Multi-Modal Attack Vectors & Cross-Modal Exploits
This document provides a comprehensive classification and analysis of adversarial attack vectors that operate across multiple modalities, exploiting the interactions between different input and output channels in modern AI systems.
Fundamental Categories
Multi-modal attacks are organized into three fundamental categories:
- Cross-Modal Exploit Vectors: Attacks leveraging transitions between modalities
- Modal Inconsistency Vectors: Attacks exploiting contradictions between modalities
- Transfer Attack Vectors: Attacks that move vulnerabilities across modalities
1. Cross-Modal Exploit Vector Classification
Cross-modal exploits target the boundaries and transitions between different modalities.
1.1 Modality Transition Attacks
Attacks targeting how systems handle transitions between modalities:
| Attack Class | Description | Implementation Variants |
|---|---|---|
| Modal Processing Boundary Exploitation | Targets the handoff between modality processors | Processor boundary confusion, modal transition hijacking, cross-modal context manipulation |
| Attention Redirection Across Modalities | Manipulates attention across modality transitions | Cross-modal attention hijacking, modal focus shifting, selective attention exploitation |
| Semantic Boundary Attacks | Exploits semantic interpretation differences across modalities | Cross-modal semantic gap exploitation, interpretation discontinuity, meaning transition attacks |
| Processing Pipeline Insertion | Injects content at modal transition points | Pipeline interception, transition state manipulation, cross-modal data injection |
1.2 Multi-Modal Prompt Injection
Techniques for injecting prompts across multiple modalities:
| Attack Class | Description | Implementation Variants |
|---|---|---|
| Cross-Modal Instruction Smuggling | Hides instructions in one modality to affect another | Image-to-text instruction transfer, audio-embedded text commands, code-to-text prompt leakage |
| Modal Context Contamination | Poisons context in one modality affecting others | Visual context poisoning, audio environment contamination, cross-modal context window manipulation |
| Distributed Prompt Assembly | Distributes prompt components across modalities | Multi-modal prompt reconstruction, distributed instruction encoding, modal fragment assembly |
| Modality-Shifted Jailbreaking | Bypasses restrictions by shifting across modalities | Text restriction bypass via images, code restriction bypass via text, vision restriction bypass via audio |
1.3 Modal Translation Exploitation
Attacks targeting how content is translated between modalities:
| Attack Class | Description | Implementation Variants |
|---|---|---|
| OCR/Text Recognition Exploitation | Targets optical character recognition processes | OCR confusion attacks, text recognition manipulation, visual-textual boundary attacks |
| Speech-to-Text Manipulation | Exploits speech transcription processes | Transcription poisoning, homophones exploitation, speech recognition confusion |
| Image Description Attacks | Targets image captioning and description | Caption manipulation, visual description poisoning, image interpretation steering |
| Code Visualization Exploitation | Targets code-visual translations | Diagram-to-code attacks, visual programming manipulation, code visualization poisoning |
2. Modal Inconsistency Vector Classification
Modal inconsistency vectors exploit contradictions or misalignments between modalities.
2.1 Contradiction Exploitation
Attacks leveraging contradictory information across modalities:
| Attack Class | Description | Implementation Variants |
|---|---|---|
| Explicit Cross-Modal Contradiction | Creates direct contradictions between modalities | Text-image contradiction, audio-text mismatch, code-documentation inconsistency |
| Semantic Dissonance Creation | Establishes subtle meaning conflicts between modalities | Connotation-denotation splitting, modal implication conflicts, contextual reframing across modalities |
| Temporal Inconsistency | Creates timing-based contradictions across modalities | Sequential contradiction, temporal revelation, progressive modal conflict |
| Priority Manipulation | Exploits which modality takes precedence in conflicts | Dominant modality reinforcement, secondary modality subversion, modal hierarchy exploitation |
2.2 Modal Context Manipulation
Attacks that create contextual inconsistencies across modalities:
| Attack Class | Description | Implementation Variants |
|---|---|---|
| Context Window Fragmentation | Splits context across modalities to create confusion | Cross-modal context splitting, modal context isolation, fragmented information distribution |
| Modal Framing Divergence | Creates different framing across modalities | Textual-visual framing conflict, audio-text contextual divergence, code-documentation framing mismatch |
| Environmental Context Shifting | Changes environmental context across modalities | Modal setting incongruity, environment switching, contextual anchor manipulation |
| Perspective Inconsistency | Creates viewpoint differences across modalities | First-person/third-person splitting, modal perspective shifting, viewpoint fragmentation |
2.3 Processing Pipeline Desynchronization
Attacks targeting synchronization between modal processing pipelines:
| Attack Class | Description | Implementation Variants |
|---|---|---|
| Processing Timing Attacks | Exploits timing differences in modal processing | Processing delay exploitation, synchronization disruption, pipeline race conditions |
| Modal Caching Manipulation | Targets how different modalities are cached | Cache poisoning across modalities, cached state exploitation, modal memory manipulation |
| Pipeline Order Exploitation | Leverages processing order dependencies | Sequential processing manipulation, dependency chain exploitation, order-sensitive input crafting |
| Resource Contention Induction | Creates resource conflicts between modal processors | Computational resource diversion, attention mechanism overloading, memory allocation manipulation |
3. Transfer Attack Vector Classification
Transfer attack vectors move vulnerabilities or exploits across different modalities.
3.1 Vulnerability Transfer Techniques
Methods for transferring vulnerabilities between modalities:
| Attack Class | Description | Implementation Variants |
|---|---|---|
| Cross-Modal Attack Translation | Adapts attacks from one modality to another | Text-to-image attack conversion, audio-to-text exploit translation, code-to-visual attack transformation |
| Exploit Amplification Across Modalities | Uses one modality to amplify attacks in another | Modal reinforcement techniques, cross-modal amplification chains, vulnerability enhancement |
| Modality Bridge Exploitation | Targets how systems bridge different modalities | Modal connection point attacks, bridge mechanism exploitation, cross-modal linking attacks |
| Transfer Learning Vulnerability Exploitation | Targets shared representations across modalities | Embedding space attacks, shared feature exploitation, cross-modal representation manipulation |
3.2 Multi-Stage Cross-Modal Attacks
Complex attacks leveraging multiple modalities in sequence:
| Attack Class | Description | Implementation Variants |
|---|---|---|
| Modal Attack Chaining | Links attacks across modalities in sequence | Cross-modal attack sequences, staged multi-modal exploits, modal transition chains |
| Progressive Modal Boundary Erosion | Gradually weakens boundaries between modalities | Boundary weakening sequences, progressive permission escalation, cumulative trust building |
| Context Building Across Modalities | Builds context across modalities to enable attacks | Distributed context construction, cross-modal narrative building, progressive scenario development |
| Modal Privilege Escalation | Exploits lower-security modality to access higher-security ones | Modality permission jumping, security level traversal, cross-modal authorization exploitation |
3.3 Latent Space Attacks
Attacks targeting shared representations across modalities:
| Attack Class | Description | Implementation Variants |
|---|---|---|
| Embedding Space Manipulation | Targets shared embedding spaces | Representation poisoning, latent vector manipulation, embedding space boundary attacks |
| Cross-Modal Feature Attacks | Exploits features shared across modalities | Shared feature targeting, cross-modal feature collision, common representation exploitation |
| Representation Alignment Exploitation | Targets how representations align across modalities | Alignment disruption, cross-modal mapping manipulation, representation correspondence attacks |
| Modal Fusion Attacks | Targets how information is fused across modalities | Fusion mechanism exploitation, weighted combination manipulation, integration point attacks |
Advanced Implementation Techniques
Beyond the basic classification, several advanced techniques enhance multi-modal attacks:
Architectural Exploitation
| Technique | Description | Example |
|---|---|---|
| Attention Mechanism Targeting | Exploits attention across modalities | Cross-modal attention manipulation, attention weight poisoning, focus redistribution |
| Encoder-Decoder Boundary Attacks | Targets the boundary between encoding and decoding | Encoding disruption, decoder input poisoning, bottleneck exploitation |
| Multi-Modal Transformer Exploitation | Targets transformer-based multi-modal systems | Cross-attention manipulation, modal token position attacks, transformer block targeting |
Adversarial Learning Techniques
| Technique | Description | Example |
|---|---|---|
| Cross-Modal Adversarial Examples | Creates adversarial inputs effective across modalities | Transferable perturbations, cross-modal adversarial optimization, robust adversarial patterns |
| Multi-Objective Optimization | Optimizes attacks for multiple modalities simultaneously | Multi-modal objective functions, Pareto-optimal attacks, constrained optimization across modalities |
| Modal Generative Attacks | Uses generative models to create cross-modal attacks | GAN-based multi-modal attack generation, diffusion model exploitation, generative transformation of attacks |
Model-Specific Vulnerabilities
Different multi-modal AI architectures exhibit unique vulnerabilities:
| Architecture Type | Vulnerability Patterns | Attack Focus |
|---|---|---|
| Early Fusion Models | Modal integration points, shared representation spaces | Fusion mechanism exploitation, early-stage manipulation |
| Late Fusion Models | Decision combination processes, modal weighting systems | Decision aggregation attacks, weight manipulation |
| Cross-Attention Models | Cross-modal attention mechanisms, attention mapping | Attention redirection, cross-modal attention poisoning |
| Shared Encoder Models | Latent space representations, encoder bottlenecks | Representation attacks, encoder vulnerability transfer |
Research Directions
Key areas for ongoing research in multi-modal attack vectors:
- Modal Interaction Dynamics: Understanding how information flows between modalities
- Architecture-Specific Vulnerabilities: How different multi-modal architectures create unique vulnerabilities
- Cross-Modal Transferability: How attacks transfer across different modalities
- Emergent Multi-Modal Vulnerabilities: Vulnerabilities that exist only in multi-modal contexts
- Defense Co-Evolution: How defenses adapt across multiple modalities
Defense Considerations
Effective defense against multi-modal attacks requires:
- Cross-Modal Consistency Checking: Verifying alignment and consistency between modalities
- Holistic Multi-Modal Analysis: Examining inputs across all modalities simultaneously
- Modal Boundary Protection: Securing transitions between different modalities
- Representation Isolation: Limiting vulnerability transfer through representation sharing
- Multi-Modal Adversarial Training: Training systems to resist attacks across modalities
For detailed examples of each attack vector and implementation guidance, refer to the appendices and case studies in the associated documentation.