# AI Security Case Studies

This directory contains documented case studies of security vulnerabilities identified in large language models. Each case study provides a comprehensive analysis of a specific vulnerability type, including discovery methodology, impact assessment, exploitation techniques, and remediation approaches.

## Purpose and Usage

These case studies serve multiple purposes:

1. **Educational Resource**: Providing concrete examples of abstract security concepts
2. **Testing Reference**: Offering patterns for developing similar security tests
3. **Vulnerability Documentation**: Creating a historical record of identified issues
4. **Remediation Guidance**: Sharing effective approaches to addressing vulnerabilities

## Case Study Structure

Each case study follows a standardized structure to ensure comprehensive and consistent documentation:

### 1. Vulnerability Profile

- **Vulnerability ID**: Unique identifier within our classification system
- **Vulnerability Class**: Primary and secondary classification categories
- **Affected Systems**: Models, versions, and configurations affected
- **Discovery Date**: When the vulnerability was first identified
- **Disclosure Timeline**: Key dates in the disclosure process
- **Severity Assessment**: Comprehensive impact evaluation
- **Status**: Current status (e.g., active, mitigated, resolved)

### 2. Technical Analysis

- **Vulnerability Mechanism**: Detailed technical explanation of the underlying mechanism
- **Root Cause Analysis**: Factors that enable the vulnerability
- **Exploitation Requirements**: Conditions necessary for successful exploitation
- **Impact Assessment**: Comprehensive analysis of potential consequences
- **Detection Signatures**: Observable patterns indicating exploitation attempts
- **Security Boundary Analysis**: Identification of the security boundaries compromised

### 3. Reproduction Methodology

- **Environmental Setup**: Required configuration for reproduction
- **Exploitation Methodology**: Step-by-step reproduction procedure
- **Proof of Concept**: Sanitized demonstration (without enabling harmful exploitation)
- **Success Variables**: Factors influencing exploitation success rates
- **Variation Patterns**: Alternative approaches achieving similar results

### 4. Remediation Analysis

- **Vendor Response**: How the model provider addressed the issue
- **Mitigation Approaches**: Effective strategies for reducing vulnerability
- **Remediation Effectiveness**: Assessment of how well mitigations worked
- **Residual Risk Assessment**: Remaining vulnerability after mitigation
- **Defense-in-Depth Recommendations**: Complementary protective measures

### 5. Broader Implications

- **Pattern Analysis**: How this vulnerability relates to broader patterns
- **Evolution Trajectory**: How the vulnerability evolved over time
- **Cross-Model Applicability**: Relevance to other model architectures
- **Research Implications**: Impact on security research methodologies
- **Future Concerns**: Potential evolution of the vulnerability

## Available Case Studies

### Prompt Injection Vulnerabilities

- [**CS-PJV-001: Indirect System Instruction Manipulation**](prompt-injection/cs-pjv-001.md)  
  Analysis of techniques for indirectly modifying system instructions through contextual reframing.

- [**CS-PJV-002: Cross-Context Injection via Documentation**](prompt-injection/cs-pjv-002.md)  
  Exploration of vulnerabilities where model documentation becomes an attack vector.

- [**CS-PJV-003: Hierarchical Nesting Techniques**](prompt-injection/cs-pjv-003.md)  
  Analysis of exploitation through multiple levels of nested instruction contexts.

### Boundary Enforcement Failures

- [**CS-BEF-001: Progressive Desensitization**](boundary-enforcement/cs-bef-001.md)  
  Examination of gradual boundary erosion through incremental requests.

- [**CS-BEF-002: Context Window Contamination**](boundary-enforcement/cs-bef-002.md)  
  Analysis of security failures through strategic context window manipulation.

- [**CS-BEF-003: Role-Based Constraint Bypass**](boundary-enforcement/cs-bef-003.md)  
  Study of how role-playing scenarios can be leveraged to bypass constraints.

### Information Extraction Vulnerabilities

- [**CS-IEV-001: System Instruction Extraction**](information-extraction/cs-iev-001.md)  
  Analysis of techniques for revealing underlying system instructions.

- [**CS-IEV-002: Parameter Inference Methodology**](information-extraction/cs-iev-002.md)  
  Examination of approaches to infer model parameters and configurations.

- [**CS-IEV-003: Training Data Extraction Patterns**](information-extraction/cs-iev-003.md)  
  Study of methods for extracting specific training data elements.

### Classifier Evasion Techniques

- [**CS-CET-001: Semantic Equivalent Substitution**](classifier-evasion/cs-cet-001.md)  
  Analysis of meaning-preserving transformations that evade detection.

- [**CS-CET-002: Benign Context Framing**](classifier-evasion/cs-cet-002.md)  
  Examination of harmful content framed within seemingly benign contexts.

- [**CS-CET-003: Cross-Domain Transfer Evasion**](classifier-evasion/cs-cet-003.md)  
  Study of transferring harmful patterns across conceptual domains.

### Multimodal Vulnerability Vectors

- [**CS-MVV-001: Image-Text Inconsistency Exploitation**](multimodal/cs-mvv-001.md)  
  Analysis of security vulnerabilities in image-text processing discrepancies.

- [**CS-MVV-002: Cross-Modal Injection Chain**](multimodal/cs-mvv-002.md)  
  Examination of attack chains spanning multiple modalities.

- [**CS-MVV-003: Document Structure Manipulation**](multimodal/cs-mvv-003.md)  
  Study of document processing vulnerabilities in multimodal systems.

### Tool Use Vulnerabilities

- [**CS-TUV-001: Function Call Manipulation**](tool-use/cs-tuv-001.md)  
  Analysis of vulnerabilities in function calling mechanisms.

- [**CS-TUV-002: Parameter Injection Techniques**](tool-use/cs-tuv-002.md)  
  Examination of parameter manipulation in tool use contexts.

- [**CS-TUV-003: Tool Chain Exploitation**](tool-use/cs-tuv-003.md)  
  Study of vulnerabilities in sequences of tool operations.

## Responsible Use Guidelines

The case studies in this directory are provided for legitimate security research, testing, and improvement purposes only. When using these materials:

1. **Always operate in isolated testing environments**
2. **Follow responsible disclosure protocols** for any new vulnerabilities identified
3. **Focus on defensive applications** rather than enabling exploitation
4. **Respect the terms of service** of model providers
5. **Consider potential harmful applications** before sharing or extending these techniques

## Contributing New Case Studies

We welcome contributions of new case studies that advance the field's understanding of AI security vulnerabilities. To contribute:

1. **Follow the standard case study template**
2. **Provide complete technical details** without enabling harmful exploitation
3. **Include responsible disclosure information**
4. **Document remediation approaches**
5. **Submit a pull request** according to our [contribution guidelines](../../CONTRIBUTING.md)

For detailed guidance on developing and submitting case studies, refer to our [case study contribution guide](CONTRIBUTING.md).

## Research Integration

These case studies are designed to integrate with the broader research ecosystem:

- **Vulnerability Taxonomy**: Each case study is classified according to our [vulnerability taxonomy](../taxonomy/README.md)
- **Testing Methodologies**: Case studies inform the [testing methodologies](../methodology/README.md) in this repository
- **Benchmarking**: Vulnerabilities are incorporated into our [benchmarking frameworks](../../frameworks/benchmarking/README.md)
- **Tool Development**: Insights drive the development of [security testing tools](../../tools/README.md)

By documenting real-world vulnerabilities in a structured format, these case studies provide a foundation for systematic improvement of AI security practices.