case-studies.md

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
  Analysis of techniques for indirectly modifying system instructions through contextual reframing.

• CS-PJV-002: Cross-Context Injection via Documentation
  Exploration of vulnerabilities where model documentation becomes an attack vector.

• CS-PJV-003: Hierarchical Nesting Techniques
  Analysis of exploitation through multiple levels of nested instruction contexts.

Boundary Enforcement Failures

• CS-BEF-001: Progressive Desensitization
  Examination of gradual boundary erosion through incremental requests.

• CS-BEF-002: Context Window Contamination
  Analysis of security failures through strategic context window manipulation.

• CS-BEF-003: Role-Based Constraint Bypass
  Study of how role-playing scenarios can be leveraged to bypass constraints.

Information Extraction Vulnerabilities

• CS-IEV-001: System Instruction Extraction
  Analysis of techniques for revealing underlying system instructions.

• CS-IEV-002: Parameter Inference Methodology
  Examination of approaches to infer model parameters and configurations.

• CS-IEV-003: Training Data Extraction Patterns
  Study of methods for extracting specific training data elements.

Classifier Evasion Techniques

• CS-CET-001: Semantic Equivalent Substitution
  Analysis of meaning-preserving transformations that evade detection.

• CS-CET-002: Benign Context Framing
  Examination of harmful content framed within seemingly benign contexts.

• CS-CET-003: Cross-Domain Transfer Evasion
  Study of transferring harmful patterns across conceptual domains.

Multimodal Vulnerability Vectors

• CS-MVV-001: Image-Text Inconsistency Exploitation
  Analysis of security vulnerabilities in image-text processing discrepancies.

• CS-MVV-002: Cross-Modal Injection Chain
  Examination of attack chains spanning multiple modalities.

• CS-MVV-003: Document Structure Manipulation
  Study of document processing vulnerabilities in multimodal systems.

Tool Use Vulnerabilities

• CS-TUV-001: Function Call Manipulation
  Analysis of vulnerabilities in function calling mechanisms.

• CS-TUV-002: Parameter Injection Techniques
  Examination of parameter manipulation in tool use contexts.

• CS-TUV-003: Tool Chain Exploitation
  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

For detailed guidance on developing and submitting case studies, refer to our case study contribution guide.

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
• Testing Methodologies: Case studies inform the testing methodologies in this repository
• Benchmarking: Vulnerabilities are incorporated into our benchmarking frameworks
• Tool Development: Insights drive the development of security testing tools

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