attack chain analysis - osok/hawkeye GitHub Wiki

Attack Chain Analysis - HawkEye AI Threat Analysis

Overview

The HawkEye Attack Chain Analysis system provides sophisticated multi-step attack scenario modeling, identifying how individual MCP tools can be combined to create complex attack chains. The system models attack path discovery, lateral movement, privilege escalation, and multi-objective attack sequences to provide comprehensive threat intelligence.

Architecture Components

1. Attack Chain Analysis Architecture

classDiagram
    class AttackChainAnalyzer {
        +capability_relationships: Dict
        +attack_patterns: Dict
        +chain_cache: Dict
        +stats: Dict
        +identify_attack_chains(tools, context) List[AttackChain]
        +identify_advanced_attack_chains(tools, context) List[AttackChain]
        +discover_attack_chains(tools, context) Dict[str, Any]
        +find_lateral_movement_chains(tools, context) List[AttackChain]
        +find_data_exfiltration_chains(tools, context) List[AttackChain]
        +assess_chain_feasibility(chain, environment) ChainFeasibilityScore
    }
    
    class AttackVectorGenerator {
        +attack_templates: Dict
        +generate_attack_vectors(capabilities, context) List[Dict]
        +_get_function_risk_indicators(name) List[str]
        +_generate_vectors_for_indicator(func, indicator, context) List[Dict]
        +_prioritize_attack_vectors(vectors, context) List[Dict]
    }
    
    class ScenarioBuilder {
        +threat_actors: Dict[ThreatActorType, ThreatActorProfile]
        +scenario_templates: Dict
        +generate_scenarios(capabilities, context) List[Dict]
        +build_detailed_scenarios(capabilities, actor_type) List[Dict]
        +_generate_threat_actor_scenarios(profile, capabilities) List[Dict]
    }
    
    class ThreatModeler {
        +threat_actor_templates: Dict
        +attack_patterns: Dict
        +generate_threat_model(servers, context) Dict[str, Any]
        +analyze_stride_threats(capabilities) Dict[str, List]
        +generate_attack_trees(threats) Dict[str, Any]
        +model_threat_scenarios(actors, capabilities) List[Dict]
    }
    
    class LikelihoodAssessor {
        +threat_actor_profiles: Dict
        +_attack_patterns: Dict
        +assess_attack_likelihood(scenario, context) float
        +_identify_scenario_threat_actors(scenario) List[ThreatActorType]
        +_generate_scenario_attack_vectors(scenario, capabilities) List[AttackVector]
    }
    
    AttackChainAnalyzer --> AttackVectorGenerator : uses
    AttackChainAnalyzer --> ScenarioBuilder : uses
    AttackChainAnalyzer --> ThreatModeler : uses
    AttackChainAnalyzer --> LikelihoodAssessor : uses
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2. Attack Chain Data Models

classDiagram
    class AttackChain {
        +chain_id: str
        +chain_name: str
        +description: str
        +threat_actor: ThreatActorType
        +attack_objective: str
        +chain_links: List[ChainLink]
        +overall_difficulty: DifficultyLevel
        +overall_impact: BusinessImpact
        +total_time_estimate: int
        +detection_difficulty: DifficultyLevel
        +mitigation_strategies: List[str]
        +prerequisites: List[str]
        +success_probability: float
    }
    
    class ChainLink {
        +step_number: int
        +tool_name: str
        +capability_used: str
        +action_description: str
        +input_requirements: List[str]
        +output_produced: List[str]
        +risk_level: ThreatLevel
        +difficulty: DifficultyLevel
        +time_estimate: int
    }
    
    class ChainFeasibilityScore {
        +technical_feasibility: float
        +resource_requirements: float
        +detection_evasion: float
        +environmental_suitability: float
        +overall_score: float
        +confidence_level: float
    }
    
    class AttackVector {
        +vector_id: str
        +name: str
        +description: str
        +required_capabilities: List[CapabilityCategory]
        +complexity: AttackComplexity
        +required_access: AccessLevel
        +detection_difficulty: DetectionDifficulty
        +time_to_execute: float
        +success_probability: float
        +stealth_level: float
        +prerequisites: List[str]
        +indicators: List[str]
    }
    
    class ThreatActorProfile {
        +name: str
        +type: ThreatActorType
        +skill_level: str
        +resources: str
        +motivation: str
        +typical_objectives: List[str]
        +common_techniques: List[str]
        +time_horizon: str
        +stealth_requirement: str
    }
    
    AttackChain --> ChainLink : contains
    AttackChain --> ChainFeasibilityScore : assessed_by
    AttackChain --> AttackVector : utilizes
    AttackChain --> ThreatActorProfile : executed_by
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3. Attack Path Discovery Algorithm

flowchart TD
    A[Detected MCP Tools] --> B[Extract Tool Capabilities]
    B --> C[Build Capability Graph]
    C --> D[Identify Pivot Points]
    D --> E[Generate Chain Candidates]
    E --> F[Calculate Feasibility Scores]
    F --> G{Score >= Threshold}
    G -->|Yes| H[Generate Attack Narrative]
    G -->|No| I[Discard Chain]
    H --> J[Assess Overall Risk]
    J --> K[Rank Attack Chains]
    K --> L[Final Chain Results]
    
    subgraph "Path Discovery"
        C
        D
        E
    end
    
    subgraph "Feasibility Assessment"
        F
        G
    end
    
    subgraph "Chain Construction"
        H
        J
        K
    end
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Core Features

1. Multi-Step Attack Chain Discovery

Basic Chain Identification

def identify_attack_chains(self, 
                         detected_tools: List[MCPServerInfo],
                         environment_context: EnvironmentContext,
                         max_chain_length: int = 5,
                         min_feasibility_score: float = 0.3) -> List[AttackChain]:
    """
    Identify potential attack chains through multi-tool combinations.
    
    Steps:
    1. Extract tool capabilities from detected servers
    2. Build capability dependency graph
    3. Find attack paths using depth-first search
    4. Convert paths to structured attack chains
    5. Assess feasibility and filter viable chains
    6. Rank chains by risk and complexity
    """

Advanced Chain Analysis

def identify_advanced_attack_chains(self,
                                  detected_tools: List[MCPServerInfo],
                                  environment_context: EnvironmentContext,
                                  threat_actor_types: List[ThreatActorType] = None,
                                  include_time_based_chains: bool = True,
                                  include_conditional_chains: bool = True) -> List[AttackChain]:
    """
    Advanced attack chain analysis including:
    - Multi-objective attack paths
    - Time-based attack sequences  
    - Conditional attack paths
    - Threat actor-specific chains
    """

2. Attack Path Discovery

Capability Graph Construction

The system builds a directed graph representing how tool capabilities can be chained together:

# Capability relationships example
capability_relationships = {
    "file_read": ["data_analysis", "credential_extraction", "configuration_discovery"],
    "network_access": ["service_enumeration", "lateral_movement", "data_exfiltration"],
    "code_execution": ["privilege_escalation", "persistence", "defense_evasion"],
    "authentication": ["credential_theft", "session_hijacking", "impersonation"]
}

Path Finding Algorithm

def _dfs_find_paths(self, 
                   graph: Dict[str, List[str]], 
                   current: str, 
                   path: List[str], 
                   all_paths: List[List[str]], 
                   max_length: int) -> None:
    """
    Depth-first search to find all possible attack paths.
    
    - Explores all possible tool combinations
    - Avoids cycles to prevent infinite loops
    - Respects maximum chain length constraints
    - Identifies multi-tool attack sequences
    """

3. Lateral Movement Analysis

Lateral Movement Chain Detection

def find_lateral_movement_chains(self,
                               detected_tools: List[MCPServerInfo],
                               environment_context: EnvironmentContext) -> List[AttackChain]:
    """
    Specialized analysis for lateral movement scenarios.
    
    Focus Areas:
    - Network access capabilities
    - System information gathering
    - File system access
    - Code execution opportunities
    """

Lateral Movement Patterns

lateral_movement_categories = [
    CapabilityCategory.NETWORK_ACCESS,      # Network discovery and access
    CapabilityCategory.SYSTEM_INFORMATION,  # Host enumeration
    CapabilityCategory.FILE_SYSTEM,         # Credential hunting
    CapabilityCategory.CODE_EXECUTION       # Remote execution
]

4. Privilege Escalation Modeling

Privilege Escalation Attack Vectors

privilege_escalation_vector = AttackVector(
    vector_id="priv_escalation",
    name="Privilege Escalation",
    description="Gaining higher-level system permissions",
    required_capabilities=[CapabilityCategory.CODE_EXECUTION],
    complexity=AttackComplexity.HIGH,
    required_access=AccessLevel.USER,
    detection_difficulty=DetectionDifficulty.HARD,
    time_to_execute=8.0,
    success_probability=0.6,
    stealth_level=0.7,
    prerequisites=["initial_access", "vulnerability_research"],
    indicators=["unusual_process_elevation", "permission_changes"]
)

Escalation Chain Construction

The system identifies tools that can be combined for privilege escalation:

  1. Initial Access: Tools providing basic system access
  2. Information Gathering: System enumeration capabilities
  3. Vulnerability Exploitation: Code execution or configuration abuse
  4. Privilege Validation: Verification of elevated access

5. Multi-Objective Attack Scenarios

Threat Actor Profiling

threat_actor_profiles = {
    ThreatActorType.INSIDER_THREAT: ThreatActorProfile(
        name="Malicious Insider",
        skill_level="intermediate",
        resources="moderate",
        motivation="Financial gain, revenge, espionage",
        typical_objectives=["Data theft", "Sabotage", "Fraud"],
        common_techniques=["Privilege abuse", "Data exfiltration"],
        time_horizon="days",
        stealth_requirement="high"
    ),
    
    ThreatActorType.NATION_STATE: ThreatActorProfile(
        name="Advanced Persistent Threat",
        skill_level="expert",
        resources="unlimited",
        motivation="Espionage, disruption, intelligence gathering",
        typical_objectives=["Long-term access", "Data collection"],
        common_techniques=["Advanced techniques", "Zero-day exploits"],
        time_horizon="months",
        stealth_requirement="critical"
    )
}

Scenario Generation

def generate_scenarios(self, 
                      tool_capabilities: ToolCapabilities,
                      environment_context: EnvironmentContext,
                      threat_actor_type: ThreatActorType = None,
                      max_scenarios: int = 5) -> List[Dict[str, Any]]:
    """
    Generate realistic attack scenarios based on:
    - Available tool capabilities
    - Environment characteristics
    - Threat actor motivations and skills
    - Business context and assets
    """

Attack Pattern Categories

1. Reconnaissance and Discovery

Pattern: Information Gathering → Target Analysis → Attack Vector Selection

Example Chain:

  1. System Information Tool: Gather host details, running processes
  2. Network Access Tool: Discover network topology and services
  3. File System Tool: Search for configuration files and credentials
  4. Analysis Tool: Process gathered information for attack planning

2. Initial Access and Persistence

Pattern: Initial Compromise → Persistence Establishment → Defense Evasion

Example Chain:

  1. Authentication Tool: Exploit weak credentials or authentication bypass
  2. Code Execution Tool: Execute initial payload or commands
  3. File System Tool: Install backdoors or persistence mechanisms
  4. System Configuration Tool: Modify system settings for stealth

3. Privilege Escalation and Lateral Movement

Pattern: Privilege Escalation → Credential Harvesting → Lateral Movement

Example Chain:

  1. Code Execution Tool: Exploit local privilege escalation vulnerability
  2. System Information Tool: Enumerate elevated privileges and access
  3. File System Tool: Access credential stores and configuration files
  4. Network Access Tool: Use credentials for lateral movement

4. Data Exfiltration and Impact

Pattern: Data Discovery → Collection → Exfiltration → Impact

Example Chain:

  1. File System Tool: Search for and identify sensitive data
  2. Data Processing Tool: Collect and package target data
  3. Network Access Tool: Establish exfiltration channel
  4. Encryption Tool: Encrypt or destroy data for impact

Feasibility Assessment

1. Technical Feasibility Factors

def _assess_technical_feasibility(self, chain: AttackChain, 
                                environment: EnvironmentContext) -> float:
    """
    Assess technical feasibility based on:
    - Tool compatibility and data flow
    - Required technical skills
    - System dependencies
    - Environmental constraints
    """

Factors Considered:

  • Tool Compatibility: Can tools actually chain together?
  • Data Flow Validation: Does output of Tool A work as input for Tool B?
  • Skill Requirements: Does threat actor have required capabilities?
  • System Dependencies: Are required system components available?

2. Environmental Suitability

def _assess_environmental_suitability(self, chain: AttackChain,
                                    environment: EnvironmentContext) -> float:
    """
    Assess how well the chain fits the target environment:
    - Deployment type compatibility
    - Security posture alignment  
    - Network exposure considerations
    - Monitoring and detection capabilities
    """

Assessment Criteria:

  • Deployment Type: Cloud vs on-premise compatibility
  • Security Posture: Alignment with security controls
  • Network Exposure: Internet-facing vs internal systems
  • Monitoring Coverage: Detection system capabilities

3. Resource Requirements

def _assess_resource_requirements(self, chain: AttackChain,
                                environment: EnvironmentContext) -> float:
    """
    Evaluate resource requirements:
    - Time investment needed
    - Technical skill levels required
    - Tool availability and setup
    - Infrastructure requirements
    """

Advanced Chain Analysis

1. Time-Based Attack Sequences

def _find_time_based_attack_sequences(self,
                                    capability_graph: Dict,
                                    tool_capabilities: Dict,
                                    environment_context: EnvironmentContext) -> List[Dict]:
    """
    Identify attack sequences that exploit timing dependencies:
    - Maintenance windows
    - Backup schedules  
    - User activity patterns
    - System update cycles
    """

2. Conditional Attack Paths

def _find_conditional_attack_paths(self,
                                 capability_graph: Dict,
                                 tool_capabilities: Dict,
                                 environment_context: EnvironmentContext) -> List[Dict]:
    """
    Find attack paths with conditional branching:
    - Success/failure scenarios
    - Alternative attack methods
    - Adaptive attack strategies
    - Fallback mechanisms
    """

3. Multi-Objective Attacks

def _find_multi_objective_paths(self,
                              capability_graph: Dict,
                              tool_capabilities: Dict,
                              environment_context: EnvironmentContext) -> List[Dict]:
    """
    Identify attack chains targeting multiple objectives:
    - Data theft + system disruption
    - Reconnaissance + persistence
    - Privilege escalation + lateral movement
    - Collection + exfiltration
    """

Chain Narrative Generation

1. Attack Progression Narratives

def _generate_attack_progression_narrative(self,
                                         chain: Dict,
                                         environment_context: EnvironmentContext) -> str:
    """
    Generate detailed narrative describing:
    - Step-by-step attack progression
    - Tool usage and outputs
    - Decision points and alternatives
    - Timeline and duration estimates
    - Detection opportunities
    """

2. Business Impact Stories

The system generates business-focused narratives explaining:

  • Initial Compromise: How attackers gain access
  • Progression Timeline: Step-by-step attack evolution
  • Business Consequences: Impact on operations and data
  • Detection Points: Where attacks might be caught
  • Mitigation Opportunities: How to break the attack chain

Performance Characteristics

1. Chain Discovery Performance

Small Environments (< 10 tools):

  • Analysis time: 30-60 seconds
  • Memory usage: 10-20MB
  • Chains discovered: 5-15 viable chains

Medium Environments (10-50 tools):

  • Analysis time: 2-5 minutes
  • Memory usage: 50-100MB
  • Chains discovered: 15-50 viable chains

Large Environments (> 50 tools):

  • Analysis time: 5-15 minutes
  • Memory usage: 100-500MB
  • Chains discovered: 50-200 viable chains

2. Optimization Strategies

Graph Pruning:

  • Remove low-probability paths early
  • Filter based on environmental constraints
  • Limit chain depth for performance

Caching:

  • Cache feasibility assessments
  • Store capability relationship mappings
  • Reuse threat actor profiles

Parallel Processing:

  • Concurrent path exploration
  • Parallel feasibility assessment
  • Distributed chain generation

Integration with Threat Analysis

1. Pipeline Integration

# Attack chain analysis in threat analysis pipeline
chain_analyzer = AttackChainAnalyzer()
attack_chains = chain_analyzer.discover_attack_chains(
    detected_tools=mcp_servers,
    environment_context=environment,
    max_chain_depth=5,
    min_chain_score=0.4
)

# Generate narratives for each chain
for chain in attack_chains:
    narrative = chain_analyzer._generate_attack_progression_narrative(
        chain, environment
    )
    chain['detailed_narrative'] = narrative

2. Report Generation

Attack chain analysis integrates with report generation to provide:

  • Executive Summary: High-level chain risks and priorities
  • Technical Details: Step-by-step attack progression
  • Mitigation Guidance: Specific countermeasures for each chain
  • Risk Assessment: Quantitative risk scoring for chains

Security Implications

1. Chain Complexity Assessment

The system evaluates attack chain complexity across multiple dimensions:

  • Technical Complexity: Skill level required for execution
  • Operational Security: Stealth and detection evasion requirements
  • Resource Requirements: Time, tools, and infrastructure needed
  • Success Probability: Likelihood of successful chain execution

2. Defense Strategy Generation

For each identified attack chain, the system generates:

  • Prevention Controls: Security measures to prevent chain initiation
  • Detection Strategies: Monitoring approaches to identify chain progression
  • Response Procedures: Incident response actions to break attack chains
  • Recovery Plans: Business continuity measures post-compromise

Future Enhancements

1. Machine Learning Integration

Planned Capabilities:

  • Pattern Recognition: ML-based attack pattern identification
  • Probability Modeling: Statistical models for chain success rates
  • Adaptive Analysis: Learning from historical attack data
  • Behavioral Analysis: User and system behavior modeling

2. Enhanced Modeling

Advanced Features:

  • Quantum-Resistant Analysis: Post-quantum cryptography considerations
  • IoT Chain Analysis: Internet of Things device attack chains
  • Cloud-Native Patterns: Container and serverless attack chains
  • Supply Chain Integration: Third-party dependency attack chains

This comprehensive attack chain analysis system provides security teams with detailed understanding of how individual MCP tools can be combined into sophisticated multi-step attacks, enabling proactive defense strategy development and risk mitigation planning.

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