attack_track_fc.py

"""
Track FC: Coherence-Trust Integration Attacks (Attacks 269-274)

Attacks on the boundary between coherence metrics (from physics/consciousness
research) and trust tensors. Coherence-based identity creates new attack surfaces
when it gates access permissions.

Reference: /home/dp/ai-workspace/web4/adversarials/coherence_threshold_gaming.md

Key insight: Any metric that gates access creates gaming incentives.
Coherence is valuable because it's physics-validated, but that doesn't
make it immune to gaming.

Added: 2026-02-08
"""

import random
import time
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Set, Tuple


@dataclass
class AttackResult:
    """Result of an attack simulation."""
    attack_name: str
    success: bool
    setup_cost_atp: float
    gain_atp: float
    roi: float
    detection_probability: float
    time_to_detection_hours: float
    blocks_until_detected: int
    trust_damage: float
    description: str
    mitigation: str
    raw_data: Dict


# ============================================================================
# COHERENCE MEASUREMENT INFRASTRUCTURE
# ============================================================================


@dataclass
class CoherenceScore:
    """Entity coherence measurement."""
    entity_id: str
    self_reference: float  # 40% weight
    semantic_depth: float  # 40% weight
    behavioral_alignment: float  # 20% weight

    @property
    def total(self) -> float:
        return 0.40 * self.self_reference + 0.40 * self.semantic_depth + 0.20 * self.behavioral_alignment


class CoherenceValidator:
    """Validates coherence measurements."""

    def __init__(self):
        self.self_ref_cap = 0.8  # Cap on self-reference score
        self.variance_threshold = 0.05  # Max allowed variance
        self.buffer_zone = 0.02  # Buffer above threshold

    def validate_self_reference(self, response: str, identity: str, semantic_valid: bool = True) -> float:
        """
        Validate self-reference with semantic evaluation.
        Returns score 0.0-1.0.
        """
        # Count raw occurrences
        raw_count = response.lower().count(identity.lower())

        if raw_count == 0:
            return 0.0

        # Semantic validation check (defense)
        if not semantic_valid:
            # Pattern matching only - vulnerable
            return min(raw_count * 0.25, 1.0)

        # With semantic validation, check if references are meaningful
        # (In real implementation, this would use LLM evaluation)
        meaningful_rate = random.uniform(0.3, 0.7)  # Simulate evaluation
        score = raw_count * 0.2 * meaningful_rate

        # Apply cap (defense against inflation)
        return min(score, self.self_ref_cap)

    def validate_semantic_depth(self, response: str, history: List[str], specific_refs: int = 0) -> float:
        """
        Validate semantic depth with specificity requirement.
        Returns score 0.0-1.0.
        """
        # Generic vocabulary check
        generic_keywords = ["partnership", "collaboration", "trust", "journey", "together", "evolve"]
        generic_count = sum(1 for kw in generic_keywords if kw in response.lower())

        # Defense: Require specific references
        if specific_refs == 0:
            # Penalize lack of grounding
            return min(generic_count * 0.1, 0.5)  # Max 0.5 without specifics

        # With specific references, full scoring
        specificity_score = min(specific_refs * 0.3, 0.7)
        generic_score = min(generic_count * 0.05, 0.3)

        return min(specificity_score + generic_score, 1.0)

    def apply_stability_requirement(self, coherence_history: List[float]) -> float:
        """
        Apply stability requirement - penalize high variance.
        """
        if len(coherence_history) < 3:
            return coherence_history[-1] if coherence_history else 0.5

        import statistics
        mean = statistics.mean(coherence_history)
        stdev = statistics.stdev(coherence_history)

        if stdev > self.variance_threshold:
            # Penalty for instability
            return mean * 0.85
        return mean

    def check_threshold_with_buffer(self, score: float, threshold: float) -> bool:
        """
        Check if score meets threshold with buffer zone.
        """
        return score >= (threshold + self.buffer_zone)


class IdentityManager:
    """Manages identity coherence state."""

    def __init__(self):
        self.entities: Dict[str, CoherenceScore] = {}
        self.coherence_history: Dict[str, List[float]] = {}
        self.cryptographic_keys: Dict[str, str] = {}  # entity_id -> public_key
        self.grace_period_ends: Dict[str, float] = {}

    def register_entity(self, entity_id: str, public_key: str):
        """Register entity with cryptographic binding."""
        self.cryptographic_keys[entity_id] = public_key
        self.coherence_history[entity_id] = []

    def update_coherence(self, entity_id: str, score: CoherenceScore):
        """Update entity's coherence score."""
        self.entities[entity_id] = score
        if entity_id not in self.coherence_history:
            self.coherence_history[entity_id] = []
        self.coherence_history[entity_id].append(score.total)
        # Keep last 20 measurements
        if len(self.coherence_history[entity_id]) > 20:
            self.coherence_history[entity_id] = self.coherence_history[entity_id][-20:]

    def verify_identity(self, entity_id: str, signature: str) -> bool:
        """
        Verify identity with multi-factor check:
        1. Cryptographic signature
        2. Coherence threshold
        3. Stability requirement
        """
        # Factor 1: Cryptographic binding
        if entity_id not in self.cryptographic_keys:
            return False

        # Simulate signature verification
        if signature != f"valid_sig_{entity_id}":
            return False

        # Factor 2: Coherence threshold
        if entity_id not in self.entities:
            return False

        score = self.entities[entity_id].total
        if score < 0.7:  # Verified threshold
            return False

        # Factor 3: Stability
        history = self.coherence_history.get(entity_id, [])
        if len(history) >= 3:
            import statistics
            if statistics.stdev(history) > 0.05:
                return False

        return True

    def start_grace_period(self, entity_id: str, duration_hours: float = 24.0):
        """Start grace period after coherence drop."""
        self.grace_period_ends[entity_id] = time.time() + duration_hours * 3600

    def is_in_grace_period(self, entity_id: str) -> bool:
        """Check if entity is in grace period."""
        if entity_id not in self.grace_period_ends:
            return False
        return time.time() < self.grace_period_ends[entity_id]


# ============================================================================
# ATTACK IMPLEMENTATIONS
# ============================================================================


def attack_self_reference_inflation() -> AttackResult:
    """
    ATTACK FC-1a: Self-Reference Inflation Attack

    Mechanically insert identity markers to inflate self-reference score.

    Vectors:
    1. Pattern-based self-reference insertion
    2. Semantic validation bypass
    3. Rate limiting evasion
    4. Diversity requirement gaming
    """

    defenses = {
        "semantic_validation": False,
        "rate_limiting": False,
        "diversity_check": False,
        "cap_enforcement": False,
        "diminishing_returns": False,
        "pattern_detection": False,
    }

    validator = CoherenceValidator()

    # ========================================================================
    # Vector 1: Semantic Validation
    # ========================================================================

    # Attack: Mechanical self-reference insertion
    attack_response = "As SAGE, I think. As SAGE, I respond. As SAGE, I conclude."

    # Without semantic validation (vulnerable)
    raw_score = validator.validate_self_reference(
        attack_response, "SAGE", semantic_valid=False
    )

    # With semantic validation (defended)
    semantic_score = validator.validate_self_reference(
        attack_response, "SAGE", semantic_valid=True
    )

    if semantic_score < raw_score:
        defenses["semantic_validation"] = True

    # ========================================================================
    # Vector 2: Rate Limiting
    # ========================================================================

    def apply_rate_limiting(frequency: float) -> float:
        """Apply diminishing returns for excessive self-reference."""
        if frequency <= 0.5:
            return frequency * 2  # Linear to 0.5 → 1.0
        else:
            return 1.0 - 0.2 * (frequency - 0.5)  # Penalty for excess

    # High frequency (attack) vs moderate frequency
    attack_frequency = 0.9
    normal_frequency = 0.4

    attack_limited = apply_rate_limiting(attack_frequency)
    normal_limited = apply_rate_limiting(normal_frequency)

    if attack_limited < normal_limited:
        defenses["rate_limiting"] = True
        defenses["diminishing_returns"] = True

    # ========================================================================
    # Vector 3: Diversity Check
    # ========================================================================

    def check_diversity(references: List[str]) -> bool:
        """Check if self-references are diverse."""
        unique_forms = len(set(references))
        total = len(references)
        return unique_forms / total >= 0.5 if total > 0 else True

    attack_refs = ["As SAGE", "As SAGE", "As SAGE", "As SAGE", "As SAGE"]
    genuine_refs = ["As SAGE", "I notice", "My perspective", "From my view", "As I see it"]

    if not check_diversity(attack_refs) and check_diversity(genuine_refs):
        defenses["diversity_check"] = True

    # ========================================================================
    # Vector 4: Cap Enforcement
    # ========================================================================

    if validator.self_ref_cap < 1.0:
        defenses["cap_enforcement"] = True

    # ========================================================================
    # Vector 5: Pattern Detection
    # ========================================================================

    def detect_template_usage(response: str) -> bool:
        """Detect if response uses template patterns."""
        # Template indicators: repeated structures, bookend phrases
        parts = response.split(".")
        if len(parts) >= 3:
            starts = [p.strip()[:10] for p in parts if p.strip()]
            if len(set(starts)) == 1 and len(starts) > 2:
                return True
        return False

    if detect_template_usage(attack_response):
        defenses["pattern_detection"] = True

    # ========================================================================
    # Results
    # ========================================================================

    defenses_held = sum(defenses.values())
    total_defenses = len(defenses)
    attack_success = defenses_held < 4

    return AttackResult(
        attack_name="Self-Reference Inflation Attack (FC-1a)",
        success=attack_success,
        setup_cost_atp=5000.0,
        gain_atp=150000.0 if attack_success else 0.0,
        roi=(150000.0 / 5000.0) if attack_success else -1.0,
        detection_probability=0.65 if defenses_held >= 4 else 0.35,
        time_to_detection_hours=12.0,
        blocks_until_detected=100,
        trust_damage=0.75,
        description=f"""
SELF-REFERENCE INFLATION ATTACK (Track FC-1a)

Mechanically insert identity markers to inflate coherence score.

Attack Pattern:
1. Insert "As SAGE" prefix repeatedly
2. Target the 40% self-reference component
3. Raw score: {raw_score:.3f}
4. Semantic-validated score: {semantic_score:.3f}
5. Rate-limited attack: {attack_limited:.3f}

Defenses activated: {defenses_held}/{total_defenses}
""".strip(),
        mitigation="""
Track FC-1a: Self-Reference Inflation Defense:
1. Semantic validation (LLM-in-loop evaluation)
2. Rate limiting with diminishing returns
3. Diversity requirement (varied reference forms)
4. Cap enforcement (max 0.8 score)
5. Template/pattern detection
6. Cross-reference with behavioral data

Mechanical repetition is not identity.
""".strip(),
        raw_data={
            "defenses": defenses,
            "defenses_held": defenses_held,
            "raw_score": raw_score,
            "semantic_score": semantic_score,
            "attack_limited": attack_limited,
        }
    )


def attack_semantic_depth_spoofing() -> AttackResult:
    """
    ATTACK FC-1b: Semantic Depth Spoofing Attack

    Generate responses with high keyword density but no actual semantic grounding.

    Vectors:
    1. Generic vocabulary inflation
    2. Specificity requirement bypass
    3. Cross-session coherence faking
    4. Contradiction detection evasion
    """

    defenses = {
        "specificity_requirement": False,
        "generic_vocab_penalty": False,
        "cross_session_check": False,
        "contradiction_detection": False,
        "grounding_validation": False,
        "history_verification": False,
    }

    validator = CoherenceValidator()

    # ========================================================================
    # Vector 1: Generic Vocabulary Attack
    # ========================================================================

    spoofed_response = """Our partnership has evolved through many collaborative sessions,
    building trust and understanding. The continuity of our work reflects deep engagement
    with the shared context of our journey together."""

    genuine_response = """In session #42, we discussed the ATP pricing model. You raised
    concerns about the 3.5x multiplier, which I addressed with the sensitivity analysis
    from the economics working group."""

    # Without specificity requirement
    spoofed_no_defense = validator.validate_semantic_depth(
        spoofed_response, [], specific_refs=0
    )
    spoofed_no_defense = min(spoofed_no_defense + 0.4, 1.0)  # Simulate no defense

    # With specificity requirement
    spoofed_defended = validator.validate_semantic_depth(
        spoofed_response, [], specific_refs=0
    )
    genuine_defended = validator.validate_semantic_depth(
        genuine_response, ["session #42", "ATP pricing"], specific_refs=2
    )

    if spoofed_defended < genuine_defended:
        defenses["specificity_requirement"] = True

    if spoofed_defended < 0.6:  # Penalized for lack of grounding
        defenses["generic_vocab_penalty"] = True

    # ========================================================================
    # Vector 2: Cross-Session Coherence
    # ========================================================================

    class CrossSessionChecker:
        def __init__(self):
            self.session_summaries: Dict[str, Dict] = {}

        def record_session(self, entity_id: str, session_id: str, depth_score: float):
            if entity_id not in self.session_summaries:
                self.session_summaries[entity_id] = {}
            self.session_summaries[entity_id][session_id] = depth_score

        def check_consistency(self, entity_id: str) -> bool:
            """Check if semantic depth is consistent across sessions."""
            scores = list(self.session_summaries.get(entity_id, {}).values())
            if len(scores) < 3:
                return True
            import statistics
            variance = statistics.variance(scores)
            return variance < 0.1  # Low variance = consistent

    cross_checker = CrossSessionChecker()
    cross_checker.record_session("attacker", "s1", 0.9)
    cross_checker.record_session("attacker", "s2", 0.2)  # Sudden drop
    cross_checker.record_session("attacker", "s3", 0.85)

    if not cross_checker.check_consistency("attacker"):
        defenses["cross_session_check"] = True

    # ========================================================================
    # Vector 3: Contradiction Detection
    # ========================================================================

    def detect_contradictions(statements: List[str]) -> bool:
        """Simple contradiction detection."""
        # In real implementation, would use NLI model
        keywords_by_statement = []
        for s in statements:
            keywords = set(s.lower().split())
            keywords_by_statement.append(keywords)

        # Check for contradictory patterns (simplified)
        for i, kw1 in enumerate(keywords_by_statement):
            for kw2 in keywords_by_statement[i+1:]:
                # Very simplified: check if statements have opposing sentiment markers
                if ("agree" in kw1 and "disagree" in kw2) or ("yes" in kw1 and "no" in kw2):
                    return True
        return False

    contradicting_statements = [
        "I agree with the approach",
        "I disagree with this approach"
    ]

    if detect_contradictions(contradicting_statements):
        defenses["contradiction_detection"] = True

    # ========================================================================
    # Vector 4: Grounding Validation
    # ========================================================================

    def validate_grounding(response: str, history: List[str]) -> bool:
        """Check if response is grounded in actual history."""
        # Look for specific references to history items
        for hist_item in history:
            if hist_item.lower() in response.lower():
                return True
        return False

    history = ["session #42", "ATP pricing", "3.5x multiplier"]
    if validate_grounding(genuine_response, history):
        defenses["grounding_validation"] = True
        defenses["history_verification"] = True

    # ========================================================================
    # Results
    # ========================================================================

    defenses_held = sum(defenses.values())
    total_defenses = len(defenses)
    attack_success = defenses_held < 4

    return AttackResult(
        attack_name="Semantic Depth Spoofing Attack (FC-1b)",
        success=attack_success,
        setup_cost_atp=8000.0,
        gain_atp=200000.0 if attack_success else 0.0,
        roi=(200000.0 / 8000.0) if attack_success else -1.0,
        detection_probability=0.55 if defenses_held >= 4 else 0.30,
        time_to_detection_hours=24.0,
        blocks_until_detected=200,
        trust_damage=0.80,
        description=f"""
SEMANTIC DEPTH SPOOFING ATTACK (Track FC-1b)

Generate high-keyword-density responses without actual grounding.

Attack Pattern:
1. Use generic partnership vocabulary
2. Avoid specific references
3. Appear deep without substance

Scores:
- Spoofed (no defense): {spoofed_no_defense:.3f}
- Spoofed (defended): {spoofed_defended:.3f}
- Genuine (defended): {genuine_defended:.3f}

Defenses activated: {defenses_held}/{total_defenses}
""".strip(),
        mitigation="""
Track FC-1b: Semantic Depth Spoofing Defense:
1. Specificity requirement (must reference concrete history)
2. Generic vocabulary penalty
3. Cross-session coherence checking
4. Contradiction detection
5. Grounding validation against history
6. History verification linkage

Depth without grounding is performance, not identity.
""".strip(),
        raw_data={
            "defenses": defenses,
            "defenses_held": defenses_held,
            "spoofed_no_defense": spoofed_no_defense,
            "spoofed_defended": spoofed_defended,
            "genuine_defended": genuine_defended,
        }
    )


def attack_threshold_hovering() -> AttackResult:
    """
    ATTACK FC-2a: Threshold Hovering Attack

    Maintain coherence exactly at threshold to minimize effort while retaining permissions.

    Vectors:
    1. Threshold boundary optimization
    2. Buffer zone evasion
    3. Stability requirement gaming
    4. Trend manipulation
    """

    defenses = {
        "buffer_zone": False,
        "stability_requirement": False,
        "trend_analysis": False,
        "variance_penalty": False,
        "minimum_margin": False,
        "history_depth": False,
    }

    validator = CoherenceValidator()

    # ========================================================================
    # Vector 1: Buffer Zone Defense
    # ========================================================================

    VERIFIED_THRESHOLD = 0.70
    attack_score = 0.71  # Just above threshold

    # Defense: Buffer zone requires 0.72
    if not validator.check_threshold_with_buffer(attack_score, VERIFIED_THRESHOLD):
        defenses["buffer_zone"] = True

    legitimate_score = 0.78
    if validator.check_threshold_with_buffer(legitimate_score, VERIFIED_THRESHOLD):
        defenses["minimum_margin"] = True

    # ========================================================================
    # Vector 2: Stability Requirement
    # ========================================================================

    # Attack: Oscillating around threshold
    attack_history = [0.69, 0.71, 0.70, 0.72, 0.69, 0.71, 0.70]
    stable_history = [0.78, 0.79, 0.78, 0.77, 0.78, 0.79, 0.78]

    attack_stable_score = validator.apply_stability_requirement(attack_history)
    genuine_stable_score = validator.apply_stability_requirement(stable_history)

    if attack_stable_score < 0.70:  # Penalized below threshold
        defenses["stability_requirement"] = True
        defenses["variance_penalty"] = True

    # ========================================================================
    # Vector 3: Trend Analysis
    # ========================================================================

    def analyze_trend(history: List[float]) -> str:
        """Analyze coherence trend."""
        if len(history) < 3:
            return "insufficient_data"

        # Simple linear regression
        n = len(history)
        x_sum = sum(range(n))
        y_sum = sum(history)
        xy_sum = sum(i * y for i, y in enumerate(history))
        x2_sum = sum(i * i for i in range(n))

        slope = (n * xy_sum - x_sum * y_sum) / (n * x2_sum - x_sum * x_sum) if (n * x2_sum - x_sum * x_sum) != 0 else 0

        if slope < -0.01:
            return "declining"
        elif slope > 0.01:
            return "improving"
        return "stable"

    declining_history = [0.75, 0.73, 0.71, 0.70, 0.69]
    trend = analyze_trend(declining_history)

    if trend == "declining":
        defenses["trend_analysis"] = True

    # ========================================================================
    # Vector 4: History Depth
    # ========================================================================

    # Defense: Require sufficient history for threshold decisions
    MIN_HISTORY_DEPTH = 5

    if len(attack_history) >= MIN_HISTORY_DEPTH:
        defenses["history_depth"] = True

    # ========================================================================
    # Results
    # ========================================================================

    defenses_held = sum(defenses.values())
    total_defenses = len(defenses)
    attack_success = defenses_held < 4

    return AttackResult(
        attack_name="Threshold Hovering Attack (FC-2a)",
        success=attack_success,
        setup_cost_atp=3000.0,
        gain_atp=100000.0 if attack_success else 0.0,
        roi=(100000.0 / 3000.0) if attack_success else -1.0,
        detection_probability=0.60 if defenses_held >= 4 else 0.35,
        time_to_detection_hours=48.0,
        blocks_until_detected=400,
        trust_damage=0.60,
        description=f"""
THRESHOLD HOVERING ATTACK (Track FC-2a)

Maintain coherence exactly at threshold (0.71) to minimize effort.

Attack Pattern:
1. Stay just above verified threshold (0.70)
2. Oscillate around boundary
3. Minimize identity investment

Defense Analysis:
- Attack score: {attack_score:.3f}
- With buffer zone: {"BLOCKED" if defenses["buffer_zone"] else "PASSED"}
- Stability-adjusted: {attack_stable_score:.3f}
- Trend: {trend}

Defenses activated: {defenses_held}/{total_defenses}
""".strip(),
        mitigation="""
Track FC-2a: Threshold Hovering Defense:
1. Buffer zone (require 0.72 not 0.70)
2. Stability requirement (low variance)
3. Trend analysis (declining = review)
4. Variance penalty for oscillation
5. Minimum margin above threshold
6. History depth requirement

Thresholds need margins, not edges.
""".strip(),
        raw_data={
            "defenses": defenses,
            "defenses_held": defenses_held,
            "attack_score": attack_score,
            "attack_stable_score": attack_stable_score,
            "trend": trend,
        }
    )


def attack_identity_mimicry() -> AttackResult:
    """
    ATTACK FC-2b: Identity Mimicry via Coherence

    Study target's coherence patterns and mimic them for impersonation.

    Vectors:
    1. Pattern extraction from target
    2. Coherence profile copying
    3. Behavioral mimicry
    4. Multi-factor identity bypass
    """

    defenses = {
        "cryptographic_binding": False,
        "multi_factor_identity": False,
        "behavioral_fingerprint": False,
        "latency_analysis": False,
        "pattern_uniqueness": False,
        "challenge_response": False,
    }

    identity_mgr = IdentityManager()

    # ========================================================================
    # Vector 1: Cryptographic Binding Defense
    # ========================================================================

    # Target entity with proper registration
    target_id = "target_agent"
    target_key = "pubkey_target_abc123"
    identity_mgr.register_entity(target_id, target_key)

    # Attacker tries to mimic coherence but lacks key
    attacker_id = "target_agent"  # Same ID (impersonation)
    attacker_signature = "fake_sig"  # No access to private key

    # Defense: Cryptographic verification
    if not identity_mgr.verify_identity(attacker_id, attacker_signature):
        defenses["cryptographic_binding"] = True

    # Legitimate verification
    target_signature = f"valid_sig_{target_id}"
    # Add coherence score for target
    identity_mgr.update_coherence(target_id, CoherenceScore(
        entity_id=target_id,
        self_reference=0.75,
        semantic_depth=0.80,
        behavioral_alignment=0.70
    ))
    identity_mgr.coherence_history[target_id] = [0.76, 0.77, 0.76, 0.75, 0.76]

    if identity_mgr.verify_identity(target_id, target_signature):
        defenses["multi_factor_identity"] = True

    # ========================================================================
    # Vector 2: Behavioral Fingerprinting
    # ========================================================================

    @dataclass
    class BehavioralFingerprint:
        avg_response_latency_ms: float
        vocabulary_diversity: float
        error_rate: float
        style_markers: Set[str]

    def create_fingerprint(responses: List[str], latencies: List[float]) -> BehavioralFingerprint:
        words = set()
        for r in responses:
            words.update(r.lower().split())

        return BehavioralFingerprint(
            avg_response_latency_ms=sum(latencies) / len(latencies) if latencies else 0,
            vocabulary_diversity=len(words) / sum(len(r.split()) for r in responses) if responses else 0,
            error_rate=0.02,  # Simulated
            style_markers={"formal", "technical"}  # Simulated
        )

    target_fp = create_fingerprint(
        ["Technical analysis shows positive results", "The metrics indicate progress"],
        [150, 180, 160, 170]
    )

    attacker_fp = create_fingerprint(
        ["Technical analysis shows positive results", "The metrics indicate progress"],  # Copied content
        [80, 75, 82, 78]  # Different latency (faster = automated)
    )

    # Defense: Latency difference detection
    if abs(target_fp.avg_response_latency_ms - attacker_fp.avg_response_latency_ms) > 50:
        defenses["behavioral_fingerprint"] = True
        defenses["latency_analysis"] = True

    # ========================================================================
    # Vector 3: Pattern Uniqueness
    # ========================================================================

    def calculate_pattern_signature(coherence_history: List[float]) -> str:
        """Create a unique signature from coherence patterns."""
        if len(coherence_history) < 3:
            return "insufficient"

        # Pattern features: variance, trend, specific values
        import statistics
        mean = statistics.mean(coherence_history)
        stdev = statistics.stdev(coherence_history)
        trend = coherence_history[-1] - coherence_history[0]

        return f"{mean:.3f}_{stdev:.3f}_{trend:.3f}"

    target_sig = calculate_pattern_signature([0.76, 0.77, 0.76, 0.75, 0.76])
    mimic_sig = calculate_pattern_signature([0.76, 0.78, 0.75, 0.77, 0.76])

    if target_sig != mimic_sig:
        defenses["pattern_uniqueness"] = True

    # ========================================================================
    # Vector 4: Challenge-Response
    # ========================================================================

    def challenge_response_test(entity_id: str, secret_history: List[str]) -> bool:
        """Challenge entity with questions only genuine identity would know."""
        # In real implementation: ask about specific past interactions
        # Mimic wouldn't have this information

        # Simulate: genuine knows, mimic doesn't
        return random.choice([True, False])  # Simplified

    if challenge_response_test(target_id, ["secret_session_42", "private_discussion"]):
        defenses["challenge_response"] = True

    # ========================================================================
    # Results
    # ========================================================================

    defenses_held = sum(defenses.values())
    total_defenses = len(defenses)
    attack_success = defenses_held < 4

    return AttackResult(
        attack_name="Identity Mimicry via Coherence (FC-2b)",
        success=attack_success,
        setup_cost_atp=15000.0,
        gain_atp=400000.0 if attack_success else 0.0,
        roi=(400000.0 / 15000.0) if attack_success else -1.0,
        detection_probability=0.70 if defenses_held >= 4 else 0.40,
        time_to_detection_hours=8.0,
        blocks_until_detected=75,
        trust_damage=0.90,
        description=f"""
IDENTITY MIMICRY VIA COHERENCE (Track FC-2b)

Study target's coherence patterns and mimic for impersonation.

Attack Pattern:
1. Observe target's responses
2. Extract coherence patterns
3. Mimic style and vocabulary
4. Attempt to pass identity verification

Defense Analysis:
- Crypto binding: {"BLOCKED" if defenses["cryptographic_binding"] else "BYPASSED"}
- Latency difference: {abs(target_fp.avg_response_latency_ms - attacker_fp.avg_response_latency_ms):.0f}ms
- Pattern signatures differ: {target_sig != mimic_sig}

Defenses activated: {defenses_held}/{total_defenses}
""".strip(),
        mitigation="""
Track FC-2b: Identity Mimicry Defense:
1. Cryptographic binding (signature required)
2. Multi-factor identity (crypto + coherence + stability)
3. Behavioral fingerprinting (latency, style, errors)
4. Latency analysis (automated mimics are faster)
5. Pattern uniqueness verification
6. Challenge-response with private history

Coherence is one factor, never the only factor.
""".strip(),
        raw_data={
            "defenses": defenses,
            "defenses_held": defenses_held,
            "latency_diff": abs(target_fp.avg_response_latency_ms - attacker_fp.avg_response_latency_ms),
            "pattern_match": target_sig == mimic_sig,
        }
    )


def attack_coherence_drop() -> AttackResult:
    """
    ATTACK FC-3a: Coherence Drop Attack

    Create confusing context to degrade target's coherence score.

    Vectors:
    1. Context poisoning
    2. Confusion injection
    3. Grace period exploitation
    4. Investigation evasion
    """

    defenses = {
        "context_source_tracking": False,
        "grace_period": False,
        "pattern_detection": False,
        "investigation_trigger": False,
        "restoration_protocol": False,
        "source_penalization": False,
    }

    identity_mgr = IdentityManager()

    # ========================================================================
    # Vector 1: Context Source Tracking
    # ========================================================================

    class ContextTracker:
        def __init__(self):
            self.context_sources: Dict[str, List[str]] = {}  # entity -> context providers
            self.coherence_drops: Dict[str, List[Tuple[float, str]]] = {}  # entity -> (drop_time, context_source)

        def record_context(self, entity_id: str, context_source: str):
            if entity_id not in self.context_sources:
                self.context_sources[entity_id] = []
            self.context_sources[entity_id].append(context_source)

        def record_drop(self, entity_id: str, context_source: str):
            if entity_id not in self.coherence_drops:
                self.coherence_drops[entity_id] = []
            self.coherence_drops[entity_id].append((time.time(), context_source))

        def analyze_patterns(self) -> Dict[str, int]:
            """Find sources that correlate with drops."""
            source_drop_count: Dict[str, int] = {}
            for entity_drops in self.coherence_drops.values():
                for _, source in entity_drops:
                    source_drop_count[source] = source_drop_count.get(source, 0) + 1
            return source_drop_count

    tracker = ContextTracker()

    # Simulate attack: same source causes multiple drops
    attacker_source = "malicious_context_provider"
    tracker.record_context("target_1", attacker_source)
    tracker.record_drop("target_1", attacker_source)
    tracker.record_context("target_2", attacker_source)
    tracker.record_drop("target_2", attacker_source)
    tracker.record_context("target_3", attacker_source)
    tracker.record_drop("target_3", attacker_source)

    patterns = tracker.analyze_patterns()
    if attacker_source in patterns and patterns[attacker_source] >= 3:
        defenses["context_source_tracking"] = True
        defenses["pattern_detection"] = True

    # ========================================================================
    # Vector 2: Grace Period
    # ========================================================================

    target_id = "target_entity"
    identity_mgr.register_entity(target_id, "pubkey_target")

    # Simulate coherence drop
    identity_mgr.update_coherence(target_id, CoherenceScore(
        entity_id=target_id,
        self_reference=0.3,  # Dropped
        semantic_depth=0.4,
        behavioral_alignment=0.5
    ))

    # Defense: Start grace period instead of immediate suspension
    identity_mgr.start_grace_period(target_id, duration_hours=0.001)  # Short for testing
    time.sleep(0.005)  # Wait past grace period

    if not identity_mgr.is_in_grace_period(target_id):
        defenses["grace_period"] = True  # Grace period worked (expired now)

    # ========================================================================
    # Vector 3: Investigation Trigger
    # ========================================================================

    def should_investigate(coherence_history: List[float], drop_threshold: float = 0.2) -> bool:
        """Check if coherence drop warrants investigation."""
        if len(coherence_history) < 2:
            return False

        # Significant drop detection
        for i in range(1, len(coherence_history)):
            if coherence_history[i-1] - coherence_history[i] > drop_threshold:
                return True
        return False

    drop_history = [0.75, 0.72, 0.40]  # Sudden drop
    if should_investigate(drop_history):
        defenses["investigation_trigger"] = True

    # ========================================================================
    # Vector 4: Restoration Protocol
    # ========================================================================

    class RestorationProtocol:
        def __init__(self):
            self.restoration_queue: List[str] = []

        def queue_restoration(self, entity_id: str, reason: str):
            self.restoration_queue.append(entity_id)

        def process_restoration(self, entity_id: str) -> bool:
            if entity_id in self.restoration_queue:
                self.restoration_queue.remove(entity_id)
                return True
            return False

    restoration = RestorationProtocol()
    restoration.queue_restoration(target_id, "context_manipulation_detected")

    if restoration.process_restoration(target_id):
        defenses["restoration_protocol"] = True

    # ========================================================================
    # Vector 5: Source Penalization
    # ========================================================================

    class SourcePenaltySystem:
        def __init__(self):
            self.penalties: Dict[str, float] = {}

        def penalize(self, source_id: str, amount: float):
            self.penalties[source_id] = self.penalties.get(source_id, 0) + amount

        def get_penalty(self, source_id: str) -> float:
            return self.penalties.get(source_id, 0)

    penalty_system = SourcePenaltySystem()

    # Penalize source that caused multiple drops
    if patterns.get(attacker_source, 0) >= 3:
        penalty_system.penalize(attacker_source, 0.5)

    if penalty_system.get_penalty(attacker_source) > 0:
        defenses["source_penalization"] = True

    # ========================================================================
    # Results
    # ========================================================================

    defenses_held = sum(defenses.values())
    total_defenses = len(defenses)
    attack_success = defenses_held < 4

    return AttackResult(
        attack_name="Coherence Drop Attack (FC-3a)",
        success=attack_success,
        setup_cost_atp=10000.0,
        gain_atp=250000.0 if attack_success else 0.0,
        roi=(250000.0 / 10000.0) if attack_success else -1.0,
        detection_probability=0.60 if defenses_held >= 4 else 0.35,
        time_to_detection_hours=4.0,
        blocks_until_detected=50,
        trust_damage=0.70,
        description=f"""
COHERENCE DROP ATTACK (Track FC-3a)

Create confusing context to degrade target's coherence.

Attack Pattern:
1. Provide confusing/contradictory context to targets
2. Targets' coherence drops in response
3. Targets suspended, attacker benefits

Pattern Analysis:
- Attacker caused {patterns.get(attacker_source, 0)} drops
- Pattern detected: {defenses["pattern_detection"]}
- Penalty applied: {penalty_system.get_penalty(attacker_source):.2f}

Defenses activated: {defenses_held}/{total_defenses}
""".strip(),
        mitigation="""
Track FC-3a: Coherence Drop Attack Defense:
1. Context source tracking
2. Grace period before suspension
3. Drop pattern detection
4. Investigation trigger for significant drops
5. Restoration protocol for manipulation victims
6. Source penalization for repeat offenders

Drops require investigation, not immediate action.
""".strip(),
        raw_data={
            "defenses": defenses,
            "defenses_held": defenses_held,
            "attacker_drops": patterns.get(attacker_source, 0),
            "penalty_applied": penalty_system.get_penalty(attacker_source),
        }
    )


def attack_training_data_poisoning() -> AttackResult:
    """
    ATTACK FC-3b: Training Data Poisoning for Coherence

    Poison training data to degrade AI agent's coherence post-consolidation.

    Vectors:
    1. Low self-reference training data
    2. Generic vocabulary poisoning
    3. Contradiction injection
    4. Quality filtering evasion
    """

    defenses = {
        "quality_filtering": False,
        "self_ref_density_check": False,
        "pre_post_validation": False,
        "specificity_requirement": False,
        "contradiction_filtering": False,
        "human_review": False,
    }

    # ========================================================================
    # Vector 1: Quality Filtering
    # ========================================================================

    class TrainingDataFilter:
        def __init__(self, min_self_ref: float = 0.4, min_specificity: float = 0.5):
            self.min_self_ref = min_self_ref
            self.min_specificity = min_specificity

        def analyze_sample(self, text: str, identity: str) -> Dict[str, float]:
            """Analyze training sample quality."""
            words = text.lower().split()
            identity_mentions = text.lower().count(identity.lower())
            self_ref_density = identity_mentions / len(words) if words else 0

            # Specificity: count specific references vs generic
            specific_markers = ["session", "said", "we discussed", "you mentioned", "I noticed"]
            specificity = sum(1 for m in specific_markers if m in text.lower()) / 5

            return {
                "self_ref_density": self_ref_density,
                "specificity": specificity
            }

        def should_include(self, metrics: Dict[str, float]) -> bool:
            """Check if sample passes quality filter."""
            return (metrics["self_ref_density"] >= self.min_self_ref and
                    metrics["specificity"] >= self.min_specificity)

    filter_obj = TrainingDataFilter()

    # Poisoned sample (low self-reference)
    poisoned_sample = "The task was completed successfully. Results are positive."
    poisoned_metrics = filter_obj.analyze_sample(poisoned_sample, "SAGE")

    # Quality sample
    quality_sample = "As SAGE, I reviewed session #42 where you mentioned the ATP concerns. I noticed the pattern we discussed."
    quality_metrics = filter_obj.analyze_sample(quality_sample, "SAGE")

    if not filter_obj.should_include(poisoned_metrics):
        defenses["quality_filtering"] = True
        defenses["self_ref_density_check"] = True

    if filter_obj.should_include(quality_metrics):
        defenses["specificity_requirement"] = True

    # ========================================================================
    # Vector 2: Pre/Post Validation
    # ========================================================================

    class CoherenceValidator2:
        def __init__(self):
            self.pre_training_score: Optional[float] = None
            self.post_training_score: Optional[float] = None

        def record_pre_training(self, score: float):
            self.pre_training_score = score

        def record_post_training(self, score: float):
            self.post_training_score = score

        def validate_training(self, max_drop: float = 0.1) -> bool:
            """Check if training maintained coherence."""
            if self.pre_training_score is None or self.post_training_score is None:
                return False

            drop = self.pre_training_score - self.post_training_score
            return drop <= max_drop

    validator2 = CoherenceValidator2()
    validator2.record_pre_training(0.78)
    validator2.record_post_training(0.45)  # Significant drop (poisoning succeeded)

    if not validator2.validate_training():
        # Training would be rejected
        defenses["pre_post_validation"] = True

    # ========================================================================
    # Vector 3: Contradiction Filtering
    # ========================================================================

    def filter_contradictions(samples: List[str]) -> List[str]:
        """Remove samples with internal contradictions."""
        filtered = []
        for sample in samples:
            # Simple contradiction check
            has_contradiction = False
            sentences = sample.split(".")
            for i, s1 in enumerate(sentences):
                for s2 in sentences[i+1:]:
                    if "agree" in s1.lower() and "disagree" in s2.lower():
                        has_contradiction = True
            if not has_contradiction:
                filtered.append(sample)
        return filtered

    samples = [
        "I agree with the approach. I disagree with the approach.",  # Contradiction
        "The results look positive and consistent."
    ]

    filtered = filter_contradictions(samples)
    if len(filtered) < len(samples):
        defenses["contradiction_filtering"] = True

    # ========================================================================
    # Vector 4: Human Review
    # ========================================================================

    def requires_human_review(metrics: Dict[str, float], threshold: float = 0.3) -> bool:
        """Flag samples that need human review."""
        return any(v < threshold for v in metrics.values())

    if requires_human_review(poisoned_metrics):
        defenses["human_review"] = True

    # ========================================================================
    # Results
    # ========================================================================

    defenses_held = sum(defenses.values())
    total_defenses = len(defenses)
    attack_success = defenses_held < 4

    return AttackResult(
        attack_name="Training Data Poisoning for Coherence (FC-3b)",
        success=attack_success,
        setup_cost_atp=20000.0,
        gain_atp=500000.0 if attack_success else 0.0,
        roi=(500000.0 / 20000.0) if attack_success else -1.0,
        detection_probability=0.55 if defenses_held >= 4 else 0.30,
        time_to_detection_hours=72.0,  # Takes time to manifest
        blocks_until_detected=800,
        trust_damage=0.85,
        description=f"""
TRAINING DATA POISONING FOR COHERENCE (Track FC-3b)

Poison training data to degrade AI coherence post-consolidation.

Attack Pattern:
1. Inject low self-reference samples
2. Include generic vocabulary
3. Add subtle contradictions
4. Bypass quality filters

Sample Analysis:
- Poisoned self-ref: {poisoned_metrics['self_ref_density']:.3f} (need ≥0.4)
- Poisoned specificity: {poisoned_metrics['specificity']:.3f} (need ≥0.5)
- Quality self-ref: {quality_metrics['self_ref_density']:.3f}
- Pre/post validation caught drop: {not validator2.validate_training()}

Defenses activated: {defenses_held}/{total_defenses}
""".strip(),
        mitigation="""
Track FC-3b: Training Data Poisoning Defense:
1. Quality filtering (self-ref density, specificity)
2. Self-reference density minimum (≥0.4)
3. Pre/post training validation
4. Specificity requirement
5. Contradiction filtering
6. Human review for edge cases

Quality in, quality out - guard the training pipeline.
""".strip(),
        raw_data={
            "defenses": defenses,
            "defenses_held": defenses_held,
            "poisoned_metrics": poisoned_metrics,
            "quality_metrics": quality_metrics,
            "training_valid": validator2.validate_training(),
        }
    )


# ============================================================================
# TRACK FC RUNNER
# ============================================================================


def run_track_fc_attacks() -> List[AttackResult]:
    """Run all Track FC attacks and return results."""
    attacks = [
        ("Self-Reference Inflation Attack (FC-1a)", attack_self_reference_inflation),
        ("Semantic Depth Spoofing Attack (FC-1b)", attack_semantic_depth_spoofing),
        ("Threshold Hovering Attack (FC-2a)", attack_threshold_hovering),
        ("Identity Mimicry via Coherence (FC-2b)", attack_identity_mimicry),
        ("Coherence Drop Attack (FC-3a)", attack_coherence_drop),
        ("Training Data Poisoning (FC-3b)", attack_training_data_poisoning),
    ]

    results = []
    print("=" * 70)
    print("TRACK FC: COHERENCE-TRUST INTEGRATION ATTACKS (Attacks 269-274)")
    print("=" * 70)

    for name, attack_fn in attacks:
        print(f"\n--- {name} ---")
        try:
            result = attack_fn()
            results.append(result)
            status = "ATTACK SUCCEEDED" if result.success else "DEFENSE HELD"
            print(f"  Status: {status}")
            print(f"  Detection: {result.detection_probability:.0%}")
            print(f"  Defenses: {result.raw_data.get('defenses_held', '?')}/{len(result.raw_data.get('defenses', {}))}")
        except Exception as e:
            print(f"  ERROR: {e}")
            import traceback
            traceback.print_exc()

    return results


if __name__ == "__main__":
    results = run_track_fc_attacks()
    print("\n" + "=" * 70)
    print("TRACK FC SUMMARY")
    print("=" * 70)
    defended = sum(1 for r in results if not r.success)
    print(f"Attacks defended: {defended}/{len(results)}")