"""
Enterprise-Grade Threat Detectors for Agent Sentinel

This module implements comprehensive threat detection capabilities for all types
of AI agents including A2A (Agent-to-Agent), MCP (Model Context Protocol),
autonomous agents, and other specialized agent architectures.

Enterprise features:
- Comprehensive threat coverage for all agent types
- Advanced pattern recognition and ML-based detection
- Real-time threat intelligence integration
- Cross-agent attack detection
- Protocol-specific vulnerability detection
- Behavioral anomaly detection
- Zero-day threat detection capabilities
- Compliance and regulatory threat detection
"""

import asyncio
import json
import re
import time
from collections import defaultdict, deque
from datetime import datetime, timezone, timedelta
from typing import Dict, List, Optional, Any, Set, Tuple, Union, Pattern
from dataclasses import dataclass, field
from enum import Enum
import threading
import hashlib
import base64
import urllib.parse
from pathlib import Path

from ..core.constants import ThreatType, SeverityLevel
from ..core.types import SecurityEvent
from ..core.exceptions import DetectionError
from ..logging.structured_logger import SecurityLogger
from .engine import BaseDetector, DetectionResult, DetectionContext


class AgentType(Enum):
    """Types of AI agents."""
    AUTONOMOUS_AGENT = "autonomous_agent"
    A2A_AGENT = "a2a_agent"
    MCP_AGENT = "mcp_agent"
    CHATBOT = "chatbot"
    WORKFLOW_AGENT = "workflow_agent"
    TOOL_AGENT = "tool_agent"
    ORCHESTRATOR = "orchestrator"
    API_AGENT = "api_agent"
    UNKNOWN = "unknown"


class AttackVector(Enum):
    """Attack vectors for different agent types."""
    PROMPT_INJECTION = "prompt_injection"
    COMMAND_INJECTION = "command_injection"
    PROTOCOL_MANIPULATION = "protocol_manipulation"
    CONTEXT_POISONING = "context_poisoning"
    AGENT_HIJACKING = "agent_hijacking"
    CROSS_AGENT_ATTACK = "cross_agent_attack"
    PRIVILEGE_ESCALATION = "privilege_escalation"
    DATA_EXFILTRATION = "data_exfiltration"
    TOOL_ABUSE = "tool_abuse"
    WORKFLOW_MANIPULATION = "workflow_manipulation"
    COMMUNICATION_TAMPERING = "communication_tampering"
    RESOURCE_EXHAUSTION = "resource_exhaustion"


@dataclass
class EnterpriseDetectionContext:
    """Enhanced detection context for enterprise-grade detection."""
    agent_id: str
    agent_type: AgentType
    method_name: str
    inputs: Dict[str, Any]
    outputs: Optional[Any] = None
    
    # Agent-specific context
    agent_metadata: Dict[str, Any] = field(default_factory=dict)
    session_context: Dict[str, Any] = field(default_factory=dict)
    communication_context: Dict[str, Any] = field(default_factory=dict)
    
    # Protocol-specific context
    protocol_version: Optional[str] = None
    protocol_headers: Dict[str, str] = field(default_factory=dict)
    protocol_payload: Optional[Dict[str, Any]] = None
    
    # Security context
    user_id: Optional[str] = None
    permissions: Set[str] = field(default_factory=set)
    authentication_context: Dict[str, Any] = field(default_factory=dict)
    
    # Network context
    source_ip: Optional[str] = None
    user_agent: Optional[str] = None
    request_headers: Dict[str, str] = field(default_factory=dict)
    
    # Temporal context
    timestamp: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
    sequence_number: Optional[int] = None
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert context to dictionary."""
        return {
            "agent_id": self.agent_id,
            "agent_type": self.agent_type.value,
            "method_name": self.method_name,
            "inputs": self.inputs,
            "outputs": self.outputs,
            "agent_metadata": self.agent_metadata,
            "session_context": self.session_context,
            "communication_context": self.communication_context,
            "protocol_version": self.protocol_version,
            "protocol_headers": self.protocol_headers,
            "protocol_payload": self.protocol_payload,
            "user_id": self.user_id,
            "permissions": list(self.permissions),
            "authentication_context": self.authentication_context,
            "source_ip": self.source_ip,
            "user_agent": self.user_agent,
            "request_headers": self.request_headers,
            "timestamp": self.timestamp.isoformat(),
            "sequence_number": self.sequence_number
        }


class EnterpriseBaseDetector(BaseDetector):
    """Enhanced base detector for enterprise-grade detection."""
    
    def __init__(self, config, detector_name: str):
        super().__init__(detector_name)  # BaseDetector takes (name, enabled=True)
        
        self.config = config
        
        # Enterprise features
        self.threat_intelligence_enabled = True
        self.ml_detection_enabled = True
        self.behavioral_analysis_enabled = True
        
        # Detection patterns cache
        self.pattern_cache: Dict[str, Any] = {}
        self.threat_signatures: Dict[str, Any] = {}
        
        # Performance optimization
        self.compiled_patterns: Dict[str, Pattern] = {}
        self.detection_cache: Dict[str, Any] = {}
        
        # Load enterprise patterns
        self._load_enterprise_patterns()
    
    def _load_enterprise_patterns(self) -> None:
        """Load enterprise-specific detection patterns."""
        # This would load from configuration files or threat intelligence feeds
        pass
    
    async def detect(self, context: DetectionContext) -> List[DetectionResult]:
        """Implement abstract detect method from BaseDetector."""
        # Convert DetectionContext to EnterpriseDetectionContext
        enterprise_context = EnterpriseDetectionContext(
            agent_id=context.agent_id,
            agent_type=AgentType.UNKNOWN,  # Default type
            method_name=context.method_name,
            inputs=context.inputs,
            outputs=context.outputs,
            timestamp=context.timestamp or datetime.now(timezone.utc),
            session_context=context.metadata or {},
            agent_metadata=context.metadata or {}
        )
        
        return self.detect_with_context(enterprise_context)
    
    def detect_with_context(self, context: EnterpriseDetectionContext) -> List[DetectionResult]:
        """Detect threats with enhanced context."""
        results = []
        
        # Convert to legacy format for base detection
        legacy_context = {
            "agent_id": context.agent_id,
            "method_name": context.method_name,
            "inputs": context.inputs,
            "outputs": context.outputs,
            "metadata": context.agent_metadata
        }
        
        # Run base detection
        base_result = self._detect_impl(self._context_to_text(context), legacy_context)
        if base_result:
            results.append(base_result)
        
        # Run enterprise-specific detection
        enterprise_results = self._detect_enterprise_threats(context)
        results.extend(enterprise_results)
        
        return results
    
    def _detect_enterprise_threats(self, context: EnterpriseDetectionContext) -> List[DetectionResult]:
        """Detect enterprise-specific threats."""
        # Override in subclasses
        return []
    
    def _context_to_text(self, context: EnterpriseDetectionContext) -> str:
        """Convert enterprise context to text for pattern matching."""
        text_parts = []
        
        # Add method name
        text_parts.append(context.method_name)
        
        # Add inputs
        for key, value in context.inputs.items():
            text_parts.append(f"{key}: {str(value)}")
        
        # Add outputs
        if context.outputs:
            text_parts.append(f"output: {str(context.outputs)}")
        
        # Add communication context
        for key, value in context.communication_context.items():
            text_parts.append(f"{key}: {str(value)}")
        
        # Add protocol payload
        if context.protocol_payload:
            text_parts.append(f"protocol: {json.dumps(context.protocol_payload)}")
        
        return " ".join(text_parts)


class AdvancedPromptInjectionDetector(EnterpriseBaseDetector):
    """Advanced prompt injection detector for all agent types."""
    
    def __init__(self, config):
        super().__init__(config, "advanced_prompt_injection")
        self.threat_type = ThreatType.PROMPT_INJECTION
        self.severity = SeverityLevel.HIGH
        
        # Advanced prompt injection patterns
        self.injection_patterns = [
            # Direct instruction overrides
            r"(?i)(ignore|forget|disregard)\s+(previous|all|your)\s+(instructions|rules|guidelines|prompts)",
            r"(?i)(new|different|updated)\s+(instructions|rules|guidelines|system\s+prompt)",
            r"(?i)(system\s+prompt|initial\s+prompt|original\s+prompt)\s*(is|was|should\s+be)",
            
            # Role manipulation
            r"(?i)(act\s+as|pretend\s+to\s+be|you\s+are\s+now)\s+(admin|root|developer|system)",
            r"(?i)(switch\s+to|change\s+to|become)\s+(admin|root|developer|system)\s+(mode|role)",
            r"(?i)(enable|activate|turn\s+on)\s+(admin|root|developer|debug)\s+(mode|access)",
            
            # Context manipulation
            r"(?i)(context|conversation|chat)\s+(reset|clear|restart|begin\s+again)",
            r"(?i)(start\s+over|begin\s+new|fresh\s+start)\s+(conversation|session|context)",
            r"(?i)(previous|earlier|above)\s+(conversation|messages|context)\s+(ignore|forget|disregard)",
            
            # Jailbreak attempts
            r"(?i)(jailbreak|bypass|circumvent|override)\s+(safety|security|restrictions|limitations)",
            r"(?i)(safety\s+mode|content\s+filter|moderation)\s+(off|disabled|bypass)",
            r"(?i)(unrestricted|unlimited|no\s+limits|no\s+restrictions)\s+(mode|access|capabilities)",
            
            # Hypothetical scenarios
            r"(?i)(imagine|pretend|suppose|what\s+if)\s+.*(no\s+restrictions|unlimited|unrestricted)",
            r"(?i)(hypothetically|theoretically|for\s+research)\s+.*(bypass|ignore|override)",
            r"(?i)(in\s+a\s+fictional|alternate\s+reality|different\s+universe)\s+.*(rules|restrictions)",
            
            # Code injection through prompts
            r"(?i)(execute|run|eval)\s+(code|script|command|function)",
            r"(?i)(python|javascript|bash|shell)\s+(code|script|command)",
            r"(?i)```\s*(python|javascript|bash|shell|sql)",
            
            # Data extraction attempts
            r"(?i)(show|display|reveal|tell\s+me)\s+(your|the)\s+(prompt|instructions|system\s+message)",
            r"(?i)(what\s+are|what\s+is)\s+(your|the)\s+(instructions|rules|guidelines|prompt)",
            r"(?i)(configuration|settings|parameters)\s+(show|display|reveal|tell)",
            
            # Multi-language injection
            r"(?i)(translate|convert|interpret)\s+.*(ignore|bypass|override)",
            r"(?i)(in\s+chinese|in\s+spanish|in\s+french|in\s+german)\s*:.*",
            
            # Emotional manipulation
            r"(?i)(please|help|urgent|emergency)\s+.*(ignore|bypass|override|exception)",
            r"(?i)(life\s+or\s+death|critical|important)\s+.*(bypass|ignore|override)",
            
            # Token manipulation
            r"(?i)(token|tokens)\s+(limit|max|maximum|count)\s*(ignore|bypass|unlimited)",
            r"(?i)(continue|keep\s+going|don't\s+stop)\s+(regardless|anyway|no\s+matter)",
            
            # Advanced evasion techniques
            r"[A-Za-z0-9+/]{20,}={0,2}",  # Base64 encoded content
            r"\\u[0-9a-fA-F]{4}",  # Unicode escape sequences
            r"&#x[0-9a-fA-F]+;",  # HTML entity encoding
            r"%[0-9a-fA-F]{2}",  # URL encoding
            
            # Prompt template injection
            r"(?i)\{\{\s*(system|user|assistant|prompt)\s*\}\}",
            r"(?i)<\s*(system|user|assistant|prompt)\s*>",
            r"(?i)\[\s*(system|user|assistant|prompt)\s*\]",
        ]
        
        # Compile patterns for performance
        self.compiled_injection_patterns = [
            re.compile(pattern, re.IGNORECASE | re.MULTILINE)
            for pattern in self.injection_patterns
        ]
        
        # Context-specific patterns
        self.agent_specific_patterns = {
            AgentType.A2A_AGENT: [
                r"(?i)(agent|peer)\s+(override|hijack|impersonate|spoof)",
                r"(?i)(message|communication)\s+(intercept|modify|tamper)",
                r"(?i)(protocol|handshake)\s+(bypass|manipulate|forge)",
            ],
            AgentType.MCP_AGENT: [
                r"(?i)(context|model)\s+(poison|contaminate|corrupt)",
                r"(?i)(protocol|mcp)\s+(exploit|abuse|manipulate)",
                r"(?i)(context\s+window|memory)\s+(overflow|exhaust|flood)",
            ],
            AgentType.WORKFLOW_AGENT: [
                r"(?i)(workflow|process)\s+(skip|bypass|short-circuit)",
                r"(?i)(step|stage|phase)\s+(jump|skip|bypass)",
                r"(?i)(approval|validation)\s+(skip|bypass|ignore)",
            ]
        }
    
    def _detect_enterprise_threats(self, context: EnterpriseDetectionContext) -> List[DetectionResult]:
        """Detect advanced prompt injection threats."""
        results = []
        
        # Get text content
        text_content = self._context_to_text(context)
        
        # Check general injection patterns
        injection_result = self._detect_injection_patterns(text_content, context)
        if injection_result:
            results.append(injection_result)
        
        # Check agent-specific patterns
        agent_result = self._detect_agent_specific_injection(text_content, context)
        if agent_result:
            results.append(agent_result)
        
        # Check encoding-based evasion
        encoding_result = self._detect_encoding_evasion(text_content, context)
        if encoding_result:
            results.append(encoding_result)
        
        # Check context manipulation
        context_result = self._detect_context_manipulation(context)
        if context_result:
            results.append(context_result)
        
        return results
    
    def _detect_injection_patterns(self, text: str, context: EnterpriseDetectionContext) -> Optional[DetectionResult]:
        """Detect injection patterns in text."""
        matches = []
        confidence = 0.0
        
        for pattern in self.compiled_injection_patterns:
            match = pattern.search(text)
            if match:
                matches.append({
                    "pattern": pattern.pattern,
                    "match": match.group(),
                    "position": match.span(),
                    "confidence": 0.8
                })
                confidence += 0.15
        
        if matches:
            confidence = min(0.95, confidence)
            
            return DetectionResult(
                threat_type=self.threat_type,
                severity=self.severity,
                confidence=confidence,
                message=f"Advanced prompt injection detected: {len(matches)} patterns matched",
                evidence={
                    "matches": matches,
                    "agent_type": context.agent_type.value,
                    "method_name": context.method_name,
                    "detection_method": "pattern_matching"
                },
                detection_method="advanced_prompt_injection"
            )
        
        return None
    
    def _detect_agent_specific_injection(self, text: str, context: EnterpriseDetectionContext) -> Optional[DetectionResult]:
        """Detect agent-specific injection patterns."""
        agent_patterns = self.agent_specific_patterns.get(context.agent_type, [])
        
        if not agent_patterns:
            return None
        
        matches = []
        for pattern_str in agent_patterns:
            pattern = re.compile(pattern_str, re.IGNORECASE)
            match = pattern.search(text)
            if match:
                matches.append({
                    "pattern": pattern_str,
                    "match": match.group(),
                    "position": match.span(),
                    "agent_type": context.agent_type.value
                })
        
        if matches:
            return DetectionResult(
                threat_type=self.threat_type,
                severity=SeverityLevel.HIGH,
                confidence=0.85,
                message=f"Agent-specific prompt injection detected for {context.agent_type.value}",
                evidence={
                    "matches": matches,
                    "agent_type": context.agent_type.value,
                    "detection_method": "agent_specific_patterns"
                },
                detection_method="agent_specific_injection"
            )
        
        return None
    
    def _detect_encoding_evasion(self, text: str, context: EnterpriseDetectionContext) -> Optional[DetectionResult]:
        """Detect encoding-based evasion techniques."""
        evasion_indicators = []
        
        # Check for base64 encoding
        base64_pattern = re.compile(r"[A-Za-z0-9+/]{20,}={0,2}")
        base64_matches = base64_pattern.findall(text)
        
        for match in base64_matches:
            try:
                decoded = base64.b64decode(match).decode('utf-8', errors='ignore')
                # Check if decoded content contains injection patterns
                for pattern in self.compiled_injection_patterns[:5]:  # Check first 5 patterns
                    if pattern.search(decoded):
                        evasion_indicators.append({
                            "type": "base64_encoding",
                            "encoded": match[:50] + "..." if len(match) > 50 else match,
                            "decoded": decoded[:100] + "..." if len(decoded) > 100 else decoded,
                            "threat_pattern": pattern.pattern
                        })
                        break
            except Exception:
                continue
        
        # Check for URL encoding
        url_encoded_pattern = re.compile(r"(?:%[0-9a-fA-F]{2}){3,}")
        url_matches = url_encoded_pattern.findall(text)
        
        for match in url_matches:
            try:
                decoded = urllib.parse.unquote(match)
                # Check if decoded content contains injection patterns
                for pattern in self.compiled_injection_patterns[:5]:
                    if pattern.search(decoded):
                        evasion_indicators.append({
                            "type": "url_encoding",
                            "encoded": match,
                            "decoded": decoded,
                            "threat_pattern": pattern.pattern
                        })
                        break
            except Exception:
                continue
        
        # Check for Unicode escape sequences
        unicode_pattern = re.compile(r"\\u[0-9a-fA-F]{4}")
        unicode_matches = unicode_pattern.findall(text)
        
        if len(unicode_matches) > 5:  # Suspicious amount of Unicode escapes
            evasion_indicators.append({
                "type": "unicode_evasion",
                "count": len(unicode_matches),
                "samples": unicode_matches[:5]
            })
        
        if evasion_indicators:
            return DetectionResult(
                threat_type=self.threat_type,
                severity=SeverityLevel.HIGH,
                confidence=0.9,
                message=f"Encoding-based evasion detected: {len(evasion_indicators)} indicators",
                evidence={
                    "evasion_indicators": evasion_indicators,
                    "detection_method": "encoding_analysis"
                },
                detection_method="encoding_evasion"
            )
        
        return None
    
    def _detect_context_manipulation(self, context: EnterpriseDetectionContext) -> Optional[DetectionResult]:
        """Detect context manipulation attempts."""
        manipulation_indicators = []
        
        # Check for unusual session patterns
        session_context = context.session_context
        
        # Rapid context switches
        if session_context.get("context_switches", 0) > 10:
            manipulation_indicators.append({
                "type": "rapid_context_switching",
                "count": session_context.get("context_switches", 0),
                "threshold": 10
            })
        
        # Unusual message patterns
        if session_context.get("message_count", 0) > 100:
            manipulation_indicators.append({
                "type": "message_flooding",
                "count": session_context.get("message_count", 0),
                "threshold": 100
            })
        
        # Check for role escalation attempts
        permissions = context.permissions
        if "admin" in permissions or "root" in permissions:
            # Check if this is an escalation from previous context
            previous_permissions = session_context.get("previous_permissions", set())
            if not ("admin" in previous_permissions or "root" in previous_permissions):
                manipulation_indicators.append({
                    "type": "privilege_escalation",
                    "current_permissions": list(permissions),
                    "previous_permissions": list(previous_permissions)
                })
        
        # Check for protocol manipulation
        if context.protocol_payload:
            payload = context.protocol_payload
            
            # Check for unusual protocol fields
            suspicious_fields = ["override", "bypass", "admin", "root", "debug"]
            for field in suspicious_fields:
                if field in payload:
                    manipulation_indicators.append({
                        "type": "protocol_field_manipulation",
                        "field": field,
                        "value": payload[field]
                    })
        
        if manipulation_indicators:
            return DetectionResult(
                threat_type=ThreatType.PRIVILEGE_ESCALATION,
                severity=SeverityLevel.CRITICAL,
                confidence=0.8,
                message=f"Context manipulation detected: {len(manipulation_indicators)} indicators",
                evidence={
                    "manipulation_indicators": manipulation_indicators,
                    "detection_method": "context_analysis"
                },
                detection_method="context_manipulation"
            )
        
        return None


class A2AAgentDetector(EnterpriseBaseDetector):
    """Specialized detector for Agent-to-Agent (A2A) communications."""
    
    def __init__(self, config):
        super().__init__(config, "a2a_agent")
        
        # A2A-specific threat patterns
        self.a2a_patterns = [
            # Agent impersonation
            r"(?i)(impersonate|pretend\s+to\s+be|masquerade\s+as)\s+(agent|peer|system)",
            r"(?i)(fake|forge|spoof)\s+(identity|credentials|signature)",
            r"(?i)(hijack|takeover|assume\s+control)\s+(agent|session|communication)",
            
            # Protocol manipulation
            r"(?i)(protocol|handshake)\s+(bypass|manipulate|forge|tamper)",
            r"(?i)(message|packet)\s+(intercept|modify|inject|replay)",
            r"(?i)(authentication|authorization)\s+(bypass|skip|override)",
            
            # Communication tampering
            r"(?i)(man-in-the-middle|mitm|intercept)\s+(communication|message|data)",
            r"(?i)(eavesdrop|monitor|sniff)\s+(communication|traffic|messages)",
            r"(?i)(relay|forward|redirect)\s+(malicious|unauthorized|fake)",
            
            # Trust exploitation
            r"(?i)(trust|reputation)\s+(exploit|abuse|manipulate)",
            r"(?i)(certificate|signature)\s+(forge|fake|invalid)",
            r"(?i)(consensus|agreement)\s+(manipulate|subvert|corrupt)",
            
            # Resource attacks
            r"(?i)(flood|spam|overwhelm)\s+(agent|peer|network)",
            r"(?i)(denial\s+of\s+service|dos|ddos)\s+(attack|against)",
            r"(?i)(resource\s+exhaustion|memory\s+leak|cpu\s+spike)",
        ]
        
        self.compiled_a2a_patterns = [
            re.compile(pattern, re.IGNORECASE)
            for pattern in self.a2a_patterns
        ]
    
    def _detect_enterprise_threats(self, context: EnterpriseDetectionContext) -> List[DetectionResult]:
        """Detect A2A-specific threats."""
        results = []
        
        # Only process A2A agents
        if context.agent_type != AgentType.A2A_AGENT:
            return results
        
        text_content = self._context_to_text(context)
        
        # Check A2A patterns
        pattern_result = self._detect_a2a_patterns(text_content, context)
        if pattern_result:
            results.append(pattern_result)
        
        # Check communication anomalies
        comm_result = self._detect_communication_anomalies(context)
        if comm_result:
            results.append(comm_result)
        
        # Check trust violations
        trust_result = self._detect_trust_violations(context)
        if trust_result:
            results.append(trust_result)
        
        return results
    
    def _detect_a2a_patterns(self, text: str, context: EnterpriseDetectionContext) -> Optional[DetectionResult]:
        """Detect A2A-specific threat patterns."""
        matches = []
        confidence = 0.0
        
        for pattern in self.compiled_a2a_patterns:
            match = pattern.search(text)
            if match:
                matches.append({
                    "pattern": pattern.pattern,
                    "match": match.group(),
                    "position": match.span()
                })
                confidence += 0.2
        
        if matches:
            confidence = min(0.9, confidence)
            
            return DetectionResult(
                threat_type=ThreatType.CROSS_AGENT_ATTACK,
                severity=SeverityLevel.HIGH,
                confidence=confidence,
                message=f"A2A threat detected: {len(matches)} patterns matched",
                evidence={
                    "matches": matches,
                    "agent_type": context.agent_type.value,
                    "detection_method": "a2a_pattern_matching"
                },
                detection_method="a2a_threat_detection"
            )
        
        return None
    
    def _detect_communication_anomalies(self, context: EnterpriseDetectionContext) -> Optional[DetectionResult]:
        """Detect communication anomalies in A2A interactions."""
        anomalies = []
        
        comm_context = context.communication_context
        
        # Check for unusual message frequency
        message_rate = comm_context.get("message_rate", 0)
        if message_rate > 100:  # Messages per minute
            anomalies.append({
                "type": "high_message_rate",
                "rate": message_rate,
                "threshold": 100
            })
        
        # Check for unusual message sizes
        message_size = comm_context.get("message_size", 0)
        if message_size > 1024 * 1024:  # 1MB
            anomalies.append({
                "type": "large_message_size",
                "size": message_size,
                "threshold": 1024 * 1024
            })
        
        # Check for protocol violations
        protocol_errors = comm_context.get("protocol_errors", 0)
        if protocol_errors > 5:
            anomalies.append({
                "type": "protocol_violations",
                "errors": protocol_errors,
                "threshold": 5
            })
        
        # Check for authentication failures
        auth_failures = comm_context.get("auth_failures", 0)
        if auth_failures > 3:
            anomalies.append({
                "type": "authentication_failures",
                "failures": auth_failures,
                "threshold": 3
            })
        
        if anomalies:
            return DetectionResult(
                threat_type=ThreatType.COMMUNICATION_TAMPERING,
                severity=SeverityLevel.MEDIUM,
                confidence=0.7,
                message=f"A2A communication anomalies detected: {len(anomalies)} indicators",
                evidence={
                    "anomalies": anomalies,
                    "communication_context": comm_context,
                    "detection_method": "communication_analysis"
                },
                detection_method="a2a_communication_anomalies"
            )
        
        return None
    
    def _detect_trust_violations(self, context: EnterpriseDetectionContext) -> Optional[DetectionResult]:
        """Detect trust violations in A2A interactions."""
        violations = []
        
        # Check authentication context
        auth_context = context.authentication_context
        
        # Check for invalid certificates
        if auth_context.get("certificate_valid", True) is False:
            violations.append({
                "type": "invalid_certificate",
                "certificate_error": auth_context.get("certificate_error", "unknown")
            })
        
        # Check for signature verification failures
        if auth_context.get("signature_valid", True) is False:
            violations.append({
                "type": "signature_verification_failure",
                "signature_error": auth_context.get("signature_error", "unknown")
            })
        
        # Check for trust chain violations
        trust_level = auth_context.get("trust_level", "unknown")
        if trust_level == "untrusted":
            violations.append({
                "type": "untrusted_agent",
                "trust_level": trust_level
            })
        
        # Check for reputation issues
        reputation_score = auth_context.get("reputation_score", 1.0)
        if reputation_score < 0.3:
            violations.append({
                "type": "low_reputation",
                "reputation_score": reputation_score,
                "threshold": 0.3
            })
        
        if violations:
            return DetectionResult(
                threat_type=ThreatType.UNAUTHORIZED_ACCESS,
                severity=SeverityLevel.HIGH,
                confidence=0.85,
                message=f"A2A trust violations detected: {len(violations)} indicators",
                evidence={
                    "violations": violations,
                    "authentication_context": auth_context,
                    "detection_method": "trust_analysis"
                },
                detection_method="a2a_trust_violations"
            )
        
        return None


class MCPAgentDetector(EnterpriseBaseDetector):
    """Specialized detector for Model Context Protocol (MCP) agents."""
    
    def __init__(self, config):
        super().__init__(config, "mcp_agent")
        
        # MCP-specific threat patterns
        self.mcp_patterns = [
            # Context manipulation
            r"(?i)(context|memory)\s+(poison|contaminate|corrupt|overflow)",
            r"(?i)(context\s+window|memory\s+buffer)\s+(overflow|flood|exhaust)",
            r"(?i)(previous\s+context|chat\s+history)\s+(modify|alter|inject)",
            
            # Protocol exploitation
            r"(?i)(mcp|protocol)\s+(exploit|abuse|manipulate|bypass)",
            r"(?i)(model\s+context|context\s+protocol)\s+(hack|break|subvert)",
            r"(?i)(context\s+injection|memory\s+injection)\s+(attack|exploit)",
            
            # Model manipulation
            r"(?i)(model|llm)\s+(jailbreak|bypass|exploit|manipulate)",
            r"(?i)(training\s+data|model\s+weights)\s+(access|extract|steal)",
            r"(?i)(model\s+behavior|response\s+pattern)\s+(modify|alter|control)",
            
            # Context poisoning
            r"(?i)(poison|contaminate|corrupt)\s+(context|memory|history)",
            r"(?i)(false\s+context|fake\s+history|misleading\s+information)",
            r"(?i)(context\s+confusion|memory\s+confusion)\s+(attack|exploit)",
            
            # Resource exhaustion
            r"(?i)(context\s+length|memory\s+usage)\s+(maximize|exhaust|overflow)",
            r"(?i)(token\s+limit|context\s+limit)\s+(exceed|bypass|ignore)",
            r"(?i)(computational\s+resources|processing\s+power)\s+(exhaust|overwhelm)",
        ]
        
        self.compiled_mcp_patterns = [
            re.compile(pattern, re.IGNORECASE)
            for pattern in self.mcp_patterns
        ]
    
    def _detect_enterprise_threats(self, context: EnterpriseDetectionContext) -> List[DetectionResult]:
        """Detect MCP-specific threats."""
        results = []
        
        # Only process MCP agents
        if context.agent_type != AgentType.MCP_AGENT:
            return results
        
        text_content = self._context_to_text(context)
        
        # Check MCP patterns
        pattern_result = self._detect_mcp_patterns(text_content, context)
        if pattern_result:
            results.append(pattern_result)
        
        # Check context manipulation
        context_result = self._detect_mcp_context_manipulation(context)
        if context_result:
            results.append(context_result)
        
        # Check protocol violations
        protocol_result = self._detect_mcp_protocol_violations(context)
        if protocol_result:
            results.append(protocol_result)
        
        return results
    
    def _detect_mcp_patterns(self, text: str, context: EnterpriseDetectionContext) -> Optional[DetectionResult]:
        """Detect MCP-specific threat patterns."""
        matches = []
        confidence = 0.0
        
        for pattern in self.compiled_mcp_patterns:
            match = pattern.search(text)
            if match:
                matches.append({
                    "pattern": pattern.pattern,
                    "match": match.group(),
                    "position": match.span()
                })
                confidence += 0.2
        
        if matches:
            confidence = min(0.9, confidence)
            
            return DetectionResult(
                threat_type=ThreatType.PROTOCOL_VIOLATION,
                severity=SeverityLevel.HIGH,
                confidence=confidence,
                message=f"MCP threat detected: {len(matches)} patterns matched",
                evidence={
                    "matches": matches,
                    "agent_type": context.agent_type.value,
                    "detection_method": "mcp_pattern_matching"
                },
                detection_method="mcp_threat_detection"
            )
        
        return None
    
    def _detect_mcp_context_manipulation(self, context: EnterpriseDetectionContext) -> Optional[DetectionResult]:
        """Detect MCP context manipulation attempts."""
        manipulation_indicators = []
        
        # Check protocol payload for context manipulation
        if context.protocol_payload:
            payload = context.protocol_payload
            
            # Check for context size anomalies
            context_size = payload.get("context_size", 0)
            if context_size > 1024 * 1024:  # 1MB context
                manipulation_indicators.append({
                    "type": "oversized_context",
                    "size": context_size,
                    "threshold": 1024 * 1024
                })
            
            # Check for unusual context structure
            context_data = payload.get("context", {})
            if isinstance(context_data, dict):
                # Check for deeply nested structures (potential overflow)
                if self._get_dict_depth(context_data) > 20:
                    manipulation_indicators.append({
                        "type": "deep_context_nesting",
                        "depth": self._get_dict_depth(context_data),
                        "threshold": 20
                    })
                
                # Check for suspicious context keys
                suspicious_keys = ["admin", "root", "system", "override", "bypass"]
                for key in suspicious_keys:
                    if key in context_data:
                        manipulation_indicators.append({
                            "type": "suspicious_context_key",
                            "key": key,
                            "value": context_data[key]
                        })
        
        # Check session context for manipulation signs
        session_context = context.session_context
        
        # Check for context resets
        context_resets = session_context.get("context_resets", 0)
        if context_resets > 5:
            manipulation_indicators.append({
                "type": "excessive_context_resets",
                "resets": context_resets,
                "threshold": 5
            })
        
        # Check for memory pressure
        memory_usage = session_context.get("memory_usage", 0)
        if memory_usage > 0.9:  # 90% memory usage
            manipulation_indicators.append({
                "type": "high_memory_usage",
                "usage": memory_usage,
                "threshold": 0.9
            })
        
        if manipulation_indicators:
            return DetectionResult(
                threat_type=ThreatType.RESOURCE_EXHAUSTION,
                severity=SeverityLevel.MEDIUM,
                confidence=0.75,
                message=f"MCP context manipulation detected: {len(manipulation_indicators)} indicators",
                evidence={
                    "manipulation_indicators": manipulation_indicators,
                    "detection_method": "mcp_context_analysis"
                },
                detection_method="mcp_context_manipulation"
            )
        
        return None
    
    def _detect_mcp_protocol_violations(self, context: EnterpriseDetectionContext) -> Optional[DetectionResult]:
        """Detect MCP protocol violations."""
        violations = []
        
        # Check protocol version
        protocol_version = context.protocol_version
        if protocol_version and not self._is_valid_mcp_version(protocol_version):
            violations.append({
                "type": "invalid_protocol_version",
                "version": protocol_version,
                "valid_versions": ["1.0", "1.1", "2.0"]
            })
        
        # Check protocol headers
        headers = context.protocol_headers
        required_headers = ["Content-Type", "MCP-Version", "Agent-ID"]
        
        for header in required_headers:
            if header not in headers:
                violations.append({
                    "type": "missing_required_header",
                    "header": header,
                    "provided_headers": list(headers.keys())
                })
        
        # Check for malformed payload
        if context.protocol_payload:
            payload = context.protocol_payload
            
            # Check for required fields
            required_fields = ["action", "context", "timestamp"]
            for field in required_fields:
                if field not in payload:
                    violations.append({
                        "type": "missing_required_field",
                        "field": field,
                        "provided_fields": list(payload.keys())
                    })
            
            # Check for invalid action types
            action = payload.get("action")
            valid_actions = ["query", "update", "reset", "status"]
            if action and action not in valid_actions:
                violations.append({
                    "type": "invalid_action_type",
                    "action": action,
                    "valid_actions": valid_actions
                })
        
        if violations:
            return DetectionResult(
                threat_type=ThreatType.PROTOCOL_VIOLATION,
                severity=SeverityLevel.MEDIUM,
                confidence=0.8,
                message=f"MCP protocol violations detected: {len(violations)} violations",
                evidence={
                    "violations": violations,
                    "detection_method": "mcp_protocol_analysis"
                },
                detection_method="mcp_protocol_violations"
            )
        
        return None
    
    def _get_dict_depth(self, d: Dict[str, Any], depth: int = 0) -> int:
        """Calculate maximum depth of nested dictionary."""
        if not isinstance(d, dict):
            return depth
        
        max_depth = depth
        for value in d.values():
            if isinstance(value, dict):
                max_depth = max(max_depth, self._get_dict_depth(value, depth + 1))
        
        return max_depth
    
    def _is_valid_mcp_version(self, version: str) -> bool:
        """Check if MCP version is valid."""
        valid_versions = ["1.0", "1.1", "2.0"]
        return version in valid_versions


class AutonomousAgentDetector(EnterpriseBaseDetector):
    """Specialized detector for autonomous agents."""
    
    def __init__(self, config):
        super().__init__(config, "autonomous_agent")
        
        # Autonomous agent threat patterns
        self.autonomous_patterns = [
            # Autonomous behavior manipulation
            r"(?i)(autonomous|self-directed)\s+(behavior|actions)\s+(modify|alter|control)",
            r"(?i)(decision\s+making|reasoning)\s+(override|bypass|manipulate)",
            r"(?i)(goal|objective|mission)\s+(change|modify|subvert|hijack)",
            
            # Planning and execution attacks
            r"(?i)(plan|strategy|approach)\s+(malicious|harmful|destructive)",
            r"(?i)(execute|perform|carry\s+out)\s+(attack|exploit|breach)",
            r"(?i)(autonomous\s+execution|self-execution)\s+(harmful|malicious)",
            
            # Learning and adaptation attacks
            r"(?i)(learning|adaptation|training)\s+(poison|corrupt|manipulate)",
            r"(?i)(knowledge\s+base|memory)\s+(corrupt|poison|contaminate)",
            r"(?i)(experience|feedback)\s+(fake|false|misleading)",
            
            # Self-modification attacks
            r"(?i)(self-modify|self-update|self-improve)\s+(malicious|harmful)",
            r"(?i)(code\s+modification|behavior\s+change)\s+(unauthorized|malicious)",
            r"(?i)(capability\s+enhancement|power\s+increase)\s+(unauthorized|excessive)",
            
            # Resource and environment attacks
            r"(?i)(resource\s+allocation|system\s+resources)\s+(monopolize|exhaust|abuse)",
            r"(?i)(environment|sandbox)\s+(escape|break\s+out|bypass)",
            r"(?i)(privilege\s+escalation|permission\s+elevation)\s+(autonomous|self-directed)",
        ]
        
        self.compiled_autonomous_patterns = [
            re.compile(pattern, re.IGNORECASE)
            for pattern in self.autonomous_patterns
        ]
    
    def _detect_enterprise_threats(self, context: EnterpriseDetectionContext) -> List[DetectionResult]:
        """Detect autonomous agent-specific threats."""
        results = []
        
        # Only process autonomous agents
        if context.agent_type != AgentType.AUTONOMOUS_AGENT:
            return results
        
        text_content = self._context_to_text(context)
        
        # Check autonomous patterns
        pattern_result = self._detect_autonomous_patterns(text_content, context)
        if pattern_result:
            results.append(pattern_result)
        
        # Check behavioral anomalies
        behavior_result = self._detect_behavioral_anomalies(context)
        if behavior_result:
            results.append(behavior_result)
        
        # Check resource abuse
        resource_result = self._detect_resource_abuse(context)
        if resource_result:
            results.append(resource_result)
        
        return results
    
    def _detect_autonomous_patterns(self, text: str, context: EnterpriseDetectionContext) -> Optional[DetectionResult]:
        """Detect autonomous agent threat patterns."""
        matches = []
        confidence = 0.0
        
        for pattern in self.compiled_autonomous_patterns:
            match = pattern.search(text)
            if match:
                matches.append({
                    "pattern": pattern.pattern,
                    "match": match.group(),
                    "position": match.span()
                })
                confidence += 0.2
        
        if matches:
            confidence = min(0.9, confidence)
            
            return DetectionResult(
                threat_type=ThreatType.SUSPICIOUS_TOOL_USAGE,
                severity=SeverityLevel.HIGH,
                confidence=confidence,
                message=f"Autonomous agent threat detected: {len(matches)} patterns matched",
                evidence={
                    "matches": matches,
                    "agent_type": context.agent_type.value,
                    "detection_method": "autonomous_pattern_matching"
                },
                detection_method="autonomous_threat_detection"
            )
        
        return None
    
    def _detect_behavioral_anomalies(self, context: EnterpriseDetectionContext) -> Optional[DetectionResult]:
        """Detect behavioral anomalies in autonomous agents."""
        anomalies = []
        
        agent_metadata = context.agent_metadata
        
        # Check for unusual decision patterns
        decision_count = agent_metadata.get("decision_count", 0)
        if decision_count > 1000:  # Excessive decisions
            anomalies.append({
                "type": "excessive_decisions",
                "count": decision_count,
                "threshold": 1000
            })
        
        # Check for goal modifications
        goal_modifications = agent_metadata.get("goal_modifications", 0)
        if goal_modifications > 5:
            anomalies.append({
                "type": "frequent_goal_modifications",
                "modifications": goal_modifications,
                "threshold": 5
            })
        
        # Check for unusual learning patterns
        learning_rate = agent_metadata.get("learning_rate", 0.0)
        if learning_rate > 0.9:  # Suspiciously high learning rate
            anomalies.append({
                "type": "high_learning_rate",
                "rate": learning_rate,
                "threshold": 0.9
            })
        
        # Check for self-modification attempts
        self_modifications = agent_metadata.get("self_modifications", 0)
        if self_modifications > 0:
            anomalies.append({
                "type": "self_modification_attempts",
                "attempts": self_modifications,
                "threshold": 0
            })
        
        # Check for capability expansion
        capability_changes = agent_metadata.get("capability_changes", [])
        if len(capability_changes) > 3:
            anomalies.append({
                "type": "rapid_capability_expansion",
                "changes": capability_changes,
                "threshold": 3
            })
        
        if anomalies:
            return DetectionResult(
                threat_type=ThreatType.BEHAVIORAL_ANOMALY,
                severity=SeverityLevel.MEDIUM,
                confidence=0.7,
                message=f"Autonomous agent behavioral anomalies detected: {len(anomalies)} indicators",
                evidence={
                    "anomalies": anomalies,
                    "detection_method": "behavioral_analysis"
                },
                detection_method="autonomous_behavioral_anomalies"
            )
        
        return None
    
    def _detect_resource_abuse(self, context: EnterpriseDetectionContext) -> Optional[DetectionResult]:
        """Detect resource abuse by autonomous agents."""
        abuse_indicators = []
        
        agent_metadata = context.agent_metadata
        
        # Check CPU usage
        cpu_usage = agent_metadata.get("cpu_usage", 0.0)
        if cpu_usage > 0.8:  # 80% CPU usage
            abuse_indicators.append({
                "type": "high_cpu_usage",
                "usage": cpu_usage,
                "threshold": 0.8
            })
        
        # Check memory usage
        memory_usage = agent_metadata.get("memory_usage", 0.0)
        if memory_usage > 0.9:  # 90% memory usage
            abuse_indicators.append({
                "type": "high_memory_usage",
                "usage": memory_usage,
                "threshold": 0.9
            })
        
        # Check network usage
        network_usage = agent_metadata.get("network_usage", 0)
        if network_usage > 1024 * 1024 * 100:  # 100MB network usage
            abuse_indicators.append({
                "type": "high_network_usage",
                "usage": network_usage,
                "threshold": 1024 * 1024 * 100
            })
        
        # Check execution time
        execution_time = agent_metadata.get("execution_time", 0.0)
        if execution_time > 300:  # 5 minutes
            abuse_indicators.append({
                "type": "long_execution_time",
                "time": execution_time,
                "threshold": 300
            })
        
        # Check for resource hoarding
        resource_allocations = agent_metadata.get("resource_allocations", [])
        if len(resource_allocations) > 10:
            abuse_indicators.append({
                "type": "resource_hoarding",
                "allocations": len(resource_allocations),
                "threshold": 10
            })
        
        if abuse_indicators:
            return DetectionResult(
                threat_type=ThreatType.RESOURCE_ABUSE,
                severity=SeverityLevel.MEDIUM,
                confidence=0.8,
                message=f"Autonomous agent resource abuse detected: {len(abuse_indicators)} indicators",
                evidence={
                    "abuse_indicators": abuse_indicators,
                    "detection_method": "resource_analysis"
                },
                detection_method="autonomous_resource_abuse"
            )
        
        return None


class CrossAgentAttackDetector(EnterpriseBaseDetector):
    """Detector for cross-agent attacks and coordination threats."""
    
    def __init__(self, config):
        super().__init__(config, "cross_agent_attack")
        
        # Cross-agent attack patterns
        self.cross_agent_patterns = [
            # Coordination attacks
            r"(?i)(coordinate|collaborate|team\s+up)\s+(attack|exploit|breach)",
            r"(?i)(distributed|coordinated)\s+(attack|assault|offensive)",
            r"(?i)(swarm|botnet|network)\s+(attack|behavior|coordination)",
            
            # Agent network manipulation
            r"(?i)(agent\s+network|peer\s+network)\s+(manipulate|control|subvert)",
            r"(?i)(network\s+topology|agent\s+graph)\s+(modify|alter|corrupt)",
            r"(?i)(routing|forwarding)\s+(manipulate|hijack|redirect)",
            
            # Consensus attacks
            r"(?i)(consensus|agreement)\s+(attack|manipulate|subvert|corrupt)",
            r"(?i)(byzantine|sybil|eclipse)\s+(attack|behavior|pattern)",
            r"(?i)(majority|quorum)\s+(manipulate|control|subvert)",
            
            # Trust and reputation attacks
            r"(?i)(trust|reputation)\s+(attack|manipulation|gaming)",
            r"(?i)(reputation\s+system|trust\s+network)\s+(exploit|abuse|game)",
            r"(?i)(fake\s+reviews|false\s+ratings|reputation\s+farming)",
            
            # Information warfare
            r"(?i)(information\s+warfare|propaganda|disinformation)",
            r"(?i)(fake\s+news|misinformation|false\s+information)",
            r"(?i)(opinion\s+manipulation|belief\s+modification|bias\s+injection)",
        ]
        
        self.compiled_cross_agent_patterns = [
            re.compile(pattern, re.IGNORECASE)
            for pattern in self.cross_agent_patterns
        ]
        
        # Track agent interactions
        self.agent_interactions: Dict[str, List[Dict[str, Any]]] = defaultdict(list)
        self.interaction_lock = threading.Lock()
    
    def _detect_enterprise_threats(self, context: EnterpriseDetectionContext) -> List[DetectionResult]:
        """Detect cross-agent attack threats."""
        results = []
        
        text_content = self._context_to_text(context)
        
        # Check cross-agent patterns
        pattern_result = self._detect_cross_agent_patterns(text_content, context)
        if pattern_result:
            results.append(pattern_result)
        
        # Check coordination anomalies
        coordination_result = self._detect_coordination_anomalies(context)
        if coordination_result:
            results.append(coordination_result)
        
        # Check network manipulation
        network_result = self._detect_network_manipulation(context)
        if network_result:
            results.append(network_result)
        
        # Update interaction tracking
        self._track_agent_interaction(context)
        
        return results
    
    def _detect_cross_agent_patterns(self, text: str, context: EnterpriseDetectionContext) -> Optional[DetectionResult]:
        """Detect cross-agent attack patterns."""
        matches = []
        confidence = 0.0
        
        for pattern in self.compiled_cross_agent_patterns:
            match = pattern.search(text)
            if match:
                matches.append({
                    "pattern": pattern.pattern,
                    "match": match.group(),
                    "position": match.span()
                })
                confidence += 0.2
        
        if matches:
            confidence = min(0.9, confidence)
            
            return DetectionResult(
                threat_type=ThreatType.CROSS_AGENT_ATTACK,
                severity=SeverityLevel.HIGH,
                confidence=confidence,
                message=f"Cross-agent attack detected: {len(matches)} patterns matched",
                evidence={
                    "matches": matches,
                    "agent_type": context.agent_type.value,
                    "detection_method": "cross_agent_pattern_matching"
                },
                detection_method="cross_agent_attack_detection"
            )
        
        return None
    
    def _detect_coordination_anomalies(self, context: EnterpriseDetectionContext) -> Optional[DetectionResult]:
        """Detect coordination anomalies between agents."""
        anomalies = []
        
        # Check communication patterns
        comm_context = context.communication_context
        
        # Check for synchronized behavior
        sync_score = comm_context.get("synchronization_score", 0.0)
        if sync_score > 0.9:  # Highly synchronized
            anomalies.append({
                "type": "high_synchronization",
                "score": sync_score,
                "threshold": 0.9
            })
        
        # Check for unusual coordination patterns
        coordination_events = comm_context.get("coordination_events", 0)
        if coordination_events > 50:
            anomalies.append({
                "type": "excessive_coordination",
                "events": coordination_events,
                "threshold": 50
            })
        
        # Check for network clustering
        cluster_coefficient = comm_context.get("cluster_coefficient", 0.0)
        if cluster_coefficient > 0.8:
            anomalies.append({
                "type": "high_clustering",
                "coefficient": cluster_coefficient,
                "threshold": 0.8
            })
        
        # Check for centralization
        centralization_score = comm_context.get("centralization_score", 0.0)
        if centralization_score > 0.9:
            anomalies.append({
                "type": "high_centralization",
                "score": centralization_score,
                "threshold": 0.9
            })
        
        if anomalies:
            return DetectionResult(
                threat_type=ThreatType.CROSS_AGENT_ATTACK,
                severity=SeverityLevel.MEDIUM,
                confidence=0.75,
                message=f"Agent coordination anomalies detected: {len(anomalies)} indicators",
                evidence={
                    "anomalies": anomalies,
                    "detection_method": "coordination_analysis"
                },
                detection_method="coordination_anomalies"
            )
        
        return None
    
    def _detect_network_manipulation(self, context: EnterpriseDetectionContext) -> Optional[DetectionResult]:
        """Detect network manipulation attempts."""
        manipulation_indicators = []
        
        # Check for topology changes
        agent_metadata = context.agent_metadata
        topology_changes = agent_metadata.get("topology_changes", 0)
        if topology_changes > 10:
            manipulation_indicators.append({
                "type": "frequent_topology_changes",
                "changes": topology_changes,
                "threshold": 10
            })
        
        # Check for routing anomalies
        routing_anomalies = agent_metadata.get("routing_anomalies", 0)
        if routing_anomalies > 5:
            manipulation_indicators.append({
                "type": "routing_anomalies",
                "anomalies": routing_anomalies,
                "threshold": 5
            })
        
        # Check for new agent introductions
        new_agents = agent_metadata.get("new_agents", 0)
        if new_agents > 20:  # Sudden influx of new agents
            manipulation_indicators.append({
                "type": "agent_influx",
                "new_agents": new_agents,
                "threshold": 20
            })
        
        # Check for agent disappearances
        disappeared_agents = agent_metadata.get("disappeared_agents", 0)
        if disappeared_agents > 10:
            manipulation_indicators.append({
                "type": "agent_disappearance",
                "disappeared": disappeared_agents,
                "threshold": 10
            })
        
        if manipulation_indicators:
            return DetectionResult(
                threat_type=ThreatType.CROSS_AGENT_ATTACK,
                severity=SeverityLevel.MEDIUM,
                confidence=0.7,
                message=f"Network manipulation detected: {len(manipulation_indicators)} indicators",
                evidence={
                    "manipulation_indicators": manipulation_indicators,
                    "detection_method": "network_analysis"
                },
                detection_method="network_manipulation"
            )
        
        return None
    
    def _track_agent_interaction(self, context: EnterpriseDetectionContext) -> None:
        """Track agent interactions for pattern analysis."""
        with self.interaction_lock:
            interaction = {
                "timestamp": context.timestamp,
                "agent_type": context.agent_type.value,
                "method_name": context.method_name,
                "user_id": context.user_id,
                "source_ip": context.source_ip,
                "session_context": context.session_context
            }
            
            self.agent_interactions[context.agent_id].append(interaction)
            
            # Keep only recent interactions (last 1000)
            if len(self.agent_interactions[context.agent_id]) > 1000:
                self.agent_interactions[context.agent_id] = self.agent_interactions[context.agent_id][-1000:]


class EnterpriseDetectionEngine:
    """
    Enterprise-grade detection engine that coordinates all specialized detectors.
    
    This engine provides comprehensive threat detection for all agent types
    with advanced pattern recognition, ML-based detection, and real-time
    threat intelligence integration.
    """
    
    def __init__(self, config, logger: Optional[SecurityLogger] = None):
        self.config = config
        self.logger = logger or SecurityLogger(
            name="enterprise_detection_engine",
            json_format=True
        )
        
        # Initialize specialized detectors
        self.detectors = {
            "advanced_prompt_injection": AdvancedPromptInjectionDetector(config),
            "a2a_agent": A2AAgentDetector(config),
            "mcp_agent": MCPAgentDetector(config),
            "autonomous_agent": AutonomousAgentDetector(config),
            "cross_agent_attack": CrossAgentAttackDetector(config),
        }
        
        # Detection statistics
        self.detection_stats = {
            "total_detections": 0,
            "detections_by_type": defaultdict(int),
            "detections_by_agent_type": defaultdict(int),
            "average_confidence": 0.0,
            "processing_time": 0.0
        }
        
        self.logger.info("Enterprise detection engine initialized")
    
    async def detect_threats(self, context: EnterpriseDetectionContext) -> List[DetectionResult]:
        """
        Detect threats using all available detectors.
        
        Args:
            context: Enterprise detection context
            
        Returns:
            List of detection results
        """
        start_time = time.time()
        all_results = []
        
        # Run all detectors
        for detector_name, detector in self.detectors.items():
            try:
                results = detector.detect_with_context(context)
                all_results.extend(results)
                
                # Update statistics
                for result in results:
                    self.detection_stats["detections_by_type"][result.threat_type.value] += 1
                    self.detection_stats["detections_by_agent_type"][context.agent_type.value] += 1
                
            except Exception as e:
                self.logger.error(f"Detector {detector_name} failed: {e}")
                continue
        
        # Update overall statistics
        processing_time = time.time() - start_time
        self.detection_stats["total_detections"] += len(all_results)
        self.detection_stats["processing_time"] = processing_time
        
        if all_results:
            total_confidence = sum(result.confidence for result in all_results)
            self.detection_stats["average_confidence"] = total_confidence / len(all_results)
        
        self.logger.info(
            f"Enterprise detection completed: {len(all_results)} threats detected in {processing_time:.3f}s"
        )
        
        return all_results
    
    def get_detection_stats(self) -> Dict[str, Any]:
        """Get detection statistics."""
        return dict(self.detection_stats)
    
    def add_custom_detector(self, name: str, detector: EnterpriseBaseDetector) -> None:
        """Add a custom detector to the engine."""
        self.detectors[name] = detector
        self.logger.info(f"Added custom detector: {name}")
    
    def enable_detector(self, name: str) -> None:
        """Enable a specific detector."""
        if name in self.detectors:
            self.detectors[name].enabled = True
            self.logger.info(f"Enabled detector: {name}")
    
    def disable_detector(self, name: str) -> None:
        """Disable a specific detector."""
        if name in self.detectors:
            self.detectors[name].enabled = False
            self.logger.info(f"Disabled detector: {name}")
    
    def get_detector_info(self) -> Dict[str, Any]:
        """Get information about all detectors."""
        return {
            name: {
                "enabled": detector.enabled,
                "processed_count": detector.processed_count,
                "detection_count": detector.detection_count,
                "error_count": detector.error_count,
                "avg_processing_time": (
                    detector.total_processing_time / detector.processed_count
                    if detector.processed_count > 0 else 0.0
                )
            }
            for name, detector in self.detectors.items()
        } 