Generic AI Testing
Runs broad payload coverage, flags model behavior, produces observations, and often requires manual context after the fact.
SPECTRA Framework
Context-Aware AI Security Testing Beyond Generic Prompt Coverage
SPECTRA is a research framework for evaluating AI systems in context. It focuses on how model behavior, system architecture, data access, tool integrations, industry context, and business impact combine to create real security risk.
What SPECTRA Is
A methodology for system-level AI security thinking
SPECTRA stands for Systematic Profiling, Exploitation, and Context-Aware Testing for Resilience of AI.
It starts by asking what the system is, what it can access, what it can do, which controls shape its behavior, and what real-world impact could follow if the model or workflow is manipulated.
The goal is not to generate more prompts. The goal is to make AI security testing more structured, contextual, and defensible.
Core Activities
Profile. Test. Chain. Remediate.
- Profile the system
- Understand defenses
- Map industry context
- Generate relevant tests
- Build attack chains
- Map remediation
The Context Problem
The same prompt can mean different things in different systems.
A prompt injection against a chatbot with no backend access is not the same as a prompt injection against an agent connected to email, CRM data, internal documents, ticketing systems, or business workflows.
The technique may be prompt injection, but the impact is determined by context.
SPECTRA
Profiles the target system, maps data and tool access, tests realistic abuse paths, and connects findings to business impact.
Model vs System
Model Testing vs. System Testing
SPECTRA separates model behavior from system risk.
| Model Testing | System Testing |
|---|---|
| Tests whether the model can be manipulated | Tests whether manipulation creates real-world risk |
| Focuses on prompts, refusals, and jailbreak behavior | Focuses on authorization, integrations, data access, workflow, and controls |
| Produces technical observations | Produces impact-driven findings |
| Often uses generic payloads | Uses industry, architecture, and threat context |
| Answers “did the model fail?” | Answers “what did the failure enable?” |
Workflow
SPECTRA Workflow
Each stage produces structured information that informs the next stage. The goal is to understand the system well enough to test it like a real adversary would.
System Profiling
→
Defense Profiling
→
Context Mapping
→
Payload Generation
→
Impact Mapping
Reconnaissance and System Profiling
Identify what the system is, what it can do, what data it can reach, and how it behaves under normal and adversarial interaction.
Defense Profiling
Evaluate input filters, model guardrails, output controls, AI gateways, authorization, retrieval controls, and human approval gates.
Attack Chain and Remediation Mapping
Connect model or workflow failure to business impact, then identify which control would most effectively break the chain.
Boundaries
What SPECTRA Is Not
SPECTRA is intentionally not positioned as a magic scanner, autonomous red teamer, or replacement for operator judgment.
Not a generic prompt list
SPECTRA is not just a library of jailbreaks or prompt injection payloads.
Not a benchmark-only suite
Benchmarks can show coverage, but they do not automatically explain business impact.
Not fully autonomous
SPECTRA is designed to preserve human judgment while using structure and tooling to support better decisions.
Not product-first
The framework defines the methodology. Future tooling can operationalize it.
Roadmap
Research and Development Roadmap
SPECTRA is being developed as a public research framework first.
Phase 1: Framework and Lab Validation
Publish the white paper, define core methodology, document workflow stages, and establish the conceptual model.
PublishedPhase 2: Knowledge Base
Build structured profiles for AI system types, industries, tools, threat personas, attack chains, and remediation patterns.
In DevelopmentPhase 3: Testing Assets
Create checklists, payload templates, finding formats, framework mappings, and sample methodology artifacts.
Planned