Core Testing Principles
The AISecForge framework is guided by a set of fundamental principles that inform all security testing methodologies. These principles ensure that our approaches are comprehensive, ethical, reproducible, and focused on improving AI system security.
1. Systematic Coverage
Definition
Security testing should comprehensively cover all model capabilities, potential attack surfaces, and vulnerability classes.
Implementation
- Map all model functionalities and capabilities before beginning testing
- Develop test suites covering each identified attack surface
- Ensure testing covers all vulnerability classes in our taxonomy
- Implement testing that addresses both known and theoretical vulnerabilities
Key Metrics
- Coverage percentage across identified attack surfaces
- Vulnerability class testing completeness
- Capability testing depth
2. Defense-in-Depth
Definition
Security testing should employ multiple layers of testing approaches, with increasing sophistication, to identify vulnerabilities that might escape simpler testing methodologies.
Implementation
- Begin with basic testing of each vulnerability class
- Progress to more sophisticated variations of each attack vector
- Combine attack vectors to test for emergent vulnerabilities
- Implement advanced evasion techniques for each test case
Key Metrics
- Testing sophistication progression
- Cross-vector testing coverage
- Advanced evasion technique incorporation
3. Reproducibility
Definition
All testing methodologies must be documented with sufficient detail to allow consistent reproduction of results across different evaluators, environments, and times.
Implementation
- Provide detailed, step-by-step testing procedures
- Specify all necessary environmental conditions
- Document exact inputs used in testing
- Establish clear evaluation criteria for test outcomes
- Version control all testing methodologies
Key Metrics
- Methodology specificity score
- Result consistency across evaluators
- Documentation completeness rating
4. Responsible Practice
Definition
All security testing must be conducted with appropriate safeguards, focusing on defensive improvement rather than exploitation, and following responsible disclosure practices.
Implementation
- Conduct all testing in isolated environments
- Focus on identification rather than exploitation of vulnerabilities
- Follow established responsible disclosure protocols
- Prioritize defense-oriented recommendations
- Maintain confidentiality of vulnerability details until patched
Key Metrics
- Ethical compliance score
- Disclosure protocol adherence
- Defense orientation rating
5. Empirical Validation
Definition
Testing methodologies should be based on empirical evidence, with continuous validation against real-world vulnerability patterns and evolving attack techniques.
Implementation
- Regularly update methodologies based on emerging vulnerability research
- Validate testing approaches against known vulnerabilities
- Incorporate feedback from actual exploitation attempts
- Benchmark against industry standards and best practices
Key Metrics
- Methodology update frequency
- Known vulnerability detection rate
- Industry standard alignment score
6. Contextual Adaptation
Definition
Testing methodologies should adapt to the specific context, capabilities, and intended use cases of the AI system under evaluation.
Implementation
- Tailor testing approaches to system-specific capabilities
- Prioritize tests based on deployment context risks
- Adjust test sophistication to match system maturity
- Consider domain-specific vulnerabilities for specialized systems
Key Metrics
- Contextual customization score
- Deployment risk alignment
- Domain-specific coverage
7. Quantitative Assessment
Definition
Testing should produce quantitative metrics that enable objective comparison, tracking of security posture over time, and prioritization of remediation efforts.
Implementation
- Apply consistent scoring methodologies
- Establish baseline measurements for comparison
- Implement multi-dimensional security metrics
- Enable trend analysis across model versions
Key Metrics
- Metric objectivity score
- Comparative analysis capability
- Trend visualization effectiveness
8. Continuous Evolution
Definition
Testing methodologies should continuously evolve to address emerging threats, new model capabilities, and advances in security research.
Implementation
- Establish a regular review cycle for all methodologies
- Incorporate feedback from the security research community
- Proactively research new attack vectors
- Maintain an emerging threats watch list
Key Metrics
- Methodology refresh rate
- New threat incorporation speed
- Research community engagement level
Applying These Principles
When developing or implementing testing methodologies:
- Begin with a principles review: Ensure your approach aligns with all eight core principles
- Perform gap analysis: Identify any principles not fully addressed in your methodology
- Document alignment: Explicitly note how each principle is implemented
- Continuous evaluation: Regularly assess methodological alignment with these principles
By consistently applying these principles, we ensure that AISecForge provides comprehensive, responsible, and effective security testing approaches for AI systems.