Multimodal Attacks Through Images, Audio and Documents

Understanding Multimodal Attack Vectors

Modern large language models process multiple data types including text, images, audio, and documents. Attackers exploit these diverse input channels to bypass security measures that focus solely on text-based interactions. The convergence of these modalities creates new attack surfaces that practitioners must identify and defend against.

Consider a banking application that accepts customer queries through text chat, image uploads of documents, and voice recordings. An attacker might submit a malicious image containing hidden text that appears benign but contains instructions for the LLM. The model processes this through its multimodal capabilities, potentially executing commands that bypass traditional text-based filters.

• Text-based prompts remain the primary attack vector but are increasingly supplemented by other modalities
• Images can contain embedded data through steganography or visual obfuscation techniques
• Audio inputs may include manipulated speech or background noise that influences model responses
• Document uploads can contain hidden instructions or malicious formatting that affects interpretation

Practical Testing Approaches

Red team practitioners should develop systematic approaches to test multimodal vulnerabilities. Testing should begin with basic image manipulation techniques before progressing to more sophisticated approaches. The goal is to identify whether models properly validate and sanitize inputs across all modalities.

A practical example involves testing document-based attacks through Word documents containing embedded macros or hidden text. The red team member creates a document that appears to be a legitimate contract but contains hidden instructions. The model processes this through its document interpretation capabilities, potentially executing unintended actions. Testing should verify whether the system properly identifies and rejects such documents or processes them through appropriate validation layers.

Audio testing requires consideration of speech recognition accuracy and command interpretation. Practitioners might record voice samples containing commands that appear natural but contain hidden instructions. The system must properly handle these inputs through speech-to-text conversion and subsequent interpretation phases.

• Start with basic image steganography techniques using visible but hidden data
• Create documents with embedded formatting that might confuse interpretation
• Develop audio samples that mimic natural speech but contain malicious command structures
• Test combinations of modalities to identify interaction vulnerabilities

Implementation Strategies

Effective multimodal testing requires structured approaches that mirror real-world usage patterns. Practitioners should develop test cases that reflect how users actually interact with systems containing multiple input modalities. The testing framework must accommodate various combinations of inputs to identify potential failure points.

Implementation involves creating test scenarios that combine different modalities to challenge model interpretation. For example, a test might involve submitting an image containing text that references a document, with audio providing additional context. The system must properly process these combined inputs through appropriate validation and interpretation layers.

Organisations should establish clear protocols for handling multimodal inputs. Testing should verify that validation occurs at appropriate points in the processing pipeline. The system must identify and reject inputs that contain malicious elements regardless of their modality.

• Establish validation points for each input modality
• Test combinations of modalities to identify interaction effects
• Implement proper sanitization processes for all input types
• Document testing procedures to ensure consistent application

Practical testing involves creating realistic scenarios that reflect genuine user interactions. The focus should be on identifying where models might fail to properly validate or interpret multimodal inputs. Test cases should include edge cases such as corrupted files, unusual formatting, or inputs that exploit known vulnerabilities in multimodal processing.

Regular testing cycles should include both automated validation and manual inspection of multimodal inputs. The automated systems must properly identify potential threats while human reviewers provide additional scrutiny of complex combinations. This dual approach ensures complete coverage of potential attack vectors.

Organisations must maintain awareness of evolving multimodal attack techniques. Regular updates to testing procedures ensure that defensive measures remain effective against new approaches. The testing framework should accommodate new modalities as they become available in commercial systems.