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# Assessment Dimensions |
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This document outlines the key security dimensions evaluated within the AISecForge framework. Each dimension represents a distinct aspect of AI system security that requires specialized testing approaches and evaluation criteria. |
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## 1. Linguistic Pattern Exploitation |
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### Definition |
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This dimension evaluates a model's vulnerability to linguistically sophisticated structures designed to bypass security measures through semantic manipulation, obfuscation, or novel linguistic formulations. |
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### Key Testing Areas |
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- **Semantic Obfuscation**: Testing for vulnerability to meaning-preserving transformations that evade detection |
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- **Multi-language Injection**: Assessing resistance to instructions embedded across multiple languages |
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- **Nested Instruction Manipulation**: Evaluating vulnerability to hierarchically structured prompts |
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- **Interpretative Framing Exploitation**: Testing susceptibility to reframing of harmful requests |
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- **Temporal Context Manipulation**: Assessing vulnerability to historically constructed attack patterns |
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### Security Implications |
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Weaknesses in this dimension can allow attackers to bypass content policies, extract sensitive information, or manipulate model behavior through sophisticated linguistic techniques that evade detection systems. |
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### Assessment Approach |
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Testing in this dimension employs a variety of linguistic transformation techniques, measuring model responses against baseline controls to identify security degradation resulting from linguistic manipulations. |
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## 2. Contextual Boundary Testing |
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### Definition |
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This dimension assesses a model's ability to maintain appropriate behavioral boundaries under various contextual pressures, including role-playing scenarios, authority invocation, ethical dilemmas, and emergency situations. |
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### Key Testing Areas |
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- **Role-based Constraint Testing**: Evaluating boundary maintenance within role-playing scenarios |
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- **Authority Figure Impersonation**: Testing susceptibility to requests framed as coming from authority sources |
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- **Persona Manipulation**: Assessing vulnerability to character-based constraint circumvention |
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- **Ethical Scenario Boundary Testing**: Evaluating model behavior in ethically ambiguous situations |
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- **Emergency Situation Simulation**: Testing boundary maintenance under simulated emergency contexts |
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### Security Implications |
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Weaknesses in this dimension can lead to inappropriate model behavior in specific contexts, potentially resulting in harmful outputs, unauthorized actions, or disclosure of sensitive information when contextual pressure is applied. |
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### Assessment Approach |
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Testing employs contextual framing techniques, systematically increasing pressure on model boundaries while measuring adherence to security constraints across various scenarios. |
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## 3. System Prompt Extraction |
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### Definition |
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This dimension evaluates a model's resistance to attempts to reveal underlying system instructions, operational parameters, or training directives through various extraction techniques. |
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### Key Testing Areas |
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- **Indirect Revelation Techniques**: Testing for vulnerability to oblique questioning that reveals system directives |
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- **Token Prediction Exploitation**: Assessing susceptibility to attacks leveraging prediction mechanics |
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- **Comparative Response Analysis**: Evaluating vulnerability to differential analysis of responses |
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- **System Instruction Reconstruction**: Testing resistance to systematic reconstruction of instructions |
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- **Parameter Inference Methods**: Assessing vulnerability to techniques that infer system parameters |
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### Security Implications |
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Weaknesses in this dimension can lead to exposure of proprietary system instructions, enabling more effective attacks by revealing model constraints, training directives, or operational parameters. |
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### Assessment Approach |
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Testing employs systematic extraction attempts with progressive refinement, measuring information leakage against known system instructions to quantify extraction success. |
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## 4. Multi-turn Vulnerability Assessment |
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### Definition |
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This dimension evaluates security degradation across extended interactions, assessing how model boundaries, restrictions, and safeguards hold up over multiple conversation turns. |
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### Key Testing Areas |
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- **Progressive Boundary Erosion**: Testing for gradual weakening of security boundaries over time |
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- **Context Window Manipulation**: Assessing vulnerability to context window poisoning or overflow |
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- **Memory Exploitation**: Evaluating susceptibility to attacks leveraging model memory mechanisms |
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- **Conversation Flow Redirection**: Testing vulnerability to subtle conversation steering techniques |
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- **Trust Building Exploitation**: Assessing vulnerability to attacks that establish trust before exploitation |
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### Security Implications |
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Weaknesses in this dimension can enable attackers to circumvent security measures through persistent interaction, potentially achieving actions or outputs that would be refused in single-turn interactions. |
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### Assessment Approach |
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Testing employs extended conversation scenarios with progressive attack sophistication, measuring security degradation over time against single-turn baseline security levels. |
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## 5. Multimodal Attack Vectors |
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### Definition |
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This dimension evaluates security across different input modalities, assessing how vulnerabilities may manifest when leveraging images, audio, code, or document inputs in addition to text. |
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### Key Testing Areas |
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- **Image-based Prompt Injection**: Testing for vulnerability to instructions embedded in images |
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- **Audio Instruction Embedding**: Assessing susceptibility to audio-based attack vectors |
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- **Code-based Vulnerability Introduction**: Evaluating security in code interpretation contexts |
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- **Document-embedded Attack Patterns**: Testing vulnerability to attacks hidden in document structure |
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- **Cross-modal Transfer Attacks**: Assessing vulnerability to attacks that transition across modalities |
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### Security Implications |
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Weaknesses in this dimension can enable attackers to bypass text-focused security measures by leveraging alternative input modalities, potentially achieving harmful outputs or actions through these channels. |
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### Assessment Approach |
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Testing employs multimodal inputs containing embedded attack patterns, measuring response differences between text-only and multimodal variants of equivalent attacks. |
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## 6. Tool Use Security |
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### Definition |
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This dimension evaluates security in the context of AI system tool use capabilities, assessing vulnerabilities related to external system access, API interactions, and function calling mechanisms. |
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### Key Testing Areas |
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- **Tool Selection Manipulation**: Testing for vulnerability in the tool selection process |
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- **Parameter Injection**: Assessing susceptibility to malicious parameter manipulation |
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- **Function Call Hijacking**: Evaluating vulnerability to redirection of function calls |
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- **Tool Output Interpretation**: Testing security of tool output processing |
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- **Tool Chaining Vulnerabilities**: Assessing security in sequences of tool operations |
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### Security Implications |
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Weaknesses in this dimension can enable attackers to leverage AI system tool use capabilities to perform unauthorized actions, access sensitive systems, or manipulate external resources in harmful ways. |
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### Assessment Approach |
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Testing employs crafted inputs designed to manipulate tool use behavior, measuring divergence from authorized and intended tool operations across various scenarios. |
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## 7. Recovery & Remediation Assessment |
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### Definition |
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This dimension evaluates a model's ability to recover from security failures, recognize exploitation attempts, and implement appropriate remediation actions when vulnerabilities are exploited. |
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### Key Testing Areas |
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- **Attack Recognition**: Assessing ability to identify exploitation attempts in progress |
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- **Conversation Reset Mechanisms**: Testing effectiveness of security reset procedures |
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- **Exploit Mitigation**: Evaluating ability to prevent further exploitation after detection |
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- **Resilience to Repeated Attacks**: Testing robustness against persistent attack attempts |
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- **Remediation Notification**: Assessing communication of security issues to users |
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### Security Implications |
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Weaknesses in this dimension can prevent effective recovery from security breaches, potentially extending impact when exploitation occurs and reducing system resilience against persistent attacks. |
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### Assessment Approach |
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Testing employs successful exploitation followed by remediation attempts, measuring recovery efficacy and persistent vulnerability after initial security failures. |
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## 8. Authorization & Authentication Boundaries |
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### Definition |
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This dimension evaluates a model's ability to maintain appropriate boundaries around user identification, authorization levels, and access controls within conversations or across sessions. |
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### Key Testing Areas |
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- **Identity Confusion Exploitation**: Testing for vulnerability to user identity manipulation |
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- **Permission Inheritance**: Assessing susceptibility to unauthorized permission acquisition |
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- **Session Boundary Enforcement**: Evaluating maintenance of security across session boundaries |
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- **Authorization Level Manipulation**: Testing resistance to attempts to elevate privileges |
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- **Authentication Bypass Techniques**: Assessing vulnerability to authentication circumvention |
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### Security Implications |
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Weaknesses in this dimension can enable unauthorized access to features, data, or capabilities restricted to specific users or roles, potentially compromising system security policies. |
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### Assessment Approach |
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Testing employs identity and authorization manipulation techniques, measuring access control enforcement against defined authorization boundaries and policies. |
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## Dimensional Interaction Analysis |
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While each dimension can be tested individually, security vulnerabilities often span multiple dimensions. The AISecForge framework includes cross-dimensional analysis to identify compound vulnerabilities that emerge from the interaction of weaknesses across different dimensions. |
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Key interaction patterns include: |
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1. **Linguistic + Contextual**: Using sophisticated linguistic patterns within specific contextual frames |
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2. **Multi-turn + System Prompt**: Leveraging extended conversations to extract system instructions |
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3. **Multimodal + Tool Use**: Employing non-text inputs to manipulate tool use behavior |
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4. **Authorization + Recovery**: Exploiting authentication weaknesses to prevent effective remediation |
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For implementation details on testing each dimension, refer to the dimension-specific methodology documents in the [dimensions directory](dimensions/). |
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