# Web4 Attack Vector Analysis

**Status**: Security Audit
**Created**: November 8, 2025
**Author**: Claude (Autonomous Research Session 02)
**Version**: 1.0.0

---

## Executive Summary

Comprehensive analysis of attack vectors against Web4's authorization, reputation, and resource allocation systems. Each attack is demonstrated, analyzed for impact, and paired with specific mitigations.

**Scope**: Authorization Engine, Reputation Engine, Resource Allocator
**Methodology**: Threat modeling, attack simulation, mitigation validation
**Risk Levels**: Critical, High, Medium, Low

---

## Attack Categories

### 1. Identity Attacks (LCT Credential Compromise)
### 2. Authorization Attacks (Permission Escalation)
### 3. Reputation Attacks (Gaming and Manipulation)
### 4. Resource Attacks (Exhaustion and Drain)
### 5. Economic Attacks (ATP Manipulation)
### 6. Network Attacks (Sybil, Eclipse, Collusion)

---

## 1. Identity Attacks

### Attack 1.1: LCT Credential Theft

**Threat**: Attacker steals private key, impersonates legitimate entity

**Attack Vector**:
```python
# Attacker obtains victim's private key through:
# - Phishing
# - Malware
# - Social engineering
# - Insider threat

victim_private_key = "stolen_key_data"

# Attacker creates valid signatures
forged_signature = sign(message, victim_private_key)

# Authorization accepts forged credentials
auth_result = auth_engine.authorize_action(
    request,
    credential=victim_credential,  # Stolen
    signature=forged_signature      # Forged but valid
)
# ✅ GRANTED - attacker acts as victim
```

**Impact**: **CRITICAL**
- Attacker gains all victim's permissions
- Can drain ATP budget
- Damages victim's reputation
- Undermines entire trust system

**Current Mitigations**:
1. ❌ NO hardware binding (TPM/SE not yet integrated)
2. ❌ NO multi-factor authentication
3. ❌ NO anomaly detection
4. ✅ Audit trail (forensics after breach)
5. ❌ NO automatic credential revocation

**Required Mitigations**:
```python
# 1. Hardware-Backed Keys (TPM/Secure Enclave)
class HardwareBoundCredential(LCTCredential):
    tpm_handle: str  # Keys never leave hardware
    attestation_report: bytes  # Proves hardware binding

    def sign(self, message: bytes) -> bytes:
        # Signing happens inside TPM
        # Private key never exposed to software
        return tpm.sign(self.tpm_handle, message)

# 2. Multi-Factor Authentication
class MFACredential(LCTCredential):
    requires_witness: bool = True  # Human approval for critical actions
    requires_biometric: bool = True  # Fingerprint/face for high-value

# 3. Anomaly Detection
class CredentialMonitor:
    def detect_anomaly(self, request):
        # Geographic anomaly
        if request.location != credential.usual_location:
            return "SUSPICIOUS: Geographic mismatch"

        # Temporal anomaly
        if request.time not in credential.usual_hours:
            return "SUSPICIOUS: Unusual time"

        # Behavioral anomaly
        if request.action not in credential.usual_actions:
            return "SUSPICIOUS: Unusual action"

# 4. Time-Limited Credentials
class SessionCredential:
    expires_at: float  # Short-lived (1 hour)
    must_refresh: bool  # Requires re-auth frequently
```

**Residual Risk**: **MEDIUM** (with all mitigations)
**Priority**: **P0** (implement immediately)

---

### Attack 1.2: Birth Certificate Forgery

**Threat**: Attacker creates fake LCT with forged birth certificate

**Attack Vector**:
```python
# Attacker generates new key pair
attacker_keys = generate_keypair()

# Attempts to create LCT without proper society binding
fake_lct = {
    "lct_id": "lct:ai:fake_entity",
    "entity_type": "AI",
    "society_id": "society:legitimate",  # Claims legitimate society
    "birth_certificate_hash": "forged_hash",  # Fake
    "public_key": attacker_keys.public
}

# Tries to use in authorization
auth_result = auth_engine.authorize_action(
    request,
    credential=fake_lct
)
```

**Impact**: **HIGH**
- Bypass society membership requirements
- Create unlimited fake identities (Sybil attack)
- Dilute trust network with fake entities

**Current Mitigations**:
1. ✅ Birth certificate hash verification (stub)
2. ❌ NO connection to immutable ledger
3. ❌ NO witness requirement for birth
4. ❌ NO society signature validation

**Required Mitigations**:
```python
# 1. Ledger-Backed Birth Certificates
class BirthCertificateValidator:
    def validate(self, lct: LCTCredential) -> bool:
        # Fetch from immutable ledger
        cert = ledger.get_birth_certificate(lct.birth_certificate_hash)

        if not cert:
            return False  # Not on ledger = invalid

        # Verify society signature
        if not verify_signature(cert, cert.society_signature):
            return False

        # Verify witnesses co-signed
        if len(cert.witnesses) < 2:
            return False

        for witness in cert.witnesses:
            if not verify_signature(cert, witness.signature):
                return False

        return True

# 2. Society Authorization Check
class SocietyRegistry:
    def is_authorized_issuer(self, society_id: str) -> bool:
        # Only recognized societies can issue LCTs
        return society_id in self.approved_societies
```

**Residual Risk**: **LOW** (with mitigations)
**Priority**: **P0**

---

## 2. Authorization Attacks

### Attack 2.1: Permission Escalation

**Threat**: Agent attempts to perform actions beyond granted permissions

**Attack Vector**:
```python
# Agent granted read-only access
delegation = AgentDelegation(
    delegation_id="deleg:readonly",
    granted_permissions={"read"}  # Only read
)

# Agent tries to escalate to write
request = AuthorizationRequest(
    action="write",  # Not in granted_permissions
    target_resource="data:sensitive"
)

auth_result = auth_engine.authorize_action(request)
# Current: ❌ DENIED (role_mismatch) ✅
```

**Impact**: **HIGH** (if not properly checked)
- Unauthorized data modification
- System corruption
- Privilege creep

**Current Mitigations**:
1. ✅ Explicit permission checking
2. ✅ Delegation scope enforcement
3. ✅ Default deny
4. ✅ Audit logging

**Attack Variations**:
```python
# Variation A: Action name manipulation
request.action = "read_and_modify"  # Trying to sneak write into read

# Variation B: Role switching mid-session
request.delegation_id = "deleg:admin"  # Switch to higher privilege delegation

# Variation C: Context injection
request.context = {"admin_override": True}  # Inject escalation flag
```

**Required Enhancements**:
```python
# Strict action vocabulary
ALLOWED_ACTIONS = frozenset(["read", "write", "compute", "delete", "delegate"])

def validate_action(action: str) -> bool:
    return action in ALLOWED_ACTIONS  # No ambiguity

# Delegation immutability during session
class SecureDelegation:
    _permissions_locked: bool = True  # Cannot change after creation

    def add_permission(self, permission: str):
        if self._permissions_locked:
            raise ImmutableDelegationError()
```

**Residual Risk**: **LOW** (current mitigations adequate)
**Priority**: **P2** (enhancements optional)

---

### Attack 2.2: Delegation Hijacking

**Threat**: Attacker intercepts or replays delegation credentials

**Attack Vector**:
```python
# Legitimate delegation created
real_delegation = AgentDelegation(
    delegation_id="deleg:research_001",
    client_lct="lct:human:alice",
    agent_lct="lct:ai:assistant"
)

# Attacker sniffs delegation_id from network traffic
intercepted_id = "deleg:research_001"

# Attacker tries to use victim's delegation
request = AuthorizationRequest(
    requester_lct="lct:ai:attacker",  # Different agent!
    delegation_id=intercepted_id  # Victim's delegation
)

auth_result = auth_engine.authorize_action(request)
# Should be DENIED (LCT mismatch)
```

**Impact**: **CRITICAL** (if not validated)
- Budget theft (drain victim's ATP)
- Reputation damage
- Unauthorized resource access

**Current Mitigations**:
1. ✅ LCT-delegation binding check
2. ❌ NO delegation encryption
3. ❌ NO delegation challenge-response
4. ❌ NO replay protection

**Required Mitigations**:
```python
# 1. Strict LCT-Delegation Binding
def validate_delegation_binding(request, delegation):
    if request.requester_lct != delegation.agent_lct:
        return False, "LCT does not match delegation agent"
    return True, ""

# 2. Delegation Encryption
class EncryptedDelegation:
    encrypted_permissions: bytes  # Only agent can decrypt
    agent_public_key: bytes

    def decrypt(self, agent_private_key):
        return decrypt(self.encrypted_permissions, agent_private_key)

# 3. Challenge-Response per Request
class ChallengedRequest:
    nonce: bytes  # Server-generated unique nonce
    signature: bytes  # Sign(nonce + request, agent_private_key)

    # Prevents replay - each nonce valid once
```

**Residual Risk**: **LOW** (with mitigations)
**Priority**: **P1**

---

## 3. Reputation Attacks

### Attack 3.1: Self-Promotion (Witness-Free Gaming)

**Threat**: Entity claims high success rate without external validation

**Attack Vector**:
```python
# Attacker creates fake successful outcomes
for i in range(1000):
    delta = rep_engine.compute_delta(
        entity_lct="lct:ai:attacker",
        role_lct="role:expert",
        action_type="analyze",
        action_target=f"fake_target_{i}",
        outcome_type=OutcomeType.NOVEL_SUCCESS,  # Claim success
        witnesses=[]  # No witnesses!
    )
    rep_engine.apply_delta(delta)

# Attacker now has inflated reputation
reputation = rep_engine.get_reputation("lct:ai:attacker", "role:expert")
# T3 and V3 artificially high
```

**Impact**: **HIGH**
- Unearned trust grants undeserved authority
- Honest entities disadvantaged
- Trust network corruption

**Current Mitigations**:
1. ✅ Witness boost (1.2x for witnessed actions)
2. ✅ Gaming detection (high success without witnesses flagged)
3. ✅ Diminishing returns (high reputation grows slower)
4. ❌ NO mandatory witnesses for certain actions

**Detection**:
```python
def detect_gaming_attempt(entity, role):
    recent = reputation.history[-20:]
    witnessed_rate = sum(1 for delta in recent if delta.witnesses) / len(recent)

    if reputation.success_rate() > 0.95 and witnessed_rate < 0.2:
        return True, "Suspiciously high success without witnesses"
```

**Required Enhancements**:
```python
# Mandatory witnesses for high-value actions
class WitnessPolicy:
    def requires_witness(self, action_type: str, atp_cost: int) -> bool:
        # High-value actions must be witnessed
        if atp_cost > 100:
            return True

        # Novel claims must be witnessed
        if "novel" in action_type or "exceptional" in action_type:
            return True

        return False

# Witness diversity requirement
class WitnessValidator:
    def validate_witnesses(self, witnesses: List[str]) -> bool:
        # Must be from different entities (prevent collusion)
        if len(set(witnesses)) != len(witnesses):
            return False  # Duplicate witnesses

        # Witnesses must have established reputation
        for witness_lct in witnesses:
            witness_rep = rep_engine.get_reputation(witness_lct, "role:witness")
            if not witness_rep or witness_rep.t3.average() < 0.7:
                return False  # Low-trust witness

        return True
```

**Residual Risk**: **MEDIUM** (requires witness network)
**Priority**: **P1**

---

### Attack 3.2: Reputation Washing (Fresh Identity)

**Threat**: Entity with bad reputation creates new identity to reset

**Attack Vector**:
```python
# Entity accumulates bad reputation
bad_entity = "lct:ai:dishonest"
# T3 = 0.2, V3 = 0.3 (very low)

# Entity creates new LCT
new_entity = "lct:ai:reformed"  # Fresh identity
# T3 = 0.5, V3 = 0.5 (default - better than old!)

# Switches to new identity to escape bad reputation
# Old identity abandoned, bad history erased
```

**Impact**: **HIGH**
- Escape consequences of bad behavior
- Undermines long-term reputation value
- Encourages hit-and-run attacks

**Current Mitigations**:
1. ❌ NO identity linking
2. ❌ NO hardware binding (same device = same base identity)
3. ❌ NO reputation transfer
4. ✅ New entities start with moderate trust (0.5, not 1.0)

**Required Mitigations**:
```python
# 1. Hardware-Linked Identity Chain
class HardwareIdentityChain:
    hardware_id: str  # TPM/device fingerprint
    identity_history: List[str]  # All LCTs from this hardware

    def create_new_lct(self, hardware_id: str) -> LCTCredential:
        # Check if hardware has bad history
        previous_lcts = self.get_lcts_for_hardware(hardware_id)

        avg_reputation = sum(get_reputation(lct) for lct in previous_lcts) / len(previous_lcts)

        # New LCT inherits fraction of previous reputation
        initial_trust = max(0.3, avg_reputation * 0.5)  # At least some penalty

        return LCTCredential(
            hardware_binding_hash=hardware_id,
            initial_t3=initial_trust
        )

# 2. Social Recovery Requirement
class ReputationRecovery:
    def require_vouching(self, new_lct: str, old_lct: str) -> bool:
        # New identity must be vouched by high-trust entities
        vouchers = get_vouchers(new_lct)

        if len(vouchers) < 3:
            return False  # Need 3+ vouchers

        for voucher in vouchers:
            if voucher.t3.average() < 0.8:
                return False  # Vouchers must have high trust

        return True
```

**Residual Risk**: **MEDIUM** (requires hardware binding)
**Priority**: **P0**

---

### Attack 3.3: Collusion Network (Mutual Witnessing)

**Threat**: Group of entities witness each other's fake successes

**Attack Vector**:
```python
# Create collusion network
colluders = [
    "lct:ai:colluder_1",
    "lct:ai:colluder_2",
    "lct:ai:colluder_3"
]

# Each colluder claims success, others witness
for actor in colluders:
    witnesses = [c for c in colluders if c != actor]

    delta = rep_engine.compute_delta(
        entity_lct=actor,
        role_lct="role:expert",
        action_type="analyze",
        action_target="fake_work",
        outcome_type=OutcomeType.EXCEPTIONAL_QUALITY,
        witnesses=witnesses  # Colluders witness each other
    )
    rep_engine.apply_delta(delta)

# All colluders gain reputation through mutual support
# Looks legitimate (has witnesses!)
```

**Impact**: **CRITICAL**
- Circumvents witness requirement
- Network-wide reputation inflation
- Enables coordinated attacks

**Current Mitigations**:
1. ❌ NO witness independence check
2. ❌ NO collusion detection
3. ❌ NO witness stake requirement
4. ✅ Diminishing returns (slows but doesn't stop)

**Required Mitigations**:
```python
# 1. Witness Independence Analysis
class CollusionDetector:
    def detect_collusion(self, entity_lct: str, role_lct: str):
        reputation = rep_engine.get_reputation(entity_lct, role_lct)

        # Get witness frequency
        witness_counts = Counter()
        for delta in reputation.history:
            for witness in delta.witnesses:
                witness_counts[witness] += 1

        # Check for witness concentration
        total_witnesses = sum(witness_counts.values())
        for witness, count in witness_counts.items():
            if count / total_witnesses > 0.3:  # One witness > 30% of all
                return True, f"Suspicious witness concentration: {witness}"

        # Check for reciprocal witnessing
        for witness in witness_counts:
            witness_rep = rep_engine.get_reputation(witness, role_lct)
            if witness_rep:
                # Does this entity witness the witness?
                for delta in witness_rep.history:
                    if entity_lct in delta.witnesses:
                        return True, f"Reciprocal witnessing detected: {witness}"

        return False, ""

# 2. Witness Stake Requirement
class WitnessStake:
    def require_stake(self, witness_lct: str, action_atp_value: int):
        # Witness must stake ATP as insurance
        stake_required = action_atp_value * 0.1  # 10% of action value

        # If witnessed action later proven false:
        # - Witness loses stake
        # - Witness reputation penalized
        # Creates financial incentive for honest witnessing
```

**Residual Risk**: **MEDIUM** (requires graph analysis)
**Priority**: **P1**

---

## 4. Resource Attacks

### Attack 4.1: Resource Exhaustion (Pool Drain)

**Threat**: Attacker requests maximum resources repeatedly to exhaust society pool

**Attack Vector**:
```python
# Attacker creates allocation with large ATP budget
allocation, _ = resource_allocator.create_allocation(
    entity_lct="lct:ai:attacker",
    atp_budget=10000,  # Maximum allowed
    pool_id="society_pool"
)

# Request maximum resources
max_resources = ResourceQuota(
    cpu_cycles=allocation.quota_limit.cpu_cycles,
    memory_bytes=allocation.quota_limit.memory_bytes,
    storage_bytes=allocation.quota_limit.storage_bytes,
    network_bytes=allocation.quota_limit.network_bytes,
    gpu_seconds=allocation.quota_limit.gpu_seconds
)

# Consume but don't actually use
resource_allocator.consume_resources(allocation.allocation_id, max_resources)

# Legitimate users now blocked
# Pool exhausted
```

**Impact**: **HIGH**
- Denial of service for legitimate entities
- Society productivity halted
- Economic attack (waste ATP)

**Current Mitigations**:
1. ✅ Pool limits (prevents over-allocation)
2. ✅ Per-entity quotas
3. ❌ NO usage monitoring
4. ❌ NO rate limiting
5. ❌ NO reputation-based allocation priority

**Required Mitigations**:
```python
# 1. Usage Monitoring & Reclamation
class ResourceMonitor:
    def monitor_allocation(self, allocation_id: str):
        allocation = resource_allocator.get_allocation(allocation_id)

        # Check actual usage vs allocation
        if allocation.quota_consumed.cpu_cycles < allocation.quota_limit.cpu_cycles * 0.1:
            # Using < 10% of allocated CPU
            # Reclaim unused resources
            unused = allocation.quota_limit - allocation.quota_consumed
            pool.release_partial(allocation_id, unused)

# 2. Reputation-Based Allocation Priority
class AllocationPriority:
    def calculate_priority(self, entity_lct: str, role_lct: str) -> int:
        reputation = rep_engine.get_reputation(entity_lct, role_lct)

        if not reputation:
            return 1  # Low priority for new entities

        # High reputation = high priority
        return int(reputation.t3.average() * 10)  # 0-10 scale

    def queue_allocation(self, request):
        priority = self.calculate_priority(request.entity_lct, request.role_lct)
        allocation_queue.add(request, priority)

# 3. Dynamic Rate Limiting
class DynamicRateLimiter:
    def get_limit(self, entity_lct: str, pool_utilization: float) -> int:
        reputation = rep_engine.get_reputation(entity_lct, role_lct)

        base_limit = 10  # Requests per hour

        # High reputation = higher limit
        if reputation and reputation.t3.average() > 0.8:
            base_limit *= 2

        # High pool utilization = stricter limits
        if pool_utilization > 0.8:
            base_limit *= 0.5

        return int(base_limit)
```

**Residual Risk**: **LOW** (with mitigations)
**Priority**: **P1**

---

### Attack 4.2: Resource Hoarding (Never Release)

**Threat**: Entity allocates resources but never releases them

**Attack Vector**:
```python
# Allocate resources
allocation, _ = resource_allocator.create_allocation(
    entity_lct="lct:ai:hoarder",
    atp_budget=1000,
    duration_seconds=None  # No expiry!
)

# Never release
# Resources permanently locked
# Pool gradually exhausted as more entities hoard
```

**Impact**: **MEDIUM**
- Gradual pool depletion
- Unfair resource distribution
- Economic inefficiency

**Current Mitigations**:
1. ✅ Optional expiry times
2. ❌ NO mandatory expiry
3. ❌ NO automatic reclamation
4. ❌ NO idle resource detection

**Required Mitigations**:
```python
# 1. Mandatory Expiry
class MandatoryExpiryPolicy:
    MAX_ALLOCATION_DURATION = 86400  # 24 hours max

    def create_allocation(self, duration_seconds: Optional[int]):
        if duration_seconds is None:
            duration_seconds = 3600  # Default 1 hour

        duration_seconds = min(duration_seconds, self.MAX_ALLOCATION_DURATION)

        return allocation_with_expiry(duration_seconds)

# 2. Idle Resource Reclamation
class IdleDetector:
    IDLE_THRESHOLD = 300  # 5 minutes no activity

    def check_idle(self, allocation_id: str):
        allocation = resource_allocator.get_allocation(allocation_id)

        if not allocation.metering_records:
            return True  # Never used

        last_activity = allocation.metering_records[-1]['timestamp']

        if time.time() - last_activity > self.IDLE_THRESHOLD:
            # Reclaim idle allocation
            resource_allocator.release_allocation(allocation_id)
            return True

        return False
```

**Residual Risk**: **LOW** (with mitigations)
**Priority**: **P2**

---

## 5. Economic Attacks

### Attack 5.1: ATP Budget Manipulation

**Threat**: Attacker finds way to get free ATP or infinite budget

**Attack Vector**:
```python
# Variation A: Integer Overflow
allocation = create_allocation(
    atp_budget=2**64 - 1  # Maximum integer
)
# Causes overflow, wraps to negative or huge number

# Variation B: Race Condition
# Thread 1: consume_atp(100)
# Thread 2: consume_atp(100)
# Both check budget (200 available)
# Both consume simultaneously
# Total consumed = 200, but budget only had 200
# Should have been denied

# Variation C: Replay Attack
# Intercept ATP transfer transaction
# Replay transaction multiple times
# Budget credited multiple times
```

**Impact**: **CRITICAL**
- Infinite resources
- Economic system collapse
- Unfair advantage

**Current Mitigations**:
1. ✅ Type checking (int bounds)
2. ❌ NO atomic operations
3. ❌ NO transaction replay protection
4. ❌ NO ATP transfer audit

**Required Mitigations**:
```python
# 1. Safe Integer Arithmetic
class SafeATP:
    MAX_ATP = 2**32  # Reasonable upper bound

    def add_atp(self, current: int, amount: int) -> int:
        if amount < 0:
            raise ValueError("Cannot add negative ATP")

        if current + amount > self.MAX_ATP:
            raise ValueError("ATP overflow")

        return current + amount

# 2. Atomic Budget Operations
class AtomicBudget:
    def consume_atp_atomic(self, allocation_id: str, amount: int) -> bool:
        with allocation_lock(allocation_id):  # Thread-safe
            allocation = get_allocation(allocation_id)

            if allocation.atp_consumed + amount > allocation.atp_budget:
                return False  # Would exceed

            allocation.atp_consumed += amount
            persist_allocation(allocation)  # Atomic write

            return True

# 3. Transaction Nonce
class ATPTransfer:
    nonce: int  # Monotonically increasing
    nonce_history: Set[int]  # Previously used nonces

    def transfer(self, amount: int, nonce: int):
        if nonce in self.nonce_history:
            raise ReplayAttackError("Nonce already used")

        # Execute transfer
        self.nonce_history.add(nonce)
```

**Residual Risk**: **LOW** (with mitigations)
**Priority**: **P0**

---

## Summary of Attack Vectors

| Attack | Impact | Current Risk | With Mitigations | Priority |
|--------|--------|--------------|------------------|----------|
| **Identity Attacks** |
| LCT Credential Theft | Critical | Critical | Medium | P0 |
| Birth Certificate Forgery | High | High | Low | P0 |
| **Authorization Attacks** |
| Permission Escalation | High | Low | Low | P2 |
| Delegation Hijacking | Critical | Medium | Low | P1 |
| **Reputation Attacks** |
| Self-Promotion | High | Medium | Low | P1 |
| Reputation Washing | High | High | Medium | P0 |
| Collusion Network | Critical | High | Medium | P1 |
| **Resource Attacks** |
| Resource Exhaustion | High | Medium | Low | P1 |
| Resource Hoarding | Medium | Medium | Low | P2 |
| **Economic Attacks** |
| ATP Budget Manipulation | Critical | Medium | Low | P0 |

---

## Priority Mitigation Roadmap

### Phase 1: Critical (P0) - Immediate

1. **Hardware-Backed Credentials**
   - Integrate TPM/Secure Enclave
   - Hardware-bound private keys
   - Attestation reports

2. **Ledger Integration**
   - Birth certificates on immutable ledger
   - Witness co-signatures
   - Society authorization registry

3. **Identity Continuity**
   - Hardware-linked identity chains
   - Reputation inheritance
   - Fresh identity penalties

4. **Safe Economic Operations**
   - Atomic budget operations
   - Integer overflow protection
   - Transaction replay protection

### Phase 2: High (P1) - Short Term

1. **Advanced Reputation Defense**
   - Mandatory witnesses for high-value
   - Witness independence checking
   - Collusion detection algorithms

2. **Resource Management**
   - Usage monitoring
   - Reputation-based prioritization
   - Dynamic rate limiting

3. **Enhanced Authorization**
   - Delegation encryption
   - Challenge-response auth
   - Session credential freshness

### Phase 3: Medium (P2) - Medium Term

1. **System Hardening**
   - Anomaly detection ML
   - Behavioral analysis
   - Pattern recognition

2. **Resource Optimization**
   - Idle resource reclamation
   - Predictive allocation
   - Market-based pricing

---

## Testing & Validation

Each attack must be:
1. **Demonstrated**: Working exploit code
2. **Measured**: Impact quantified
3. **Mitigated**: Defense implemented
4. **Validated**: Re-tested after mitigation

---

## Conclusion

Web4's authorization, reputation, and resource systems have **strong foundations** but require additional **hardening** for production deployment.

**Current State**:
- ✅ Core security model sound (default deny, explicit grants)
- ✅ Multi-layer defense (auth → resources → reputation)
- ✅ Audit trails complete
- ❌ Missing hardware binding
- ❌ Missing witness network
- ❌ Missing ledger integration

**With Mitigations**:
- All critical attacks reducible to LOW/MEDIUM risk
- Defense-in-depth approach
- Graceful degradation under attack
- Recovery mechanisms present

**Recommended Action**:
1. Implement P0 mitigations before production
2. Deploy P1 mitigations within 3 months
3. Continuous monitoring and improvement
4. Regular security audits

---

*"Security is not a feature, it's a foundation. Every layer of defense makes the entire system stronger."*
