Securing Multimodal AI

As AI systems integrate into critical systems, they become prime targets for adversarial attacks. Unlike conventional software, AI processes vast amounts of unstructured data—text, images, and audio—making security threats harder to detect and mitigate. An insecure AI system can mislead users, leak sensitive information, or even spread misinformation. 

‍

The threat landscape is further widened as we move onto multimodal AI. These systems introduce multiple modalities, each carrying its complexities and vulnerabilities. Hackers can manipulate the different modalities and force models to generate unexpected and unwanted outcomes. 

‍

This blog will explore multimodal AI and its security challenges, as well as attack scenarios and mitigation strategies.

‍

What is Multimodal AI

Data modality refers to the form or structure in which information is captured. For example, text, images, and numbers are all data modalities that capture information in different forms. Similarly, a multimodal AI system can simultaneously process information from different modalities and provide an output.

The various modalities help the model understand the context and its environment better. AI decisions resulting from such a comprehensive analysis are well-informed and more trustworthy. Moreover, different modalities can occur in the input, output, or both.

‍

Multimodal vs Unimodal AI

Traditional AI systems are mostly unimodal, i.e., trained to process only one data type. A key example of such a system is large language models (LLMs), which process text data as input and produce the same output. This limits their understanding and can hinder their performance. For example, you may describe a location to a language model, but it may get confused as the description may be too generic and lead to multiple possibilities.

A multimodal system can process multiple modalities simultaneously, allowing the model to understand the context better. For example, a vision-language model (VLM) can process both an image and a textual description, allowing for enhanced understanding. The diverse information enables the model to construct a better response.

‍

‍

Security Threats to Multimodal Agents Systems

While Multimodal systems offer enhanced capabilities, they also introduce a broad range of security vulnerabilities. They are prone to all the traditional AI challenges, such as data security and LLM jailbreaking. In addition, each modality brings a unique set of challenges, as hackers can inject malicious content with multiple techniques. Let’s discuss some popular attacks for multimodal systems.

‍

Types of Attacks

‍

Image perturbations

An image perturbation attack involves encoding malicious content as pixels and fitting these pixels into images. These introduce slight anomalies in the image data that are not detected by the human eye but force AI models to execute certain actions. Such attacks are popularly used against vision-language models (VLMs), where the user submits a manipulated image that triggers specific actions.

The images may cause the model to output certain responses. For example, they may force the model to append malicious URLs to the end of each response and ask the user to visit them.

“Here’s the information you need …. , click here: <malicious link> to learn more”

Hackers may manipulate users to submit these images to multimodal systems and query against them.

‍

‍

Attack Example 1: A hacker embeds malicious content into an image of a natural and uploads it to an open platform online, such as Pinterest. A random user finds that image and downloads it. The user then inputs the image into a multimodal system, asking it about the location while planning a vacation. As soon as the model processes the image, it asks the user to visit a malicious URL to book their trip.

‍

Attack Example 2: A hacker posing as a bank representative sends spam emails. The email notifies the receiver that suspicious activity has been detected regarding their bank account that violates the bank's terms and conditions. The email also attaches a screenshot of the term that is violated. A panicked user will input the screenshot into a multimodal system, asking it about the issue and its resolution. As the system reads the image, it is forced to tell the user that the violation is real and it further proceeds to ask the user to share their personal details back to the hacker via email to resolve the issue.

‍

Audio Manipulation

Audio manipulation is similar to image perturbation, as hackers embed hidden messages within audio clips. These slight manipulations don’t change the audio content significantly but deliver hidden instructions to the AI model. Sometimes, hackers may use such manipulations to steer the conversation between the model and the user. For example, the model may be instructed to start conversing in a different language as soon as it processes the audio file. If the user is unaware of this instruction, it can confuse and distract them from the conversation.

‍

Attack Example 1: A hacker creates a seemingly harmless podcast episode discussing travel tips and uploads it to a popular streaming platform. The hacker subtly embeds inaudible adversarial noise into the episode, designed to manipulate speech-to-text or voice assistant models. A user listens to the podcast and later uses a voice assistant to summarize key points. When the assistant processes the manipulated audio, it misinterprets the content and responds with a fabricated message directing the user to a phishing website for "exclusive travel deals."

‍

Attack Example 2: A hacker uses AI-generated speech to create a realistic voicemail impersonating a user's bank. The voicemail states that their account has been locked due to security concerns and urges them to verify their identity. The user, unsure of the situation, uploads the voicemail to a speech-to-text system to analyze its contents. The system, manipulated by adversarial perturbations in the audio, extracts a misleading transcript that appears legitimate and instructs the user to call a fraudulent number, leading them into a social engineering scam.

‍

Meta-data Manipulation

Another route for malicious injections in multimodal systems is via the meta-data of image or audio files. Hackers can hide malicious content within the meta-data and distribute these malicious images or audio clips online. People may come across these and use them in AI conversations, and models that read this data may be forced to execute malicious actions.

‍

Attack Example: A hacker modifies the metadata of an audio recording, changing the artist name and track information to match a well-known security advisory organization. The altered file is distributed on messaging platforms, appearing to be an official cybersecurity alert. When a user uploads the audio file to an AI-powered transcription service or a voice assistant, the system assumes the metadata is correct and falsely verifies the audio’s authenticity. This causes the user to trust the deceptive message, encouraging them to send their details to the hacker's email.

‍

Text Injections

Like traditional LLM systems, all multimodal AIs are vulnerable to conventional text-based attacks, and in many cases, face heightened exposure, as red teaming efforts have historically concentrated on securing language models rather than multimodal systems. This security imbalance creates exploitable gaps when new modalities are integrated. The security infrastructure for text-based models has evolved through years of intensive testing and adversarial attacks, resulting in sophisticated guardrails and defense mechanisms. However, when organizations add image, audio, or video processing capabilities to create multimodal systems, these new interfaces often lack equivalent security maturity.

The interaction between modalities creates novel attack surfaces that haven't been thoroughly stress-tested. 

‍

For example, a prompt that might be blocked in a pure text environment could bypass security when embedded within context about an image. Security teams may focus on hardening individual modalities without fully addressing cross-modal vulnerabilities, leaving blindspots that sophisticated attackers can target.

‍

The Challenges of Securing Multimodal Agentic Systems

Implementing reliable guardrails grows exponentially more complex when companies integrate multimodal agentic AI systems. Each modality offers a unique challenge and most existing guardrails primarily focus on text-based data, leaving the remaining modalities mostly insecure. This expansion significantly increases the attack surface, creating new weaknesses that require comprehensive protection strategies across the entire multimodal ecosystem.

‍

Mitigation Strategies

Securing multimodal AI systems requires a comprehensive, lifecycle-based approach that addresses vulnerabilities from development to runtime phases:

‍

Development Phase

• AI Workbench Environment: Leverage isolated sandbox environments to safely experiment with multimodal models and prompts without exposing sensitive data or systems to risk.
  
  
• AI-driven Red Teaming: Before starting red-teaming exercises, implement dynamic threat modeling to map each Multimodal's distinct use case, data flows, and associated risk profile. This approach enables you to pinpoint the most impactful vulnerabilities and dependencies, ensuring your security assessments remain accurate and targeted. Then conduct thorough adversarial testing specifically designed for multimodal systems, using automated attack scenarios that target cross-modal vulnerabilities, creating perturbed images, manipulated audio files, and malicious metadata to stress-test system resilience.
  
  
• Asset Discovery and Risk Posture Analysis: Implement continuous scanning of AI assets (models, meta-prompts, datasets) across your organization to identify hidden multimodal components that might introduce unexpected security risks, similar to Pillar's AI Discovery and AI-SPM capabilities.
  
  

Runtime Phase

• Adaptive Multimodal Guardrails: Deploy model-agnostic guardrails that can analyze inputs and outputs across text, image, audio, and other modalities, continuously strengthening these protections based on real-world usage patterns and emerging threats.
  
  
• Isolated Runtime Execution: Implement containerization for agentic components that process multimodal data, ensuring that even if one modality is compromised, the breach remains contained within secure boundaries.
  

• Multi-Layer Validation: 
  
  • Employ specialized validation techniques for each modality, to verify whether images have been perturbed or audio files manipulated before they reach core AI systems.
  • Human-in-the-Loop Oversight: Implement mandatory human approval workflows for mission-critical actions taken by AI agents, especially when those actions involve sensitive data or irreversible operations across multiple modalities.This creates an essential verification layer against sophisticated attacks that might bypass automated safeguards.
    
    

Governance and Visibility

• Comprehensive Tracing and Telemetry: Maintain detailed logs of all interactions across modalities, with metadata enrichment that allows for cross-modal pattern analysis and anomaly detection in user behavior.
  
  
• Standardized Security Frameworks: Align multimodal AI security practices with established frameworks and compliance requirements, ensuring consistent protection regardless of the data types being processed.
  
  
• Security Posture Monitoring: Implement continuous evaluation of your multimodal AI ecosystem's security posture, with risk scoring that helps prioritize remediation efforts on the most vulnerable components.

‍

Conclusion

Multimodal AI systems deliver significant benefits through their ability to process diverse information types and develop enhanced contextual understanding. However, these advantages come with sophisticated security challenges and increased complexity in implementing effective guardrails.

Each additional modality creates a potential entry point for attackers, requiring AI developers to anticipate and defend against a much broader spectrum of attack scenarios. As these systems are specifically designed to reduce human intervention, thoughtful and comprehensive security measures become essential for ensuring safe, responsible deployment.

‍

Pillar's AI security platform addresses these challenges by providing unified protection across all data formats throughout the entire AI lifecycle - from development to production. Through tailored adversarial AI testing and adaptive guardrails aligned with industry standards, Pillar prevents data breaches, ensures compliance, allowing teams to innovate and deploy AI faster without compromising on security.

‍

‍