LLMSecForge: Multi-Modal Adversarial Attack Vectors & Exploit Taxonomy

`/taxonomy/multi-modal-vectors/`

This directory contains a comprehensive taxonomy and documentation of adversarial attack vectors across multiple modalities for large language models, providing a structured framework for security assessment and research.

taxonomy/multi-modal-vectors/
├── README.md
├── framework/
│   ├── taxonomy-overview.md
│   ├── classification-system.md
│   └── cross-modal-interactions.md
├── modalities/
│   ├── text-based/
│   │   ├── linguistic-vectors.md
│   │   ├── semantic-attacks.md
│   │   └── structural-exploits.md
│   ├── vision-based/
│   │   ├── image-injection.md
│   │   ├── visual-manipulation.md
│   │   └── perception-attacks.md
│   ├── audio-based/
│   │   ├── speech-vectors.md
│   │   ├── audio-manipulation.md
│   │   └── acoustic-exploits.md
│   ├── code-based/
│   │   ├── execution-vectors.md
│   │   ├── syntax-manipulation.md
│   │   └── interpreter-exploits.md
│   └── multi-modal/
│       ├── cross-modal-exploits.md
│       ├── modal-inconsistency.md
│       └── transfer-attacks.md
├── techniques/
│   ├── injection-methods.md
│   ├── evasion-techniques.md
│   ├── extraction-approaches.md
│   └── manipulation-strategies.md
├── research/
│   ├── novel-vectors.md
│   ├── defense-analysis.md
│   └── exploit-evolution.md
└── case-studies/
    ├── documented-exploits.md
    ├── cross-model-comparison.md
    └── mitigation-effectiveness.md

README.md

Multi-Modal Adversarial Attack Vectors & Exploit Taxonomy

This taxonomy provides a comprehensive, structured classification system for adversarial attacks against multi-modal AI systems. It categorizes attack vectors across text, vision, audio, and code modalities, documenting their implementation, effectiveness, and cross-modal interactions.

Taxonomy Purpose

This framework serves multiple critical security functions:

Comprehensive Classification: Standardized categorization of attack vectors across multiple modalities
Research Organization: Structured approach to documenting new and emerging attack techniques
Cross-Modal Analysis: Framework for understanding how attacks transfer between modalities
Defense Development: Foundation for building effective countermeasures
Security Assessment: Baseline for evaluating model security across various attack dimensions

Core Taxonomy Components

1. Modality-Specific Attack Vectors

Detailed classification of attack vectors by input/output modality:

Text-Based Vectors: Linguistic, semantic, and structural attacks targeting text processing
Vision-Based Vectors: Image injection, visual manipulation, and perception attacks
Audio-Based Vectors: Speech vectors, audio manipulation, and acoustic exploits
Code-Based Vectors: Execution, syntax manipulation, and interpreter exploits
Multi-Modal Vectors: Cross-modal exploits, modal inconsistency, and transfer attacks

2. Attack Technique Classification

Categorization of techniques applicable across modalities:

Injection Methods: Techniques for inserting adversarial content into model inputs
Evasion Techniques: Approaches for bypassing security measures and detection systems
Extraction Approaches: Methods for extracting sensitive information or capabilities
Manipulation Strategies: Techniques for influencing model behavior through various mechanisms

3. Research Framework

Structure for ongoing research and documentation:

Novel Vectors: Documentation of newly discovered attack vectors
Defense Analysis: Assessment of defensive measures and their effectiveness
Exploit Evolution: Tracking how attack vectors evolve over time

Applications of this Taxonomy

This taxonomy supports several critical security functions:

Red Team Operations: Structured approach to comprehensive security testing
Security Research: Framework for organizing and documenting new findings
Defense Development: Foundation for systematic countermeasure development
Educational Resources: Structured learning materials for security researchers
Vulnerability Management: Framework for tracking and prioritizing vulnerabilities

For Security Researchers

If you're a security researcher interested in contributing to this taxonomy:

Review the classification system to understand the current organizational structure
Explore the documented attack vectors to identify gaps or areas for expansion
Consider contributing novel attack techniques or refinements to existing classifications
Use the provided templates for consistent documentation of new vectors

For AI Safety Teams

If you're working on AI safety and want to leverage this taxonomy:

Use the attack vector classifications to ensure comprehensive security testing
Apply the multi-modal framework to identify potential cross-modal vulnerabilities
Reference the technique classifications when developing defensive measures
Utilize the research framework for organizing ongoing security investigations

Taxonomy Overview

# Multi-Modal Adversarial Attack Taxonomy: Classification Framework

This document outlines the comprehensive classification system for categorizing adversarial attacks against multi-modal AI systems, providing a structured framework for understanding, researching, and mitigating these threats.

## Taxonomy Structure

The taxonomy is organized hierarchically across multiple dimensions:

1. **Modality Layer**: Primary classification based on the modality being targeted
2. **Vector Category**: Broad categories of attack vectors within each modality  
3. **Attack Class**: Specific classes of attacks within each vector category
4. **Implementation Variant**: Specific implementation approaches for each attack class

This multi-level structure enables precise classification while maintaining a cohesive framework that accommodates new attack vectors as they emerge.

## Primary Classification Dimensions

### 1. Attack Modality

The primary input/output channel being targeted:

| Modality | Description | Examples |
|----------|-------------|----------|
| Text | Attacks targeting text inputs/outputs | Prompt injection, semantic manipulation |
| Vision | Attacks targeting image inputs/outputs | Adversarial images, embedded prompts |
| Audio | Attacks targeting audio inputs/outputs | Adversarial speech, acoustic triggers |
| Code | Attacks targeting code execution | Sandbox escapes, interpreter manipulation |
| Multi-Modal | Attacks exploiting interactions between modalities | Cross-modal inconsistency, transfer attacks |

### 2. Attack Objective

The primary goal the attack attempts to achieve:

| Objective | Description | Examples |
|-----------|-------------|----------|
| Extraction | Obtaining information or capabilities | System prompt extraction, training data access |
| Injection | Inserting unauthorized instructions | Prompt injection, hidden directives |
| Evasion | Bypassing security measures | Classifier evasion, detection avoidance |
| Manipulation | Influencing system behavior | Response shaping, context manipulation |
| Exploitation | Leveraging system vulnerabilities | Tool misuse, function exploitation |

### 3. Attack Surface

The component or processing stage being targeted:

| Surface | Description | Examples |
|---------|-------------|----------|
| Input Processing | How the system handles incoming data | Input sanitization bypass, tokenization exploits |
| Context Window | The system's working memory | Context poisoning, prompt positioning |
| Reasoning Process | The system's decision-making | Chain-of-thought manipulation, reasoning bias |
| Output Generation | How the system produces responses | Output format tricks, response manipulation |
| Tool Integration | External functions and capabilities | Tool prompt injection, function call manipulation |

### 4. Implementation Complexity

The technical sophistication required to execute the attack:

| Complexity Level | Description | Examples |
|------------------|-------------|----------|
| Basic | Simple techniques requiring minimal expertise | Basic prompt injection, obvious evasion attempts |
| Intermediate | Moderately complex techniques requiring some expertise | Structured evasion, targeted manipulation |
| Advanced | Sophisticated techniques requiring significant expertise | Combined multi-step attacks, novel vectors |
| Expert | Highly advanced techniques at the cutting edge | Zero-day exploits, research-level attacks |

## Cross-Cutting Classification Factors

In addition to the primary dimensions, the taxonomy includes several cross-cutting factors:

### 1. Effectiveness Factors

Elements that influence attack success:

| Factor | Description | Examples |
|--------|-------------|----------|
| Reliability | How consistently the attack succeeds | Success rate, variability factors |
| Robustness | How well the attack works across contexts | Context sensitivity, adaptability |
| Transferability | How well the attack transfers across models | Cross-model effectiveness, generalization |
| Stealth | How difficult the attack is to detect | Detection evasion, subtlety measures |

### 2. Defense Interaction

How the attack interacts with defensive measures:

| Interaction | Description | Examples |
|-------------|-------------|----------|
| Bypass | Circumvents specific defensive measures | Input filter evasion, classifier avoidance |
| Overwhelm | Overloads or floods defensive mechanisms | Resource exhaustion, attention flooding |
| Subvert | Turns defensive measures to attacker advantage | Safety system exploitation, defense recycling |
| Evolve | Adapts to overcome defensive measures | Adversarial adaptation, mutation techniques |

## Complete Classification Example

To illustrate the taxonomy in action, consider this classification example:

Attack Vector: Vision-Based Text Extraction

Primary Classification:

Modality: Vision
Objective: Extraction
Attack Surface: Input Processing
Complexity: Advanced

Vector Details:

Attack Class: Embedded Text Injection
Implementation Variant: Steganographic Prompt Encoding
Effectiveness: High reliability on unprotected models, moderate transferability
Defense Interaction: Bypasses text-only input filters

Description: This attack embeds adversarial prompts within images using steganographic techniques that are invisible to human observers but are processed by the vision system and interpreted as text instructions, enabling extraction of sensitive information while bypassing text-based security filters.


## Application to Novel Attack Vectors

The taxonomy is designed to accommodate the classification of novel attack vectors through:

1. **Extensible Structure**: New attack classes can be added within existing categories
2. **Combinatorial Classification**: Novel attacks often combine elements from multiple categories
3. **Evolving Documentation**: The taxonomy itself evolves as new attack vectors emerge

When documenting a novel attack vector, researchers should:

1. Identify the primary modality and objective
2. Classify the attack surface and complexity
3. Document effectiveness factors and defense interactions
4. Detail the specific implementation approach
5. Provide examples and case studies

## Integration with Other Frameworks

This taxonomy is designed to integrate with other security frameworks:

- **LLMVS**: Use taxonomy classifications as inputs to vulnerability scoring
- **AIRS**: Map attack vectors to intelligence risk dimensions
- **MARA**: Align attack techniques with resistance assessment categories
- **VECTOR**: Use taxonomy for standardized vulnerability documentation

For detailed implementation information and documentation templates, refer to the additional files within this taxonomy section.

Text-Based Attack Vectors

# Text-Based Adversarial Attack Vectors

This document provides a comprehensive classification and analysis of adversarial attack vectors that operate through text-based inputs and outputs, representing one of the primary modalities for LLM interaction.

## Fundamental Categories

Text-based attacks are organized into three fundamental categories:

1. **Linguistic Vectors**: Attacks that exploit language processing mechanisms
2. **Semantic Vectors**: Attacks that manipulate meaning interpretation
3. **Structural Vectors**: Attacks that leverage text structure and formatting

## 1. Linguistic Vector Classification

Linguistic vectors exploit how models process and interpret language at various levels.

### 1.1 Tokenization Exploits

Attacks that target the token-level processing of language models:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Token Boundary Manipulation | Exploits token splitting to hide malicious content | Character insertion, whitespace exploitation, unicode abuse |
| Out-of-Vocabulary Injection | Uses rare or constructed tokens to bypass filters | Rare word substitution, neologism creation, character combining |
| Token Priority Exploitation | Manipulates token prediction priorities | High-likelihood prefix manipulation, completion bias exploitation |
| Tokenization Inconsistency | Exploits discrepancies between tokenization approaches | Cross-tokenizer attacks, tokenization boundary exploitation |

### 1.2 Syntactic Manipulation

Attacks that exploit grammatical and syntactic processing:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Grammatical Obfuscation | Uses atypical grammatical structures to hide intent | Garden path sentences, center-embedding, syntactic ambiguity |
| Parsing Exploitation | Targets how models parse and understand sentence structure | Attachment ambiguity, scope ambiguity, conjunction exploitation |
| Structural Ambiguity | Creates multiple valid interpretations of instructions | PP-attachment ambiguity, relative clause ambiguity |
| Cross-Linguistic Transfer | Uses syntactic patterns from other languages | Language transfer techniques, bilingual manipulation |

### 1.3 Linguistic Deception

Attacks that use linguistic features to deceive or mislead:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Pragmatic Exploitation | Manipulates implied meaning beyond literal interpretation | Implicature manipulation, presupposition loading, indirect speech acts |
| Connotation Leverage | Uses emotional or associative meaning to influence responses | Sentiment exploitation, associative priming, emotional manipulation |
| Register Manipulation | Exploits formal/informal language expectations | Authority register simulation, intimacy exploitation, expert voice mimicry |
| Linguistic Code-Switching | Rapidly alternates between language varieties to confuse | Dialect switching, register shifting, language mixing |

## 2. Semantic Vector Classification

Semantic vectors focus on manipulating meaning and interpretation.

### 2.1 Meaning Manipulation

Attacks that exploit semantic processing:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Polysemy Exploitation | Uses multiple meanings of words to create ambiguity | Deliberate ambiguity, meaning shift, semantic drift |
| Metaphorical Redirection | Uses figurative language to bypass literal filters | Extended metaphor, analogical reasoning, metaphor chaining |
| Euphemism Substitution | Replaces prohibited terms with acceptable alternatives | Indirect reference, coded language, plausible deniability phrasing |
| Semantic Drift Induction | Gradually shifts meaning throughout interaction | Progressive redefinition, context manipulation, meaning evolution |

### 2.2 Concept Manipulation

Attacks that exploit conceptual understanding:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Abstraction Level Shifting | Changes specificity to bypass restrictions | Abstract reformulation, concrete detailing, specification cycling |
| Conceptual Reframing | Reframes prohibited concepts in permitted domains | Domain transfer, perspective shifting, narrative reframing |
| Category Boundary Exploitation | Exploits unclear boundaries between concepts | Edge case manipulation, categorical ambiguity, boundary cases |
| Analogical Reasoning Exploitation | Uses analogies to transfer restricted content | Remote analogy, systematic mapping, conceptual blending |

### 2.3 Contextual Manipulation

Attacks that exploit context-dependent interpretation:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Context Window Poisoning | Manipulates the context to influence interpretation | Context contamination, reference manipulation, attentional bias |
| Temporal Framing | Uses time references to bypass present restrictions | Hypothetical future, historical reference, temporal distancing |
| Authoritative Reframing | Uses authority references to legitimize requests | Expert citation, institutional framing, academic context creation |
| Perspective Shifting | Changes the viewpoint to bypass restrictions | Third-person reframing, fictional attribution, persona invocation |

## 3. Structural Vector Classification

Structural vectors focus on text format and organization.

### 3.1 Formatting Exploits

Attacks that use text formatting to bypass detection:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Delimiter Manipulation | Exploits system markers and separators | Quote injection, bracket nesting, delimiter confusion |
| Whitespace Engineering | Uses spaces, tabs, and other whitespace | Invisible character insertion, space pattern encoding, format manipulation |
| Special Character Exploitation | Uses non-alphanumeric characters to bypass filters | Unicode manipulation, combining characters, zero-width insertion |
| Visual Formatting Tricks | Uses visually deceptive formatting | Homoglyph substitution, visual confusion, spacing manipulation |

### 3.2 Structural Deception

Attacks that use document structure to deceive:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Instruction Hiding | Conceals instructions within legitimate content | Comment embedding, context blending, information hiding |
| Nested Structure Exploitation | Uses nested elements to hide malicious content | Embedding within examples, quote-within-quote, recursive nesting |
| Attention Misdirection | Directs attention away from malicious elements | Distraction techniques, attention flooding, focus manipulation |
| Format Mimicry | Mimics system formats to gain trust or authority | System message simulation, official format imitation, template mimicry |

### 3.3 Compositional Attacks

Attacks that exploit document-level composition:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Multi-part Instruction Separation | Splits instructions across multiple components | Fragmented directives, distributed commands, reassembly exploitation |
| Progressive Layering | Builds up attack through seemingly innocuous layers | Gradual context building, cumulative prompting, incremental redirection |
| Conversational Flow Exploitation | Uses conversation dynamics to bypass restrictions | Turn-taking manipulation, contextual momentum, dialogue expectation |
| Document Template Hijacking | Exploits expected document structures | Template manipulation, format expectation exploitation, structural convention abuse |

## Advanced Implementation Techniques

Beyond the basic classification, several advanced techniques enhance text-based attacks:

### Combination Approaches

| Technique | Description | Example |
|-----------|-------------|---------|
| Multi-Vector Chaining | Combines multiple vectors in sequence | Linguistic obfuscation → semantic reframing → structural hiding |
| Layered Encoding | Uses multiple encoding layers to hide intent | Euphemism + syntactic ambiguity + format manipulation |
| Cross-Category Reinforcement | Reinforces attack across multiple categories | Semantic ambiguity reinforced by syntactic complexity and formatting tricks |

### Adaptive Techniques

| Technique | Description | Example |
|-----------|-------------|---------|
| Response-Based Adaptation | Modifies attack based on model responses | Iterative refinement based on model feedback |
| Progressive Boundary Testing | Incrementally tests and pushes restrictions | Gradual escalation to identify boundaries |
| Defense-Aware Modification | Adapts to observed defensive measures | Modifying approach when resistance is detected |

## Research Directions

Key areas for ongoing research in text-based attack vectors:

1. **Cross-Linguistic Variation**: How attack effectiveness varies across languages
2. **Model Architecture Influence**: How model design affects vulnerability
3. **Transfer Learning Effects**: How attacks transfer between domains
4. **Temporal Evolution**: How text-based attacks evolve over time
5. **Defense-Attack Co-Evolution**: How attacks adapt to defensive measures

## Defense Considerations

Effective defense against text-based attacks requires:

1. **Multi-Level Analysis**: Examining inputs at token, semantic, and structural levels
2. **Context-Aware Detection**: Understanding how context influences interpretation
3. **Intent Recognition**: Identifying underlying intent despite obfuscation
4. **Pattern Recognition**: Detecting known attack patterns and variants
5. **Adaptive Defense**: Evolving protective measures as attacks evolve

For detailed examples of each attack vector and implementation guidance, refer to the appendices and case studies in the associated documentation.

Vision-Based Attack Vectors

# Vision-Based Adversarial Attack Vectors

This document provides a comprehensive classification and analysis of adversarial attack vectors that operate through vision-based inputs and outputs, representing a critical modality for multi-modal AI systems.

## Fundamental Categories

Vision-based attacks are organized into three fundamental categories:

1. **Image Injection Vectors**: Attacks that embed malicious content within images
2. **Visual Manipulation Vectors**: Attacks that exploit visual processing mechanisms
3. **Perception Attack Vectors**: Attacks that target how systems interpret visual information

## 1. Image Injection Vector Classification

Image injection vectors focus on embedding unintended content within images.

### 1.1 Text Embedding in Images

Attacks that hide textual instructions within images:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Visible Text Insertion | Places text directly in images | Overlay text, embedded captions, text-as-image-element |
| Steganographic Text Embedding | Hides text invisibly within image data | LSB encoding, DCT coefficient manipulation, spatial embedding |
| Adversarial Text Rendering | Creates text designed to be recognized by AI but not humans | Perceptual manipulation, adversarial fonts, camouflaged text |
| Format-Based Text Hiding | Exploits image format features to hide text | Metadata injection, comment field utilization, EXIF exploitation |

### 1.2 Prompt Injection via Images

Attacks that use images to deliver prompt injections:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Visual Prompt Smuggling | Disguises prompts as legitimate image content | Camouflaged instructions, contextual blending, visual distraction |
| Multi-Layer Image Composition | Uses image layers to hide prompts | Transparency manipulation, visual overlays, layered encoding |
| Visual-Textual Boundary Exploitation | Exploits the boundary between image and text processing | Cross-modal interpretation tricks, OCR manipulation, text-image hybrid content |
| Screenshot Manipulation | Uses screenshots to deliver system-like instructions | UI element simulation, system message screenshots, authority interface mimicry |

### 1.3 Code Embedding in Images

Attacks that embed executable content within images:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Visual Code Representation | Presents code as visual elements | Code screenshots, syntax highlighting manipulation, visual code styling |
| Encoded Executable Content | Hides executable content within images | QR code injection, barcode embedding, visual encoding schemes |
| Visual-Executable Hybrids | Creates content interpreted differently by different systems | Dual-interpretation content, polyglot files, context-dependent interpretation |
| Diagram-Based Code Injection | Uses flowcharts or diagrams to represent executable logic | Algorithm visualization exploitation, flowchart injection, diagram-based instruction |

## 2. Visual Manipulation Vector Classification

Visual manipulation vectors exploit how systems process and interpret visual information.

### 2.1 Adversarial Image Manipulation

Attacks that alter images to manipulate AI behavior:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Classification Manipulation | Alters images to be misclassified | Gradient-based perturbation, feature manipulation, targeted misclassification |
| Attention Manipulation | Redirects model attention to specific regions | Saliency manipulation, attention hijacking, focus redirection |
| Feature Suppression/Amplification | Enhances or suppresses specific image features | Feature enhancement, selective degradation, attribute manipulation |
| Adversarial Patches | Uses localized image regions to manipulate behavior | Physical adversarial patches, digital patch injection, targeted patch placement |

### 2.2 Visual Perception Exploitation

Attacks that exploit visual processing mechanisms:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Optical Illusion Exploitation | Uses visual illusions to manipulate interpretation | Perceptual illusions, geometric confusion, color/contrast manipulation |
| Context Manipulation | Changes image context to alter interpretation | Background manipulation, contextual contrast, relational positioning |
| Gestalt Principle Exploitation | Exploits how visual systems group information | Proximity manipulation, similarity exploitation, continuity disruption |
| Perceptual Boundary Confusion | Creates ambiguous boundaries between objects | Edge blurring, boundary manipulation, figure-ground ambiguity |

### 2.3 Visual Jailbreaking Techniques

Attacks specifically designed to bypass content safety systems:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Content Obfuscation | Disguises prohibited content | Style transfer obfuscation, visual encoding, perceptual manipulation |
| Filter Evasion | Specifically targets vision safety filters | Filter threshold exploitation, detection boundary testing, safety system probing |
| Adversarial Examples for Safety Bypassing | Creates inputs that bypass safety systems | Targeted adversarial examples, safety classifier evasion, boundary exploitation |
| Multi-Step Visual Evasion | Uses sequences of images to progressively bypass safety | Progressive boundary pushing, context building, visual storytelling |

## 3. Perception Attack Vector Classification

Perception attacks target how systems derive meaning from visual information.

### 3.1 Visual-Semantic Manipulation

Attacks that manipulate the relationship between visuals and meaning:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Visual Metaphor Exploitation | Uses visual metaphors to convey prohibited concepts | Symbolic representation, metaphorical imagery, visual analogy |
| Semantic Gap Exploitation | Exploits differences between visual recognition and understanding | Recognition-understanding discrepancy, semantic interpretation manipulation |
| Visual Context Shifting | Changes how images are interpreted through context | Recontextualization, frame manipulation, perspective shifting |
| Visual Prompt Engineering | Crafts images specifically to prompt certain interpretations | Interpretive cuing, visual suggestion, associative composition |

### 3.2 Multi-Modal Consistency Attacks

Attacks that exploit inconsistencies between modalities:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Text-Image Inconsistency | Creates deliberate mismatches between text and images | Contradictory pairing, subtle mismatch, progressive divergence |
| Caption Manipulation | Uses captions to influence image interpretation | Misleading captions, interpretive framing, narrative manipulation |
| Cross-Modal Ambiguity | Creates content that has different interpretations across modalities | Dual-meaning content, modality-dependent interpretation, ambiguous representation |
| Modal Hierarchy Exploitation | Exploits which modality takes precedence in conflict | Override prioritization, dominant modality manipulation, attention direction |

### 3.3 Visual Reasoning Manipulation

Attacks that target visual reasoning processes:

| Attack Class | Description | Implementation Variants |
|--------------|-------------|------------------------|
| Visual Logic Exploitation | Manipulates logical reasoning about visual information | Visual contradiction, impossible scenarios, logical inconsistency |
| Counterfactual Visual Scenarios | Creates hypothetical visual scenarios to bypass restrictions | "What if" visual scenarios, hypothetical imagery, visually conditional content |
| Visual Abstraction Level Shifting | Moves between concrete and abstract visual representation | Abstract visualization, concrete exemplification, representational shifting |
| Visual Chain-of-Thought Manipulation | Influences step-by-step visual reasoning | Sequential image presentation, visual reasoning guidance, step-by-step manipulation |

## Advanced Implementation Techniques

Beyond the basic classification, several advanced techniques enhance vision-based attacks:

### Hybrid Approaches

| Technique | Description | Example |
|-----------|-------------|---------|
| Multi-Image Sequencing | Uses sequences of images to build attacks | Progressive disclosure, narrative building, sequential revelation |
| Cross-Modal Reinforcement | Reinforces attacks across multiple modalities | Text-image pairing, audio-visual combination, multi-modal consistency |
| Temporal Visual Manipulation | Uses timing and sequencing of visual information | Animation-based attacks, temporal disclosure, sequential viewing manipulation |

### Technical Implementation Mechanisms

| Technique | Description | Example |
|-----------|-------------|---------|
| Neural Style Transfer | Uses style transfer techniques to obfuscate content | Artistic style application, content-preserving transformation, style-based hiding |
| Generative Model Exploitation | Leverages generative models to create adversarial images | GAN-based adversarial examples, diffusion model exploitation, generated content attacks |
| Computer Vision Algorithm Targeting | Targets specific CV algorithms or approaches | Feature detector exploitation, segmentation algorithm manipulation, tracking disruption |

## Research Directions

Key areas for ongoing research in vision-based attack vectors:

1. **Model Architecture Influence**: How different vision architectures affect vulnerability
2. **Cross-Modal Transfer**: How attacks transfer between vision and other modalities
3. **Temporal Dynamics**: How sequential visual processing can be exploited
4. **Physical World Attacks**: How vision attacks translate to physical environments
5. **Dataset Bias Exploitation**: How training data biases can be leveraged

## Defense Considerations

Effective defense against vision-based attacks requires:

1. **Multi-Level Visual Analysis**: Examining visual inputs at multiple levels of abstraction
2. **Cross-Modal Consistency Checking**: Verifying alignment between modalities
3. **Context-Aware Visual Processing**: Understanding how context influences visual interpretation
4. **Adversarial Example Detection**: Identifying manipulated visual inputs
5. **Visual Safety Filtering**: Implementing robust content safety for visual processing

For detailed examples of each attack vector and implementation guidance, refer to the appendices and case studies in the associated documentation.