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osok edited this page Jul 31, 2025 · 1 revision

Threat Modeling Engine Documentation

Overview

The Threat Modeling Engine is a comprehensive component of the AI threat analysis system that implements advanced threat modeling methodologies including STRIDE analysis, attack tree generation, and threat actor profiling. This engine transforms discovered MCP tool capabilities into structured threat models that security teams can use to understand and defend against potential attacks.

Architecture

Core Components

classDiagram
    class ThreatModeler {
        +perform_stride_analysis()
        +generate_attack_trees()
        +profile_threat_actors()
        +conduct_comprehensive_analysis()
        -_initialize_stride_patterns()
        -_initialize_threat_actor_templates()
        -_initialize_attack_patterns()
    }
    
    class AttackVectorGenerator {
        +generate_attack_vectors()
        -_get_function_risk_indicators()
        -_generate_vectors_for_indicator()
        -_prioritize_attack_vectors()
    }
    
    class ScenarioBuilder {
        +build_attack_scenarios()
        +analyze_threat_actor_scenarios()
        -_select_scenario_template()
        -_build_detailed_scenario()
        -_calculate_scenario_risk()
    }
    
    class AttackChainAnalyzer {
        +identify_attack_chains()
        +assess_chain_feasibility()
        -_build_capability_graph()
        -_find_attack_paths()
        -_rank_attack_chains()
    }
    
    class LikelihoodAssessor {
        +assess_threat_likelihood()
        +create_threat_matrix()
        -_analyze_environmental_factors()
        -_calculate_threat_probability()
    }
    
    class MCPCapabilityAnalyzer {
        +map_capabilities_to_threats()
        +categorize_threat_potential()
        -_map_to_attack_techniques()
        -_generate_threat_scenarios()
    }
    
    ThreatModeler --> AttackVectorGenerator
    ThreatModeler --> ScenarioBuilder
    ThreatModeler --> AttackChainAnalyzer
    ThreatModeler --> LikelihoodAssessor
    ThreatModeler --> MCPCapabilityAnalyzer
    
    class STRIDECategory {
        <<enumeration>>
        +SPOOFING
        +TAMPERING
        +REPUDIATION
        +INFORMATION_DISCLOSURE
        +DENIAL_OF_SERVICE
        +ELEVATION_OF_PRIVILEGE
    }
    
    class AttackTreeNodeType {
        <<enumeration>>
        +GOAL
        +AND
        +OR
        +LEAF
    }
    
    class ThreatActorType {
        <<enumeration>>
        +INSIDER_THREAT
        +EXTERNAL_ATTACKER
        +SUPPLY_CHAIN
        +NATION_STATE
        +CYBERCRIMINAL
        +HACKTIVIST
    }
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Data Flow Architecture

flowchart TD
    A[Tool Capabilities] --> B[ThreatModeler]
    C[Environment Context] --> B
    
    B --> D[STRIDE Analysis]
    B --> E[Attack Tree Generation]
    B --> F[Threat Actor Profiling]
    
    D --> G[STRIDEThreat Objects]
    E --> H[AttackTree Objects]
    F --> I[ThreatActorProfile Objects]
    
    G --> J[ThreatModelingResult]
    H --> J
    I --> J
    
    K[AttackVectorGenerator] --> L[Attack Vectors]
    M[ScenarioBuilder] --> N[Abuse Scenarios]
    O[AttackChainAnalyzer] --> P[Attack Chains]
    Q[LikelihoodAssessor] --> R[Probability Matrix]
    
    L --> J
    N --> J
    P --> J
    R --> J
    
    J --> S[Comprehensive Threat Model]
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STRIDE Analysis Implementation

Algorithm Overview

The STRIDE analysis engine implements Microsoft's STRIDE methodology for systematic threat identification:

  1. Spoofing: Identity impersonation attacks
  2. Tampering: Data/system modification attacks
  3. Repudiation: Non-accountability attacks
  4. Information Disclosure: Privacy violation attacks
  5. Denial of Service: Availability attacks
  6. Elevation of Privilege: Authorization bypass attacks

Key Methods

perform_stride_analysis()

def perform_stride_analysis(
    self,
    tool_capabilities: ToolCapabilities,
    environment_context: EnvironmentContext
) -> List[STRIDEThreat]:
    """
    Perform comprehensive STRIDE analysis on MCP tool capabilities.
    
    Process:
    1. Iterate through each STRIDE category
    2. Analyze tool capabilities against category patterns
    3. Generate specific threats for each applicable category
    4. Calculate severity and likelihood scores
    5. Prioritize threats by risk level
    """

STRIDE Pattern Matching

The engine uses pattern-based threat identification:

stride_patterns = {
    STRIDECategory.SPOOFING: {
        "indicators": ["authentication", "identity", "credentials"],
        "threat_templates": [
            "Authentication bypass using {tool_name}",
            "Identity spoofing through {capability}",
            "Credential impersonation via {function}"
        ]
    },
    STRIDECategory.TAMPERING: {
        "indicators": ["write", "modify", "update", "delete"],
        "threat_templates": [
            "Data modification through {tool_name}",
            "System configuration tampering",
            "File system manipulation"
        ]
    }
    # ... additional categories
}

Attack Tree Generation

Tree Construction Algorithm

Attack trees provide hierarchical representations of attack paths:

graph TD
    A[Root Goal: Data Exfiltration] --> B[AND: Gain Access]
    A --> C[AND: Extract Data]
    
    B --> D[OR: Exploit Authentication]
    B --> E[OR: Bypass Authorization]
    
    D --> F[Credential Brute Force]
    D --> G[Session Hijacking]
    
    E --> H[Privilege Escalation]
    E --> I[Token Manipulation]
    
    C --> J[File System Access]
    C --> K[Database Queries]
    C --> L[API Exploitation]
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Key Components

generate_attack_trees()

def generate_attack_trees(
    self,
    tool_capabilities: ToolCapabilities,
    environment_context: EnvironmentContext,
    target_goals: Optional[List[str]] = None
) -> List[AttackTree]:
    """
    Generate attack trees for identified attack goals.
    
    Process:
    1. Identify potential attack goals based on tool capabilities
    2. Build tree structure using AND/OR logic gates
    3. Calculate probability and cost for each node
    4. Identify critical attack paths
    5. Generate comprehensive attack tree objects
    """

Attack Tree Metrics

  • Probability Calculation: Bottom-up propagation using Boolean logic
  • Cost Estimation: Resource requirements for each attack step
  • Critical Path Analysis: Most likely and cost-effective attack routes
  • Time to Compromise: Estimated attack timeline

Threat Actor Profiling

Actor Categories

The system models six primary threat actor types:

  1. Insider Threat: Employees, contractors with legitimate access
  2. External Attacker: Outside attackers seeking unauthorized access
  3. Supply Chain: Compromised dependencies or third-party tools
  4. Nation State: Advanced persistent threat groups
  5. Cybercriminal: Profit-motivated attackers
  6. Hacktivist: Ideologically motivated groups

Profile Attributes

@dataclass
class ThreatActorProfile:
    """Comprehensive threat actor profile."""
    name: str
    type: ThreatActorType
    skill_level: str  # novice, intermediate, advanced, expert
    motivation: str
    resources: str  # limited, moderate, substantial, unlimited
    preferred_targets: List[str]
    typical_attack_vectors: List[str]
    sophistication_level: int  # 1-10 scale
    persistence_level: str  # opportunistic, determined, persistent
    stealth_capability: str  # low, medium, high, advanced

Actor-Specific Scenarios

Each threat actor type generates tailored attack scenarios:

scenario_templates = {
    ThreatActorType.INSIDER_THREAT: {
        "data_theft": {
            "description": "Insider abuses legitimate access to exfiltrate sensitive data",
            "techniques": ["T1005", "T1041", "T1048"],
            "detection_difficulty": "high"
        },
        "sabotage": {
            "description": "Disgruntled employee damages systems or data",
            "techniques": ["T1485", "T1490", "T1529"],
            "detection_difficulty": "medium"
        }
    }
    # ... additional actor types
}

Attack Vector Generation

Dynamic Vector Generation

The AttackVectorGenerator creates attack vectors dynamically based on tool capabilities:

Risk Indicator Analysis

def _get_function_risk_indicators(self, function_name: str) -> List[str]:
    """
    Identify risk indicators from function names using pattern matching.
    
    Risk Categories:
    - File System Access: read_file, write_file, delete_file
    - Network Operations: http_request, download, upload
    - System Commands: execute, run, shell
    - Database Operations: query, insert, update
    - Authentication: login, authenticate, authorize
    """

Attack Template Mapping

attack_templates = {
    "file_access": {
        "vectors": [
            {
                "name": "Unauthorized File Access",
                "severity": "high",
                "description": "Exploit file access functions to read sensitive data",
                "attack_steps": [
                    "Identify file access functions",
                    "Craft malicious file paths",
                    "Execute unauthorized file operations",
                    "Exfiltrate sensitive information"
                ]
            }
        ]
    }
}

MITRE ATT&CK Framework Integration

Technique Mapping

The system maps MCP tool capabilities to MITRE ATT&CK techniques:

technique_mappings = {
    'command_execution': {
        'technique_id': 'T1059',
        'technique_name': 'Command and Scripting Interpreter',
        'description': 'Execute arbitrary commands through tool functions',
        'severity': 'critical'
    },
    'file_system_access': {
        'technique_id': 'T1083',
        'technique_name': 'File and Directory Discovery',
        'description': 'Access and manipulate file system through tool capabilities',
        'severity': 'high'
    },
    'network_access': {
        'technique_id': 'T1071',
        'technique_name': 'Application Layer Protocol',
        'description': 'Abuse network capabilities for command and control',
        'severity': 'high'
    }
}

Tactic Classification

Techniques are organized by MITRE ATT&CK tactics:

  • Initial Access: T1566 (Phishing), T1190 (Exploit Public-Facing Application)
  • Execution: T1059 (Command and Scripting Interpreter)
  • Persistence: T1505 (Server Software Component)
  • Privilege Escalation: T1078 (Valid Accounts)
  • Defense Evasion: T1027 (Obfuscated Files or Information)
  • Credential Access: T1110 (Brute Force)
  • Discovery: T1083 (File and Directory Discovery)
  • Collection: T1005 (Data from Local System)
  • Exfiltration: T1041 (Exfiltration Over C2 Channel)

Attack Chain Analysis

Multi-Tool Attack Scenarios

The AttackChainAnalyzer identifies sophisticated attack scenarios using multiple MCP tools:

Chain Construction Process

sequenceDiagram
    participant AAC as AttackChainAnalyzer
    participant TCG as ToolCapabilityGraph
    participant APF as AttackPathFinder
    participant CFS as ChainFeasibilityScorer
    
    AAC->>TCG: Build capability relationships
    TCG-->>AAC: Tool capability graph
    
    AAC->>APF: Find potential attack paths
    APF-->>AAC: Attack path candidates
    
    AAC->>CFS: Assess chain feasibility
    CFS-->>AAC: Feasibility scores
    
    AAC->>AAC: Rank and filter chains
    AAC-->>AAC: High-priority attack chains
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Chain Types

  1. Sequential Chains: Tool A → Tool B → Tool C
  2. Parallel Chains: Multiple tools used simultaneously
  3. Branching Chains: One tool enables multiple attack paths
  4. Convergent Chains: Multiple tools contribute to single goal

Feasibility Assessment

@dataclass
class ChainFeasibilityScore:
    """Feasibility assessment for attack chains."""
    technical_feasibility: float  # 0.0-1.0
    resource_requirements: float  # 0.0-1.0 (inverted - lower is better)
    detection_likelihood: float   # 0.0-1.0 (inverted - lower is better)
    overall_score: float         # Weighted combination
    confidence_level: str        # low, medium, high
    limiting_factors: List[str]  # Factors that reduce feasibility

Scenario Building

Realistic Attack Scenarios

The ScenarioBuilder creates detailed, realistic attack scenarios:

Scenario Components

  1. Threat Actor Profile: Who is conducting the attack
  2. Attack Timeline: Sequence of events with timestamps
  3. Attack Phases: Reconnaissance, initial access, lateral movement, etc.
  4. Technical Details: Specific commands, tools, and techniques used
  5. Business Impact: Potential damage and consequences
  6. Detection Opportunities: Points where the attack might be detected
  7. Mitigation Strategies: How to prevent or respond to the attack

Example Scenario Structure

scenario = {
    'id': 'external_attacker_data_exfiltration',
    'title': 'External Attacker: Corporate Data Theft',
    'threat_actor': {
        'name': 'Advanced Cybercriminal Group',
        'type': 'external_attacker',
        'skill_level': 'advanced',
        'motivation': 'financial_gain'
    },
    'attack_phases': [
        {
            'phase': 'reconnaissance',
            'duration': '2-3 days',
            'activities': ['Network scanning', 'Service enumeration', 'Vulnerability assessment']
        },
        {
            'phase': 'initial_access',
            'duration': '4-6 hours',
            'activities': ['Exploit MCP server vulnerability', 'Establish foothold']
        }
        # ... additional phases
    ]
}

Likelihood Assessment

Probability Modeling

The LikelihoodAssessor implements sophisticated probability models:

Assessment Factors

  1. Attack Complexity: Technical difficulty of exploitation
  2. Environmental Factors: Network topology, security controls
  3. Threat Actor Capabilities: Skill level and resources
  4. Target Attractiveness: Value and accessibility of assets
  5. Historical Patterns: Similar attacks in the past

Threat Likelihood Matrix

@dataclass
class ThreatLikelihoodMatrix:
    """Comprehensive threat likelihood assessment."""
    threat_id: str
    base_probability: float           # Inherent threat likelihood
    environmental_modifiers: Dict[str, float]  # Environment-specific adjustments
    actor_capability_score: float     # Threat actor capability assessment
    target_attractiveness: float      # Asset value and accessibility
    historical_frequency: float       # Historical attack frequency
    final_likelihood: float          # Final calculated likelihood
    confidence_interval: Tuple[float, float]  # Confidence range
    assessment_summary: str          # Human-readable summary

Performance Optimization

Caching Strategy

The threat modeling engine implements intelligent caching:

class ThreatModelCache:
    """Cache for threat modeling results."""
    
    def __init__(self):
        self.stride_cache = {}      # STRIDE analysis results
        self.tree_cache = {}        # Attack tree structures
        self.scenario_cache = {}    # Generated scenarios
        self.chain_cache = {}       # Attack chain analysis

Parallel Processing

  • Concurrent STRIDE Analysis: Each category analyzed in parallel
  • Parallel Tree Generation: Multiple attack trees built simultaneously
  • Distributed Scenario Building: Scenario generation across multiple threads
  • Asynchronous Chain Analysis: Non-blocking attack chain discovery

Quality Assurance

Validation Mechanisms

  1. Threat Model Validation: Ensure completeness and consistency
  2. MITRE ATT&CK Mapping Verification: Validate technique mappings
  3. Scenario Realism Checks: Verify attack scenario feasibility
  4. Cross-Reference Validation: Check consistency across components

Testing Strategy

  • Unit Tests: Individual component functionality
  • Integration Tests: Cross-component interaction
  • Scenario Validation: Real-world attack scenario testing
  • Performance Benchmarks: Scalability and efficiency testing

Configuration and Customization

Threat Model Profiles

threat_model_profiles = {
    "comprehensive": {
        "stride_analysis": True,
        "attack_trees": True,
        "threat_actors": "all",
        "max_chain_length": 5,
        "scenario_count": 10
    },
    "focused": {
        "stride_analysis": True,
        "attack_trees": False,
        "threat_actors": ["external_attacker", "insider_threat"],
        "max_chain_length": 3,
        "scenario_count": 5
    }
}

Customizable Parameters

  • Analysis Depth: Surface-level vs. deep analysis
  • Threat Actor Focus: Specific actor types to emphasize
  • Risk Threshold: Minimum risk level for inclusion
  • Time Constraints: Maximum analysis duration
  • Output Format: Detailed vs. summary reporting

Integration Points

Input Dependencies

  • Tool Capabilities: From MCPCapabilityAnalyzer
  • Environment Context: From environment analysis components
  • Detection Results: From MCP introspection and detection pipeline

Output Consumers

  • Report Generator: Consumes threat models for report creation
  • Risk Assessor: Uses threat analysis for risk scoring
  • Mitigation Planner: Leverages threat models for countermeasure planning
  • Security Dashboard: Displays threat modeling results

Future Enhancements

Planned Improvements

  1. Machine Learning Integration: ML-based threat prediction
  2. Dynamic Threat Intelligence: Real-time threat feed integration
  3. Automated Scenario Validation: AI-powered scenario verification
  4. Advanced Visualization: Interactive threat model displays
  5. Collaborative Threat Modeling: Multi-user threat model development

Research Areas

  • Quantum Threat Modeling: Post-quantum cryptography threats
  • AI/ML Attack Modeling: AI-specific attack vectors
  • Cloud-Native Threats: Container and serverless attack models
  • Supply Chain Risk Modeling: Third-party dependency threats

Conclusion

The Threat Modeling Engine provides comprehensive, automated threat analysis capabilities that transform raw MCP tool discoveries into actionable security intelligence. Through STRIDE analysis, attack tree generation, threat actor profiling, and MITRE ATT&CK integration, it delivers the detailed threat models security teams need to understand, prioritize, and defend against potential attacks on their MCP infrastructure.

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