop review checkpoints for high-risk decisions or anomaly triggers. Build adaptive retraining pipelines that evolve models based on detected threats. It's because securing multimodal systems isn’t a one-time patch—it’s an ongoing process.

‍

FAQs: Multimodal AI Security

‍

1. What is multimodal AI?

In simple terms, multimodal AI refers to those AI models & systems that can operate, process, and interpret all data types, forms, and modalities. Unlike unimodal systems such as GPT for text, these systems can take input and process output in any form - text, images, audio, video, and even sensory data.

2. What is the difference between generative AI and multimodal AI?

The key difference between generative AI and multimodal AI is that while the former is known for creating content, the latter is known for processing multiple data types and modalities.

3. What is the primary advantage of multimodal AI over unimodal AI models?

Multimodal AI models can handle and process various data types, which makes them perfect for contextually-intensive tasks requiring more versatility, reasoning, decision-making skills, and nuanced analysis.

4. What are the challenges of multimodal AI?

While multimodal AI is the promising future of AI, it comes along with its own set of challenges: data quality, consistency, data privacy, bias, ethical concerns, data fusion, and resource intensity.

5. What are the security risks associated with multimodal AI?

Some common security risks associated with multimodal AI are: adversarial attacks, data privacy and breaches, system exploitation and vulnerabilities, legal and reputational risk, and ethical risks.

‍