class diagram - osok/hawkeye GitHub Wiki

AI Threat Analysis Class Diagram

Overview

The HawkEye AI threat analysis module implements a sophisticated architecture for AI-powered security threat analysis of MCP servers. The system uses multiple AI providers, advanced prompt engineering, capability analysis, and threat modeling to generate comprehensive security assessments. This document provides detailed class diagrams and architectural analysis of the AI threat analysis components.

High-Level Architecture

The AI threat analysis module follows a layered architecture with clear separation between analysis orchestration, AI provider abstraction, data processing, and result generation:

  1. Orchestration Layer: Main analyzer and coordinator components
  2. AI Provider Layer: Abstract provider interface with multiple implementations
  3. Analysis Layer: Specialized analyzers for capabilities, threats, and attack chains
  4. Data Layer: Models, caches, and data structures
  5. Support Layer: Utilities, monitoring, and optimization components
classDiagram
    class AIThreatAnalyzer {
        -capability_analyzer: MCPCapabilityAnalyzer
        -prompt_engine: ThreatAnalysisPrompts
        -ai_provider: AIProvider
        -fallback_provider: AIProvider
        -cache: ThreatIntelligenceCache
        -memory_optimizer: MemoryOptimizer
        -response_monitor: ResponseTimeMonitor
        -stats: Dict[str, Any]
        
        +__init__(config)
        +analyze_threats(server, context, type) ThreatAnalysis
        +assess_capabilities(server) ToolCapabilities
        +generate_threat_report(analysis) str
        +get_statistics() Dict[str, Any]
        +clear_cache() None
        +shutdown() None
    }
    
    class AIProvider {
        <<abstract>>
        #config: Dict[str, Any]
        #prompt_engine: ThreatAnalysisPrompts
        #response_parser: ResponseParser
        #retry_handler: AdvancedRetryHandler
        #usage_stats: Dict[str, Any]
        
        +__init__(config)
        +generate_threat_analysis(request)* AnalysisResponse
        +assess_risk_level(capabilities)* Tuple[ThreatLevel, float]
        +estimate_cost(request)* float
        +get_usage_stats() Dict[str, Any]
        #_update_stats(success, cost, tokens) None
        #_execute_with_retry(operation, provider, name) Any
    }
    
    class MCPCapabilityAnalyzer {
        -risk_categories: Dict[str, List[str]]
        -dependency_patterns: Dict[str, Pattern]
        -security_analyzers: List[SecurityAnalyzer]
        
        +__init__()
        +analyze_capabilities(server) ToolCapabilities
        +analyze_tool_functions(tools) List[ToolFunction]
        +assess_risk_surface(capabilities) RiskSurface
        +build_environment_context(system_info) EnvironmentContext
        +categorize_capabilities(functions) Dict[CapabilityCategory, List[str]]
    }
    
    class ThreatModeler {
        -stride_analyzer: STRIDEAnalyzer
        -attack_tree_builder: AttackTreeBuilder
        -threat_patterns: List[ThreatPattern]
        
        +__init__()
        +model_threats(capabilities, context) ThreatModelingResult
        +build_attack_tree(threat) AttackTree
        +analyze_stride_threats(capabilities) List[STRIDEThreat]
        +generate_threat_scenarios(analysis) List[ThreatScenario]
    }
    
    class AttackChainAnalyzer {
        -chain_patterns: List[ChainPattern]
        -feasibility_calculator: FeasibilityCalculator
        -impact_assessor: ImpactAssessor
        
        +__init__()
        +analyze_attack_chains(threats) List[AttackChain]
        +calculate_chain_feasibility(chain) ChainFeasibilityScore
        +assess_chain_impact(chain) ImpactScore
        +generate_attack_paths(start, end) List[AttackPath]
    }
    
    AIThreatAnalyzer --> AIProvider : uses
    AIThreatAnalyzer --> MCPCapabilityAnalyzer : uses
    AIThreatAnalyzer --> ThreatModeler : uses
    AIThreatAnalyzer --> AttackChainAnalyzer : uses
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AI Provider Hierarchy

Abstract Provider Interface

classDiagram
    class AIProvider {
        <<abstract>>
        #config: Dict[str, Any]
        #prompt_engine: ThreatAnalysisPrompts
        #response_parser: ResponseParser
        #retry_handler: AdvancedRetryHandler
        #usage_stats: Dict[str, Any]
        
        +__init__(config: Dict[str, Any])
        +generate_threat_analysis(request: AnalysisRequest)* AnalysisResponse
        +assess_risk_level(capabilities: List[str])* Tuple[ThreatLevel, float]
        +estimate_cost(request: AnalysisRequest)* float
        +get_usage_stats() Dict[str, Any]
        +_update_stats(success: bool, cost: float, tokens: int) None
        +_execute_with_retry(operation: Callable, provider: str, name: str) Any
    }
    
    class OpenAIProvider {
        -client: OpenAI
        -model: str
        -temperature: float
        -max_tokens: int
        -cost_per_token: Dict[str, float]
        
        +__init__(config: Dict[str, Any])
        +generate_threat_analysis(request: AnalysisRequest) AnalysisResponse
        +assess_risk_level(capabilities: List[str]) Tuple[ThreatLevel, float]
        +estimate_cost(request: AnalysisRequest) float
        +_make_api_call(messages: List[Dict]) Dict[str, Any]
        +_calculate_cost(usage: Dict) float
        +_handle_rate_limit(retry_after: int) None
    }
    
    class AnthropicProvider {
        -client: Anthropic
        -model: str
        -max_tokens: int
        -temperature: float
        -cost_per_token: Dict[str, float]
        
        +__init__(config: Dict[str, Any])
        +generate_threat_analysis(request: AnalysisRequest) AnalysisResponse
        +assess_risk_level(capabilities: List[str]) Tuple[ThreatLevel, float]
        +estimate_cost(request: AnalysisRequest) float
        +_make_api_call(messages: List[Dict]) Dict[str, Any]
        +_calculate_cost(usage: Dict) float
        +_handle_anthropic_errors(error: Exception) None
    }
    
    class LocalLLMProvider {
        -model_path: str
        -model: Any
        -tokenizer: Any
        -device: str
        -generation_config: Dict[str, Any]
        
        +__init__(config: Dict[str, Any])
        +generate_threat_analysis(request: AnalysisRequest) AnalysisResponse
        +assess_risk_level(capabilities: List[str]) Tuple[ThreatLevel, float]
        +estimate_cost(request: AnalysisRequest) float
        +_load_model() None
        +_generate_local_response(prompt: str) str
        +_optimize_for_hardware() None
    }
    
    AIProvider <|-- OpenAIProvider
    AIProvider <|-- AnthropicProvider
    AIProvider <|-- LocalLLMProvider
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Provider Support Components

classDiagram
    class AdvancedRetryHandler {
        -retry_config: RetryConfig
        -circuit_breaker_config: CircuitBreakerConfig
        -error_classifier: ErrorClassifier
        -provider_health: Dict[str, ProviderHealth]
        -_lock: threading.Lock
        
        +__init__(retry_config, circuit_breaker_config)
        +execute_with_retry(operation, provider) Any
        +is_circuit_breaker_open(provider) bool
        +record_success(provider, response_time) None
        +record_failure(provider, error) None
        +get_health_statistics() Dict[str, Any]
        +reset_circuit_breaker(provider) None
    }
    
    class ResponseParser {
        -parsing_stats: Dict[str, int]
        -confidence_threshold: float
        -validation_rules: List[ValidationRule]
        
        +__init__(confidence_threshold)
        +parse_threat_analysis(response, analysis_type, capabilities, context) ThreatAnalysis
        +validate_response_structure(data) bool
        +extract_threat_components(data) Dict[str, Any]
        +calculate_confidence_score(analysis) float
        +get_parsing_statistics() Dict[str, Any]
    }
    
    class ErrorClassifier {
        -error_patterns: Dict[ErrorType, List[str]]
        
        +__init__()
        +classify_error(error_message) ErrorType
        +is_retryable_error(error_type) bool
        +get_retry_delay(error_type, attempt) float
    }
    
    class CircuitBreaker {
        -state: CircuitBreakerState
        -failure_count: int
        -success_count: int
        -last_failure_time: datetime
        -config: CircuitBreakerConfig
        
        +__init__(config)
        +call(operation) Any
        +record_success() None
        +record_failure() None
        +is_open() bool
        +is_half_open() bool
    }
    
    AIProvider --> AdvancedRetryHandler : uses
    AIProvider --> ResponseParser : uses
    AdvancedRetryHandler --> ErrorClassifier : uses
    AdvancedRetryHandler --> CircuitBreaker : uses
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Analysis Components

Capability Analysis System

classDiagram
    class MCPCapabilityAnalyzer {
        -risk_categories: Dict[str, List[str]]
        -dependency_patterns: Dict[str, Pattern]
        -security_analyzers: List[SecurityAnalyzer]
        -system_profiler: SystemProfiler
        
        +__init__()
        +analyze_capabilities(server: MCPServerInfo) ToolCapabilities
        +analyze_tool_functions(tools: List[MCPTool]) List[ToolFunction]
        +assess_risk_surface(capabilities: ToolCapabilities) RiskSurface
        +build_environment_context(system_info: SystemInfo) EnvironmentContext
        +categorize_capabilities(functions: List[ToolFunction]) Dict[CapabilityCategory, List[str]]
        +detect_external_dependencies(tools: List[MCPTool]) List[ExternalDependency]
        +assess_access_requirements(tools: List[MCPTool]) AccessRequirements
        +profile_security_posture() SecurityPosture
    }
    
    class ToolFunction {
        +name: str
        +description: str
        +parameters: Dict[str, Any]
        +return_type: str
        +risk_score: float
        +category: CapabilityCategory
        +security_implications: List[str]
        +required_permissions: List[str]
        +external_dependencies: List[str]
        
        +__init__(name, description, parameters)
        +calculate_risk_score() float
        +get_security_implications() List[str]
        +requires_elevated_privileges() bool
    }
    
    class RiskSurface {
        +total_functions: int
        +high_risk_functions: int
        +external_network_access: bool
        +file_system_access: bool
        +system_command_execution: bool
        +database_access: bool
        +api_integrations: List[str]
        +privilege_requirements: List[AccessLevel]
        +data_sensitivity_levels: List[DataSensitivity]
        
        +calculate_overall_risk_score() float
        +get_critical_attack_vectors() List[str]
        +assess_containment_feasibility() float
    }
    
    class SystemProfiler {
        +gather_system_info() SystemInfo
        +detect_containerization() bool
        +assess_network_exposure() NetworkExposure
        +determine_user_privileges() UserPrivileges
        +identify_security_controls() List[str]
        +analyze_deployment_context() DeploymentType
    }
    
    MCPCapabilityAnalyzer --> ToolFunction : creates
    MCPCapabilityAnalyzer --> RiskSurface : creates
    MCPCapabilityAnalyzer --> SystemProfiler : uses
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Threat Modeling Engine

classDiagram
    class ThreatModeler {
        -stride_analyzer: STRIDEAnalyzer
        -attack_tree_builder: AttackTreeBuilder
        -threat_patterns: List[ThreatPattern]
        -mitigation_database: MitigationDatabase
        
        +__init__()
        +model_threats(capabilities: ToolCapabilities, context: EnvironmentContext) ThreatModelingResult
        +build_attack_tree(threat: ThreatScenario) AttackTree
        +analyze_stride_threats(capabilities: ToolCapabilities) List[STRIDEThreat]
        +generate_threat_scenarios(analysis: ThreatAnalysis) List[ThreatScenario]
        +map_mitigations(threats: List[ThreatScenario]) List[MitigationStrategy]
        +assess_threat_likelihood(scenario: ThreatScenario, context: EnvironmentContext) float
    }
    
    class STRIDEAnalyzer {
        +analyze_spoofing_threats(capabilities) List[ThreatScenario]
        +analyze_tampering_threats(capabilities) List[ThreatScenario]
        +analyze_repudiation_threats(capabilities) List[ThreatScenario]
        +analyze_information_disclosure_threats(capabilities) List[ThreatScenario]
        +analyze_denial_of_service_threats(capabilities) List[ThreatScenario]
        +analyze_elevation_of_privilege_threats(capabilities) List[ThreatScenario]
    }
    
    class AttackTreeBuilder {
        +build_tree(root_threat: ThreatScenario) AttackTree
        +add_attack_vector(tree: AttackTree, vector: AttackVector) None
        +calculate_attack_probability(tree: AttackTree) float
        +identify_critical_paths(tree: AttackTree) List[AttackPath]
        +optimize_tree_structure(tree: AttackTree) AttackTree
    }
    
    class AttackTree {
        +root_node: AttackTreeNode
        +nodes: List[AttackTreeNode]
        +edges: List[AttackTreeEdge]
        +metadata: Dict[str, Any]
        
        +add_node(node: AttackTreeNode) None
        +add_edge(source: str, target: str, relationship: str) None
        +calculate_overall_probability() float
        +get_attack_paths() List[AttackPath]
        +visualize() str
    }
    
    ThreatModeler --> STRIDEAnalyzer : uses
    ThreatModeler --> AttackTreeBuilder : uses
    AttackTreeBuilder --> AttackTree : creates
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Attack Chain Analysis System

classDiagram
    class AttackChainAnalyzer {
        -chain_patterns: List[ChainPattern]
        -feasibility_calculator: FeasibilityCalculator
        -impact_assessor: ImpactAssessor
        -chain_optimizer: ChainOptimizer
        
        +__init__()
        +analyze_attack_chains(threats: List[ThreatScenario]) List[AttackChain]
        +calculate_chain_feasibility(chain: AttackChain) ChainFeasibilityScore  
        +assess_chain_impact(chain: AttackChain) ImpactScore
        +generate_attack_paths(start: str, end: str) List[AttackPath]
        +optimize_attack_chains(chains: List[AttackChain]) List[AttackChain]
        +validate_chain_logic(chain: AttackChain) bool
    }
    
    class AttackChain {
        +chain_id: str
        +name: str
        +description: str
        +links: List[ChainLink]
        +overall_feasibility: float
        +impact_score: float
        +estimated_time: str
        +required_skills: List[str]
        +detection_difficulty: DifficultyLevel
        
        +add_link(link: ChainLink) None
        +calculate_feasibility() float
        +get_critical_dependencies() List[str]
        +estimate_execution_time() str
    }
    
    class ChainLink {
        +link_id: str
        +attack_vector: AttackVector
        +prerequisites: List[str]
        +outcomes: List[str]
        +success_probability: float
        +detection_probability: float
        +mitigation_effectiveness: float
        
        +validate_prerequisites(context: Dict) bool
        +calculate_success_rate(environment: EnvironmentContext) float
        +get_detection_indicators() List[DetectionIndicator]
    }
    
    class FeasibilityCalculator {
        +calculate_technical_feasibility(chain: AttackChain) float
        +calculate_resource_requirements(chain: AttackChain) Dict[str, Any]
        +assess_skill_requirements(chain: AttackChain) List[str]
        +estimate_execution_complexity(chain: AttackChain) ComplexityLevel
    }
    
    AttackChainAnalyzer --> AttackChain : creates
    AttackChain --> ChainLink : contains
    AttackChainAnalyzer --> FeasibilityCalculator : uses
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Data Models and Structures

Core Analysis Data Models

classDiagram
    class ThreatAnalysis {
        +server_id: str
        +analysis_timestamp: datetime
        +tool_capabilities: ToolCapabilities
        +environment_context: EnvironmentContext
        +threat_level: ThreatLevel
        +attack_vectors: List[AttackVector]
        +abuse_scenarios: List[AbuseScenario]
        +mitigation_strategies: List[MitigationStrategy]
        +detection_indicators: List[DetectionIndicator]
        +compliance_impact: ComplianceImpact
        +business_impact: BusinessImpact
        +confidence_score: float
        +analysis_metadata: AnalysisMetadata
        +risk_score: float
        +severity_level: SeverityLevel
        
        +from_dict(data: Dict) ThreatAnalysis
        +to_dict() Dict[str, Any]
        +calculate_overall_risk() float
        +get_critical_findings() List[str]
        +validate_analysis() bool
    }
    
    class AttackVector {
        +vector_id: str
        +name: str
        +severity: SeverityLevel
        +description: str
        +attack_steps: List[AttackStep]
        +example_code: str
        +prerequisites: List[str]
        +impact: str
        +likelihood: float
        +mitigations: List[str]
        +cvss_score: float
        +cwe_mapping: List[str]
        
        +calculate_risk_score() float
        +get_attack_complexity() ComplexityLevel
        +estimate_exploitation_time() str
    }
    
    class AbuseScenario {
        +scenario_id: str
        +scenario_name: str
        +threat_actor: ThreatActorType
        +motivation: str
        +attack_flow: List[AttackStep]
        +required_access: AccessLevel
        +detection_difficulty: DifficultyLevel
        +business_impact: BusinessImpact
        +timeline: str
        +success_indicators: List[str]
        
        +validate_scenario() bool
        +calculate_likelihood() float
        +get_impact_assessment() Dict[str, Any]
    }
    
    class MitigationStrategy {
        +strategy_id: str
        +name: str
        +description: str
        +implementation_steps: List[str]
        +effectiveness_score: float
        +cost_estimate: str
        +implementation_time: str
        +requires_configuration: bool
        +affects_functionality: bool
        +compliance_alignment: List[ComplianceFramework]
        
        +assess_implementation_complexity() ComplexityLevel
        +calculate_cost_benefit_ratio() float
        +validate_strategy() bool
    }
    
    ThreatAnalysis --> AttackVector : contains
    ThreatAnalysis --> AbuseScenario : contains  
    ThreatAnalysis --> MitigationStrategy : contains
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Environment and Context Models

classDiagram
    class EnvironmentContext {
        +deployment_type: DeploymentType
        +security_posture: SecurityPosture
        +network_exposure: NetworkExposure
        +data_sensitivity: DataSensitivity
        +user_privileges: UserPrivileges
        +compliance_requirements: List[ComplianceFramework]
        +system_hardening: Dict[str, bool]
        +monitoring_capabilities: List[str]
        +incident_response_maturity: int
        
        +assess_overall_security_posture() float
        +get_risk_modifiers() Dict[str, float]
        +validate_context() bool
    }
    
    class ToolCapabilities {
        +total_functions: int
        +function_categories: Dict[CapabilityCategory, int]
        +high_risk_functions: List[ToolFunction]
        +external_dependencies: List[ExternalDependency]
        +access_requirements: AccessRequirements
        +data_access_patterns: List[str]
        +network_access_patterns: List[str]
        +privilege_escalation_potential: float
        
        +calculate_capability_score() float
        +get_attack_surface() Dict[str, Any]
        +assess_containment_difficulty() float
    }
    
    class SystemInfo {
        +platform: str
        +python_version: str
        +memory_total: int
        +cpu_count: int
        +disk_usage: Dict[str, Any]
        +network_interfaces: List[Dict[str, Any]]
        +running_processes: List[str]
        +environment_variables: Dict[str, str]
        +installed_packages: List[str]
        +user_groups: List[str]
        +system_uptime: float
        +security_features: Dict[str, bool]
        
        +gather_system_info() SystemInfo
        +assess_security_baseline() Dict[str, Any]
        +detect_anomalies() List[str]
    }
    
    EnvironmentContext --> SystemInfo : uses
    ThreatAnalysis --> EnvironmentContext : uses
    ThreatAnalysis --> ToolCapabilities : uses
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Support and Utility Components

Caching and Optimization

classDiagram
    class ThreatIntelligenceCache {
        -cache_data: Dict[str, CacheEntry]
        -ttl: int
        -max_size: int
        -hit_count: int
        -miss_count: int
        -_lock: threading.Lock
        
        +__init__(ttl: int, max_size: int)
        +get(key: str) Optional[Any]
        +set(key: str, value: Any, ttl: Optional[int]) None
        +invalidate(key: str) None
        +clear() None
        +get_statistics() Dict[str, Any]
        +cleanup_expired() None
    }
    
    class MemoryOptimizer {
        -optimization_level: MemoryOptimizationLevel
        -config: MemoryConfig
        -cleanup_threshold: float
        -monitoring_enabled: bool
        
        +__init__(level: MemoryOptimizationLevel, config: MemoryConfig)
        +start_optimization() None
        +stop_optimization() None
        +force_cleanup() None
        +get_memory_usage() Dict[str, Any]
        +optimize_data_structures() None
        +monitor_memory_usage() None
    }
    
    class ResponseTimeMonitor {
        -response_times: deque
        -window_size: int
        -warning_threshold: float
        -alert_threshold: float
        -statistics: Dict[str, float]
        
        +__init__(window_size: int)
        +record_response_time(duration: float) None
        +get_average_response_time() float
        +get_percentile(percentile: float) float
        +is_performance_degraded() bool
        +get_statistics() Dict[str, Any]
    }
    
    AIThreatAnalyzer --> ThreatIntelligenceCache : uses
    AIThreatAnalyzer --> MemoryOptimizer : uses
    AIThreatAnalyzer --> ResponseTimeMonitor : uses
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Advanced Analysis Components

classDiagram
    class EnhancedProviderSelector {
        -selection_criteria: SelectionCriteria
        -provider_metrics: Dict[str, ProviderMetrics]
        -load_balancer: ProviderLoadBalancer
        
        +__init__(criteria: SelectionCriteria)
        +select_provider(request: AnalysisRequest) ProviderSelection
        +update_provider_metrics(provider: str, metrics: Dict) None
        +get_optimal_provider_mix() List[str]
        +assess_provider_health() Dict[str, bool]
    }
    
    class RiskPrioritizationAlgorithm {
        -technical_weight: float
        -business_weight: float
        -likelihood_weight: float
        -environmental_modifiers: EnvironmentalModifiers
        
        +__init__(weights: Dict[str, float])
        +prioritize_threats(threats: List[ThreatAnalysis]) List[PrioritizedThreat]
        +calculate_priority_score(threat: ThreatAnalysis) float
        +apply_environmental_modifiers(score: float, context: EnvironmentContext) float
        +generate_priority_matrix() PriorityMatrix
    }
    
    class ThreatIntelligenceDB {
        -threat_patterns: List[ThreatPattern]
        -similarity_engine: SimilarityEngine
        -learning_metrics: LearningMetrics
        -pattern_cache: Dict[str, Any]
        
        +__init__()
        +find_similar_threats(analysis: ThreatAnalysis) List[SimilarityMatch]
        +update_threat_patterns(new_pattern: ThreatPattern) None
        +learn_from_analysis(analysis: ThreatAnalysis) None
        +get_threat_statistics() Dict[str, Any]
    }
    
    class DynamicExampleGenerator {
        -example_templates: Dict[ExampleType, List[str]]
        -code_generators: Dict[ProgrammingLanguage, CodeGenerator]
        -context_analyzer: ContextAnalyzer
        
        +__init__()
        +generate_examples(threat: AttackVector, context: ExampleGenerationContext) List[GeneratedExample]
        +create_code_snippet(vector: AttackVector, language: ProgrammingLanguage) CodeSnippet
        +adapt_example_to_context(example: GeneratedExample, context: Dict) GeneratedExample
    }
    
    AIThreatAnalyzer --> EnhancedProviderSelector : uses
    AIThreatAnalyzer --> RiskPrioritizationAlgorithm : uses
    AIThreatAnalyzer --> ThreatIntelligenceDB : uses
    AIThreatAnalyzer --> DynamicExampleGenerator : uses
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Component Interaction Flow

Threat Analysis Orchestration Flow

sequenceDiagram
    participant CLI as CLI Command
    participant Analyzer as AIThreatAnalyzer
    participant CapAnalyzer as MCPCapabilityAnalyzer
    participant Provider as AIProvider
    participant Parser as ResponseParser
    participant Modeler as ThreatModeler
    participant ChainAnalyzer as AttackChainAnalyzer
    
    CLI->>Analyzer: analyze_threats(server, context)
    Analyzer->>CapAnalyzer: analyze_capabilities(server)
    CapAnalyzer->>Analyzer: ToolCapabilities
    
    Analyzer->>Analyzer: build_analysis_request(capabilities, context)
    Analyzer->>Provider: generate_threat_analysis(request)
    
    Provider->>Provider: _execute_with_retry(api_call)
    Provider->>Parser: parse_threat_analysis(response)
    Parser->>Provider: ThreatAnalysis
    
    Provider->>Analyzer: AnalysisResponse
    
    Analyzer->>Modeler: model_threats(capabilities, context)
    Modeler->>Analyzer: ThreatModelingResult
    
    Analyzer->>ChainAnalyzer: analyze_attack_chains(threats)
    ChainAnalyzer->>Analyzer: List[AttackChain]
    
    Analyzer->>Analyzer: aggregate_analysis_results()
    Analyzer->>CLI: ThreatAnalysis
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Provider Selection and Failover

stateDiagram-v2
    [*] --> ProviderSelection
    ProviderSelection --> PrimaryProvider : Provider Available
    ProviderSelection --> FallbackProvider : Primary Unavailable
    
    PrimaryProvider --> ExecuteRequest : Circuit Closed
    PrimaryProvider --> FallbackProvider : Circuit Open
    
    ExecuteRequest --> Success : Request Successful
    ExecuteRequest --> RetryLogic : Request Failed
    
    RetryLogic --> ExecuteRequest : Retry Available
    RetryLogic --> FallbackProvider : Max Retries Reached
    RetryLogic --> Failed : All Providers Failed
    
    FallbackProvider --> ExecuteRequest : Switch to Fallback
    
    Success --> [*]
    Failed --> [*]
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Memory Optimization Lifecycle

flowchart TD
    A[Memory Monitor Start] --> B[Monitor Memory Usage]
    B --> C{Memory Above Threshold?}
    C -->|No| B
    C -->|Yes| D[Trigger Cleanup]
    
    D --> E[Clear Cache Entries]
    E --> F[Compress Data Structures]
    F --> G[Release Unused Objects]
    G --> H[Force Garbage Collection]
    
    H --> I{Memory Still High?}
    I -->|No| J[Log Success]
    I -->|Yes| K[Escalate to Aggressive Cleanup]
    
    K --> L[Clear All Caches]
    L --> M[Restart Components]
    M --> N[Log Warning]
    
    J --> B
    N --> B
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Performance Characteristics

Concurrent Processing Architecture

graph TD
    A[Main Analysis Thread] --> B[Provider Selection]
    B --> C[Request Preparation]
    C --> D[Parallel Processing]
    
    D --> E[OpenAI Provider Thread]
    D --> F[Anthropic Provider Thread] 
    D --> G[Local LLM Thread]
    
    E --> H[Response Processing]
    F --> H
    G --> H
    
    H --> I[Result Aggregation]
    I --> J[Quality Assessment]
    J --> K[Final Analysis Result]
    
    subgraph "Memory Management"
        L[Memory Monitor]
        M[Cache Manager]
        N[Garbage Collector]
    end
    
    subgraph "Error Handling"
        O[Retry Logic]
        P[Circuit Breaker]
        Q[Fallback Strategy]
    end
    
    H --> L
    H --> O
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Optimization Strategies

Performance Optimizations:

  • Provider Load Balancing: Distribute requests across multiple AI providers
  • Response Caching: Cache similar analysis requests to reduce API calls
  • Memory Management: Proactive memory cleanup and optimization
  • Circuit Breakers: Prevent cascading failures in provider chains
  • Parallel Processing: Concurrent analysis of multiple components

Cost Optimizations:

  • Request Batching: Combine related analysis requests
  • Smart Provider Selection: Choose cost-effective providers based on request type
  • Token Optimization: Minimize prompt length while maintaining quality
  • Caching Strategy: Reduce redundant API calls through intelligent caching

Configuration and Extensibility

classDiagram
    class ThreatAnalysisConfig {
        +ai_provider: str
        +fallback_provider: str
        +max_analysis_time: float
        +confidence_threshold: float
        +cache_enabled: bool
        +cache_ttl: int
        +memory_optimization_level: str
        +cost_limit: float
        +parallel_processing: bool
        +max_workers: int
        +retry_attempts: int
        +circuit_breaker_threshold: int
        
        +validate_config() bool
        +get_provider_config(provider: str) Dict[str, Any]
        +apply_optimization_settings() None
    }
    
    class ProviderPlugin {
        <<interface>>
        +provider_name: str
        +supported_models: List[str]
        +cost_per_token: Dict[str, float]
        
        +initialize(config: Dict) None
        +generate_analysis(request: AnalysisRequest) AnalysisResponse
        +estimate_cost(request: AnalysisRequest) float
        +health_check() bool
    }
    
    class AnalysisPlugin {
        <<interface>>
        +plugin_name: str
        +supported_analysis_types: List[str]
        
        +analyze(data: Any, context: Dict) Any
        +validate_input(data: Any) bool
        +get_dependencies() List[str]
    }
    
    ThreatAnalysisConfig --> ProviderPlugin : configures
    ThreatAnalysisConfig --> AnalysisPlugin : configures
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Testing Architecture

Unit Test Structure

classDiagram
    class AIThreatAnalyzerTest {
        <<test>>
        +test_analyze_threats_success()
        +test_analyze_threats_with_fallback()
        +test_capability_analysis_integration()
        +test_caching_behavior()
        +test_memory_optimization()
        +test_error_handling()
        +test_concurrent_analysis()
    }
    
    class AIProviderTest {
        <<test>>
        +test_openai_provider()
        +test_anthropic_provider()
        +test_local_llm_provider()
        +test_provider_failover()
        +test_circuit_breaker()
        +test_retry_logic()
        +test_cost_estimation()
    }
    
    class ThreatModelingTest {
        <<test>>
        +test_stride_analysis()
        +test_attack_tree_building()
        +test_threat_prioritization()
        +test_mitigation_mapping()
        +test_scenario_validation()
    }
    
    class MockAIProvider {
        <<mock>>
        +mock_successful_response()
        +mock_rate_limit_error()
        +mock_network_error()
        +mock_invalid_response()
        +simulate_high_latency()
        +inject_specific_errors()
    }
    
    AIThreatAnalyzerTest --> MockAIProvider : uses
    AIProviderTest --> MockAIProvider : uses
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Integration Testing Strategy

class ThreatAnalysisIntegrationTest:
    """Integration tests for AI threat analysis system."""
    
    def test_end_to_end_analysis(self):
        """Test complete threat analysis workflow."""
        
        # Setup test MCP server
        test_server = MCPServerInfo(
            server_id="test-server",
            tools=[
                MCPTool(name="file_read", description="Read file contents"),
                MCPTool(name="execute_command", description="Execute system command"),
                MCPTool(name="network_request", description="Make HTTP requests")
            ],
            capabilities=["file_operations", "system_commands", "network_access"]
        )
        
        # Initialize analyzer with test configuration
        config = {
            "ai_provider": "mock",
            "enable_caching": True,
            "memory_optimization_level": "standard"
        }
        analyzer = AIThreatAnalyzer(config)
        
        # Execute analysis
        result = analyzer.analyze_threats(
            test_server,
            environment_context=EnvironmentContext(
                deployment_type=DeploymentType.CLOUD,
                security_posture=SecurityPosture.BASIC
            ),
            analysis_type="comprehensive"
        )
        
        # Validate results
        assert isinstance(result, ThreatAnalysis)
        assert result.threat_level in [ThreatLevel.LOW, ThreatLevel.MEDIUM, ThreatLevel.HIGH, ThreatLevel.CRITICAL]
        assert len(result.attack_vectors) > 0
        assert len(result.mitigation_strategies) > 0
        assert result.confidence_score >= 0.0 and result.confidence_score <= 1.0
        
        # Validate analysis components
        assert result.tool_capabilities.total_functions == 3
        assert CapabilityCategory.FILE_OPERATIONS in result.tool_capabilities.function_categories
        assert CapabilityCategory.SYSTEM_COMMANDS in result.tool_capabilities.function_categories
        
    def test_provider_failover_scenario(self):
        """Test AI provider failover behavior."""
        
        # Configure primary provider to fail
        config = {
            "ai_provider": "failing_provider",
            "fallback_provider": "mock_provider"
        }
        
        analyzer = AIThreatAnalyzer(config)
        
        # Simulate provider failure
        with patch.object(analyzer.ai_provider, 'generate_threat_analysis') as mock_primary:
            mock_primary.side_effect = Exception("Provider unavailable")
            
            # Analysis should succeed using fallback
            result = analyzer.analyze_threats(test_server)
            
            # Verify fallback was used
            assert result is not None
            assert analyzer.get_statistics()['fallback_usage_count'] > 0
    
    def test_concurrent_analysis_performance(self):
        """Test concurrent analysis performance and correctness."""
        
        servers = [create_test_server(f"server-{i}") for i in range(10)]
        analyzer = AIThreatAnalyzer({"parallel_processing": True, "max_workers": 3})
        
        start_time = time.time()
        results = []
        
        with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
            futures = [
                executor.submit(analyzer.analyze_threats, server)
                for server in servers
            ]
            
            for future in concurrent.futures.as_completed(futures):
                result = future.result()
                results.append(result)
        
        duration = time.time() - start_time
        
        # Validate results
        assert len(results) == 10
        assert all(isinstance(r, ThreatAnalysis) for r in results)
        
        # Check performance improvement from concurrency
        assert duration < 30.0  # Should complete within reasonable time
        
        # Validate resource usage
        stats = analyzer.get_statistics()
        assert stats['concurrent_analyses'] == 10
        assert stats['memory_peak_usage'] < 500 * 1024 * 1024  # < 500MB

Conclusion

The HawkEye AI threat analysis architecture demonstrates a sophisticated, enterprise-grade approach to AI-powered security analysis with the following key strengths:

Multi-Provider AI Architecture:

  • Abstract Provider Interface: Clean abstraction supporting OpenAI, Anthropic, and local LLM providers
  • Intelligent Provider Selection: Automatic failover and load balancing across providers
  • Circuit Breaker Pattern: Protection against cascading failures
  • Advanced Retry Logic: Exponential backoff with jitter and error classification

Comprehensive Analysis Framework:

  • Capability Analysis: Deep analysis of MCP tool capabilities and risk surfaces
  • Threat Modeling: STRIDE-based threat analysis with attack tree generation
  • Attack Chain Analysis: Multi-step attack scenario modeling with feasibility assessment
  • Risk Prioritization: Business-context-aware threat prioritization

Enterprise-Grade Features:

  • Memory Optimization: Proactive memory management with configurable optimization levels
  • Performance Monitoring: Response time tracking and performance degradation detection
  • Intelligent Caching: Multi-layer caching with TTL and intelligent invalidation
  • Cost Management: API cost estimation and budget controls

Extensibility and Integration:

  • Plugin Architecture: Support for custom AI providers and analysis components
  • Configuration Management: Comprehensive configuration with validation
  • Testing Framework: Comprehensive unit and integration testing support
  • Monitoring and Observability: Detailed statistics and health monitoring

Key Architectural Patterns:

  1. Abstract Factory Pattern: AI provider creation and management
  2. Strategy Pattern: Pluggable analysis algorithms and provider selection
  3. Circuit Breaker Pattern: Fault tolerance and provider protection
  4. Observer Pattern: Performance monitoring and health tracking
  5. Template Method Pattern: Analysis workflow with customization points

The architecture provides a robust foundation for AI-powered threat analysis while maintaining scalability, reliability, and extensibility for future enhancements in security analysis capabilities.

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