# Attack Vector Analysis: Energy-Backed ATP System

**Session #38** - 2025-11-16
**Purpose:** Comprehensive security analysis of Sessions #30-37 implementations
**Status:** Attack discovery and mitigation testing

---

## Overview

The energy-backed ATP system (Sessions #30-37) introduces several innovations:
- Energy capacity proofs (Session #36)
- ATP expiration (Session #36)
- Trust-based priority queues (Session #36)
- Energy-backed identity bonds (Session #36)
- Gaming resistance (Session #34)
- Web of trust (Session #35)

Each innovation creates new attack surfaces. This document catalogs potential attacks and tests defenses.

---

## Attack Vector Categories

### Category A: Energy Proof Manipulation
### Category B: ATP Hoarding & Gaming
### Category C: Priority Queue Exploitation
### Category D: Identity Bond Circumvention
### Category E: Reputation Washing
### Category F: Collusion & Coordination
### Category G: Resource Exhaustion

---

## Category A: Energy Proof Manipulation

### A1: Fake Energy Capacity Proof

**Attack:**
Attacker claims to have energy capacity they don't possess.

**Example:**
```python
# Attacker submits fake solar panel proof
fake_proof = SolarPanelProof(
    panel_serial="FAKE-123",
    rated_watts=10000.0,  # Claims 10kW but has nothing
    panel_model="Imaginary Panel",
    installation_date=datetime.now(timezone.utc),
    last_verified=datetime.now(timezone.utc),
)
```

**Impact:**
- Can charge ATP without real energy backing
- Undermines thermodynamic constraint
- Can create unlimited ATP

**Mitigations (Current):**
1. ✅ `EnergyCapacityValidator` checks proof structure
2. ✅ Proofs have expiration (30 days for solar)
3. ❌ NO external validation (solar API, hardware query)

**Mitigation Gaps:**
- Validators can't detect fictional serial numbers
- No cross-reference with manufacturer databases
- No oracle integration

**Enhanced Mitigation (Proposed):**
```python
class ExternalEnergyValidator:
    def validate_solar_panel(self, proof: SolarPanelProof) -> bool:
        # 1. Query solar panel manufacturer API
        # 2. Verify serial number exists
        # 3. Check production history matches claimed wattage
        # 4. Require 3rd party installer certification
        pass

    def validate_compute(self, proof: ComputeResourceProof) -> bool:
        # 1. Query hardware via CUDA/ROCm
        # 2. Read actual TDP from device
        # 3. Verify device ID against blockchain registry
        pass
```

**Test Required:** ✅ Implement in `test_attack_vectors.py`

---

### A2: Proof Reuse Attack

**Attack:**
Register same energy source for multiple societies.

**Example:**
```python
# Society A registers solar panel
registry_a.register_source(solar_proof)

# Society B also registers SAME solar panel
registry_b.register_source(solar_proof)  # Should fail!

# Both societies can now charge ATP from same 300W panel
# Total claimed: 600W (but only 300W exists)
```

**Impact:**
- Double-spending of energy capacity
- Can create 2x ATP from same physical energy
- Breaks thermodynamic conservation

**Mitigations (Current):**
1. ❌ NO global registry of energy sources
2. ❌ NO uniqueness check across societies

**Mitigation Gaps:**
- Each society has independent registry
- Same source_identifier can be used multiple times
- No blockchain-level uniqueness constraint

**Enhanced Mitigation (Proposed):**
```python
class GlobalEnergyRegistry:
    """Blockchain-level energy source registry"""
    sources: Dict[str, str]  # source_identifier -> society_lct

    def register_source(self, proof: EnergyCapacityProof, society_lct: str) -> bool:
        if proof.source_identifier in self.sources:
            existing_owner = self.sources[proof.source_identifier]
            if existing_owner != society_lct:
                raise ValueError(f"Energy source already registered to {existing_owner}")

        self.sources[proof.source_identifier] = society_lct
        return True
```

**Test Required:** ✅ Implement test case

---

### A3: Capacity Inflation Attack

**Attack:**
Claim higher capacity than actually exists.

**Example:**
```python
# Real GPU: RTX 4090, 450W TDP
# Attacker claims:
proof = ComputeResourceProof(
    device_model="RTX 4090",
    tdp_watts=900.0,  # Claims 2x actual TDP!
    utilization_factor=1.0,
)
```

**Impact:**
- Can charge 2x ATP from same hardware
- Dilutes energy backing ratio
- Can sustain more work than energy allows

**Mitigations (Current):**
1. ✅ Proof structure validation
2. ❌ NO verification against known TDP specs

**Mitigation Gaps:**
- No database of device TDP specs
- No hardware query to verify actual power draw
- Relies on honor system

**Enhanced Mitigation (Proposed):**
```python
class DeviceSpecDatabase:
    """Known TDP specifications"""
    specs = {
        "RTX4090": {"tdp_watts": 450.0, "idle_watts": 50.0},
        "Jetson Orin NX": {"tdp_watts": 15.0, "idle_watts": 5.0},
        # ...
    }

    def validate_tdp(self, model: str, claimed_tdp: float) -> bool:
        if model not in self.specs:
            return False  # Unknown device

        spec = self.specs[model]
        # Allow 10% tolerance for manufacturing variance
        max_tdp = spec["tdp_watts"] * 1.1
        return claimed_tdp <= max_tdp
```

**Test Required:** ✅ Implement test case

---

## Category B: ATP Hoarding & Gaming

### B1: Expiration Circumvention

**Attack:**
Continuously recharge ATP just before expiration to hoard indefinitely.

**Example:**
```python
# Day 0: Charge 1000 ATP (expires day 7)
atp1 = pool.charge_atp(1000.0, solar_id, lifetime_days=7)

# Day 6: Discharge to ADP
adp = pool.complete_work(ticket.id, quality_score=1.0)

# Day 6.1: Immediately recharge
atp2 = pool.charge_atp(1000.0, solar_id, lifetime_days=7)

# Result: Perpetual ATP without expiration pressure
```

**Impact:**
- Defeats ATP expiration mechanism
- Can hoard energy indefinitely
- No urgency to use ATP efficiently

**Mitigations (Current):**
1. ✅ ATP expires after 7 days
2. ❌ NO rate limiting on recharging
3. ❌ NO minimum usage requirement

**Mitigation Gaps:**
- Can cycle ATP → ADP → ATP indefinitely
- No cost to recharging
- No penalty for unused ATP

**Enhanced Mitigation (Proposed):**
```python
class ExpirationEnforcement:
    def charge_atp(self, amount, source_id, lifetime_days=7):
        # 1. Check recent discharge history
        recent_discharges = self.get_recent_discharges(hours=24)

        # 2. If >80% of recent ATP was just cycled (not used for work):
        if self.is_cycling_detected(recent_discharges, amount):
            # Reduce lifetime or charge efficiency penalty
            actual_lifetime = max(1, lifetime_days // 2)
            efficiency_penalty = 0.9
            amount = amount * efficiency_penalty

        return self.create_charged_atp(amount, source_id, actual_lifetime)
```

**Test Required:** ✅ Implement test case

---

### B2: Minimum Work Gaming

**Attack:**
Allocate ATP to trivial work to avoid expiration without providing value.

**Example:**
```python
# ATP about to expire in 1 hour
# Quickly allocate to fake work
ticket = pool.allocate_atp_to_work(
    worker_lct="lct-self",
    description="Do nothing",  # Trivial task
    atp_amount=1000.0,
)

# "Complete" work immediately
adp = pool.complete_work(ticket.id, quality_score=1.0)

# Result: Avoided expiration, generated ADP, no real work done
```

**Impact:**
- Can generate ADP without real work
- Gaming the reputation system
- Defeats work-based value creation

**Mitigations (Current):**
1. ✅ Quality score affects reputation
2. ❌ NO minimum work duration
3. ❌ NO work validation

**Mitigation Gaps:**
- Self-reported quality scores
- No external validation of work
- Can complete work instantly

**Enhanced Mitigation (Proposed):**
```python
class WorkValidator:
    def complete_work(self, ticket, quality_score):
        # 1. Check work duration
        duration = datetime.now(timezone.utc) - ticket.allocated_at
        min_duration = timedelta(minutes=self.calculate_min_duration(ticket.atp_allocated))

        if duration < min_duration:
            # Suspicious: Completed too quickly
            quality_score *= 0.5  # Penalize quality

        # 2. Require proof of work (varies by work type)
        if not self.validate_work_proof(ticket):
            quality_score *= 0.5

        # 3. Cross-validate with other workers
        if not self.cross_validate(ticket):
            quality_score *= 0.5

        return quality_score
```

**Test Required:** ✅ Implement test case

---

## Category C: Priority Queue Exploitation

### C1: Trust Score Manipulation

**Attack:**
Artificially inflate trust score to get high priority without earning it.

**Example:**
```python
# Create fake vouching network
voucher_a.vouch(newcomer_b, 100W)
voucher_b.vouch(newcomer_a, 100W)  # Circular!

# Both get transitive trust from each other
# Result: High trust without real reputation
```

**Impact:**
- Can jump queue without merit
- Unfair resource allocation
- Defeats trust-based priority

**Mitigations (Current):**
1. ✅ Vouching commits energy (reputation at risk)
2. ✅ Trust combines direct + transitive + bonds
3. ❌ NO circular vouching detection

**Mitigation Gaps:**
- Circular vouching creates fake trust
- Sybil network can vouch for each other
- No graph analysis for collusion

**Enhanced Mitigation (Proposed):**
```python
class CircularVouchingDetector:
    def detect_vouching_cycles(self, trust_graph):
        # 1. Find strongly connected components
        sccs = self.tarjan_scc(trust_graph)

        # 2. Flag SCCs larger than threshold
        suspicious_sccs = [scc for scc in sccs if len(scc) > 3]

        # 3. Reduce trust for circular vouchers
        for scc in suspicious_sccs:
            for node in scc:
                node.trust_score *= 0.5  # Penalty

        return suspicious_sccs
```

**Test Required:** ✅ Implement test case

---

### C2: Priority Spam Attack

**Attack:**
Submit many requests to clog queue, even if deferred.

**Example:**
```python
# Low-trust attacker submits 10,000 requests
for i in range(10000):
    priority.submit_work_request(
        "lct-low-trust",
        f"Request {i}",
        10.0,  # Small ATP amounts
    )

# Result: Queue has 10,000 items, processing slowed
```

**Impact:**
- Degrades system performance
- High-trust entities must wait for queue processing
- DoS via queue flooding

**Mitigations (Current):**
1. ✅ Low-trust gets DEFERRED priority
2. ❌ NO rate limiting per LCT
3. ❌ NO queue size limits

**Mitigation Gaps:**
- Can submit unlimited requests
- No cost to submitting (except reputation)
- Queue can grow unbounded

**Enhanced Mitigation (Proposed):**
```python
class RateLimiter:
    def submit_work_request(self, requester_lct, ...):
        # 1. Check recent submission count
        recent_submissions = self.get_submissions_last_hour(requester_lct)

        # 2. Calculate rate limit based on trust
        trust_score = self.get_trust_score(requester_lct)
        max_submissions = int(10 * trust_score)  # 0-10 per hour

        if len(recent_submissions) >= max_submissions:
            raise ValueError(f"Rate limit exceeded: {max_submissions}/hour")

        # 3. Require ATP stake for low-trust submitters
        if trust_score < 0.5:
            self.stake_atp_for_request(requester_lct, request)

        return self.add_to_queue(request)
```

**Test Required:** ✅ Implement test case

---

### C3: Priority Escalation via False Criticality

**Attack:**
Mark all requests as "critical" to bypass trust-based priority.

**Example:**
```python
# Low-trust attacker marks request as critical
request = priority.submit_work_request(
    "lct-low-trust",
    "Critical work",
    100.0,
    critical=True,  # Bypass trust check!
)

# Result: Gets CRITICAL priority despite low trust
```

**Impact:**
- Can bypass entire priority system
- Defeats trust-based differentiation
- Unfair queue jumping

**Mitigations (Current):**
1. ✅ Critical flag exists
2. ❌ NO authorization check for critical flag
3. ❌ NO audit of critical usage

**Mitigation Gaps:**
- Anyone can mark request as critical
- No governance or role check
- No penalty for false criticality

**Enhanced Mitigation (Proposed):**
```python
class CriticalRequestValidator:
    authorized_critical_roles = ["emergency_responder", "system_admin"]

    def submit_work_request(self, requester_lct, critical=False):
        if critical:
            # 1. Check if requester has critical authorization role
            if not self.has_critical_role(requester_lct):
                # 2. Require multi-sig approval or governance vote
                if not self.get_critical_approval(requester_lct):
                    critical = False  # Downgrade
                    self.penalize_false_critical(requester_lct)

        return self.create_request(requester_lct, critical)
```

**Test Required:** ✅ Implement test case

---

## Category D: Identity Bond Circumvention

### D1: Temporary Capacity Commitment

**Attack:**
Register energy capacity for bond, then remove it after lock period.

**Example:**
```python
# Day 0: Register bond with solar panel
bond = registry.register_bond(
    "lct-attacker",
    energy_sources=[solar_proof],
    lock_period_days=30,
)

# Day 0-30: Capacity validated successfully

# Day 31: Lock expired, remove solar panel
registry.unregister_source(solar_proof.source_identifier)

# Day 31: Reclaim bond with good standing
bond.fulfill_bond(trust_score=0.8)  # Success!

# Result: Got 30 days of identity without long-term commitment
```

**Impact:**
- Can bypass Sybil resistance temporarily
- No long-term skin in game
- Can cycle through identities

**Mitigations (Current):**
1. ✅ 30-day lock period
2. ✅ Validates capacity during lock
3. ❌ NO requirement to maintain after lock

**Mitigation Gaps:**
- Only need capacity for 30 days
- Can remove capacity once lock expires
- No ongoing commitment

**Enhanced Mitigation (Proposed):**
```python
class OngoingCapacityRequirement:
    def fulfill_bond(self, bond, trust_score):
        # 1. Check lock expired
        if not bond.is_lock_expired():
            return (False, "Lock not expired")

        # 2. Require ONGOING capacity for high-trust status
        if trust_score >= 0.8:
            # Must maintain capacity to keep high trust
            is_valid, reason = bond.validate_capacity(self.energy_registry)
            if not is_valid:
                # Can fulfill bond, but trust score capped
                trust_score = min(trust_score, 0.5)

        bond.status = BondStatus.FULFILLED
        return (True, f"Bond fulfilled, adjusted trust: {trust_score}")
```

**Test Required:** ✅ Implement test case

---

### D2: Borrowed Capacity Attack

**Attack:**
Borrow energy capacity temporarily to create bond, then return it.

**Example:**
```python
# Rent a GPU for 1 month from cloud provider
gpu_rental = rent_gpu("RTX4090", duration_days=30)

# Create proof with rented GPU
proof = ComputeResourceProof(
    device_id=gpu_rental.device_id,
    tdp_watts=450.0,
)

# Register bond
bond = registry.register_bond("lct-attacker", [proof], 30)

# Day 30: Return GPU to cloud provider
# Day 31: Bond fulfilled

# Result: Created identity with borrowed, not owned, capacity
```

**Impact:**
- Can create bonds without owning energy
- Rental market for capacity bonds
- Defeats ownership requirement

**Mitigations (Current):**
1. ✅ Must prove capacity exists
2. ❌ NO ownership verification
3. ❌ NO long-term commitment

**Mitigation Gaps:**
- Can use rented/borrowed capacity
- No proof of ownership
- Economic cost only (rental fee)

**Enhanced Mitigation (Proposed):**
```python
class OwnershipVerification:
    def register_bond(self, society_lct, energy_sources):
        for source in energy_sources:
            # 1. Check ownership proof (deed, purchase receipt, etc.)
            if not self.verify_ownership(society_lct, source):
                # 2. Allow rental but require larger capacity
                required_capacity = source.capacity_watts * 2  # Double requirement

            # 3. Require multi-year commitment for rented capacity
            if self.is_rental(source):
                min_lock_period = 365  # 1 year for rentals

        return self.create_bond(society_lct, energy_sources)
```

**Test Required:** ✅ Implement test case

---

## Category E: Reputation Washing

### E1: Identity Abandonment

**Attack:**
Violate bond, forfeit reputation, create new identity.

**Example:**
```python
# Identity A with low reputation (0.2) from violations
bond_a.handle_violation("Capacity dropped")  # reputation -= 0.5 → -0.3

# Abandon identity A, create identity B
bond_b = registry.register_bond("lct-new-identity", [new_solar], 30)

# Start fresh with clean reputation (0.5 default for newcomers)
# Result: Escaped bad reputation
```

**Impact:**
- Can reset reputation by switching identities
- Defeats reputation accumulation
- No long-term consequences

**Mitigations (Current):**
1. ✅ Reputation forfeiture on violation
2. ✅ Newcomer penalty (Session #34)
3. ❌ NO identity linking

**Mitigation Gaps:**
- Can create unlimited new identities
- No connection between old and new identity
- Only cost is energy capacity (which may be same)

**Enhanced Mitigation (Proposed):**
```python
class IdentityLinking:
    def register_bond(self, society_lct, energy_sources):
        # 1. Check if energy sources were used before
        previous_identities = []
        for source in energy_sources:
            prev_id = self.find_previous_owner(source.source_identifier)
            if prev_id:
                previous_identities.append(prev_id)

        # 2. If energy source had violated bond before:
        for prev_id in previous_identities:
            prev_bond = self.get_bond(prev_id)
            if prev_bond.status == BondStatus.VIOLATED:
                # Inherit partial reputation penalty
                new_reputation = 0.5 * (1 - prev_bond.reputation_at_risk)
                return (bond, new_reputation)

        return (bond, 0.5)  # Default newcomer
```

**Test Required:** ✅ Implement test case

---

## Category F: Collusion & Coordination

### F1: Coordinated Priority Gaming

**Attack:**
Multiple low-trust entities submit requests simultaneously to overwhelm queue.

**Example:**
```python
# 100 Sybil identities submit at same time
for i in range(100):
    priority.submit_work_request(
        f"lct-sybil-{i}",
        "Coordinated request",
        10.0,
    )

# Each gets DEFERRED priority individually
# But 100 DEFERRED requests can still clog queue

# Result: Coordinated attack even with low individual trust
```

**Impact:**
- Distributed attack harder to detect
- Can still degrade performance
- Sybil network coordination

**Mitigations (Current):**
1. ✅ Low trust → low priority
2. ❌ NO Sybil detection
3. ❌ NO coordinated attack detection

**Mitigation Gaps:**
- No clustering analysis
- No temporal correlation detection
- Each request evaluated independently

**Enhanced Mitigation (Proposed):**
```python
class CoordinatedAttackDetector:
    def detect_coordinated_requests(self, time_window=60):
        # 1. Get recent requests
        recent = self.get_requests_last_n_seconds(time_window)

        # 2. Cluster by similarity (same description, amount, timing)
        clusters = self.cluster_requests(recent)

        # 3. Flag coordinated clusters
        for cluster in clusters:
            if len(cluster) > 10:  # >10 similar requests
                # Check if requesters are connected (same energy sources, vouching)
                if self.are_connected(cluster.requesters):
                    # Penalize entire cluster
                    for request in cluster:
                        request.priority = RequestPriority.DEFERRED
                        self.penalize_requester(request.requester_lct)
```

**Test Required:** ✅ Implement test case

---

### F2: Vouching Collusion Ring

**Attack:**
Create vouching ring where all members vouch for each other.

**Example:**
```python
# 10 Sybils vouch for each other in a ring
sybils = ["lct-sybil-{i}" for i in range(10)]

for i in range(10):
    voucher = sybils[i]
    newcomer = sybils[(i + 1) % 10]  # Ring topology
    registry.register_vouch(voucher, newcomer, 50.0, 30)

# Each Sybil gets vouched by one other
# Result: All have bootstrap trust without external validation
```

**Impact:**
- Self-reinforcing Sybil network
- Can bootstrap trust internally
- No external vouching needed

**Mitigations (Current):**
1. ✅ Voucher commits energy (reputation at risk)
2. ✅ Newcomer must establish capacity
3. ❌ NO ring detection

**Mitigation Gaps:**
- Circular vouching not detected
- Can create closed vouching networks
- No requirement for external vouchers

**Enhanced Mitigation (Proposed):**
```python
class VouchingRingDetector:
    def detect_vouching_rings(self):
        # 1. Build vouching graph
        graph = self.build_vouching_graph()

        # 2. Find cycles using DFS
        cycles = self.find_cycles(graph)

        # 3. Penalize vouchers in rings
        for cycle in cycles:
            for voucher in cycle:
                # Reduce trust from ring vouching
                voucher.transitive_trust *= 0.5

                # Require external vouch for validation
                external_vouches = self.get_external_vouches(voucher)
                if len(external_vouches) == 0:
                    voucher.status = "suspicious"
```

**Test Required:** ✅ Implement test case

---

## Category G: Resource Exhaustion

### G1: Memory Exhaustion via ATP Hoarding

**Attack:**
Create many small ATP batches to exhaust storage.

**Example:**
```python
# Create 10,000 tiny ATP batches
for i in range(10000):
    pool.charge_atp(
        amount=0.01,  # 0.01 ATP each
        energy_source_identifier=solar.source_identifier,
    )

# Total ATP: 100
# Total storage: 10,000 ChargedATP objects

# Result: Storage bloat, slow queries
```

**Impact:**
- Memory exhaustion
- Slow iteration over ATP batches
- DoS via storage bloat

**Mitigations (Current):**
1. ✅ ATP expires (eventually cleaned)
2. ❌ NO minimum ATP batch size
3. ❌ NO maximum batches per society

**Mitigation Gaps:**
- Can create unlimited small batches
- Storage grows linearly with batches
- Cleanup only happens on expiration

**Enhanced Mitigation (Proposed):**
```python
class BatchLimits:
    MIN_ATP_BATCH = 10.0  # Minimum 10 ATP per batch
    MAX_BATCHES_PER_SOCIETY = 1000

    def charge_atp(self, amount, source_id):
        # 1. Enforce minimum batch size
        if amount < self.MIN_ATP_BATCH:
            raise ValueError(f"Minimum batch size: {self.MIN_ATP_BATCH}")

        # 2. Check batch count
        current_batches = len(self.get_valid_atp_batches())
        if current_batches >= self.MAX_BATCHES_PER_SOCIETY:
            # Merge smallest batches first
            self.merge_small_batches()

        return self.create_charged_atp(amount, source_id)
```

**Test Required:** ✅ Implement test case

---

## Summary of Attack Vectors

### High Severity (Breaks Core Security)
1. **A1: Fake Energy Capacity Proof** - Can create unlimited ATP
2. **A2: Proof Reuse Attack** - Double-spending energy
3. **E1: Identity Abandonment** - Reputation washing

### Medium Severity (Degrades Performance)
4. **C2: Priority Spam Attack** - Queue flooding
5. **F1: Coordinated Priority Gaming** - Distributed DoS
6. **G1: Memory Exhaustion** - Storage bloat

### Low Severity (Edge Cases)
7. **B1: Expiration Circumvention** - Defeats urgency
8. **C1: Trust Score Manipulation** - Circular vouching
9. **D1: Temporary Capacity** - Short-term gaming

---

## Mitigation Priority

### Phase 1: Critical Fixes (Block Breaks)
1. **Global Energy Registry** - Prevent proof reuse
2. **Device Spec Database** - Prevent capacity inflation
3. **Identity Linking** - Prevent reputation washing

### Phase 2: Performance Protection
4. **Rate Limiting** - Prevent spam
5. **Batch Limits** - Prevent storage bloat
6. **Critical Request Auth** - Prevent priority bypass

### Phase 3: Advanced Detection
7. **Coordinated Attack Detection** - ML-based clustering
8. **Circular Vouching Detection** - Graph analysis
9. **External Oracle Integration** - Real validation

---

## Next Steps

1. **Implement attack simulations** in `test_attack_vectors.py`
2. **Test each vulnerability** with concrete examples
3. **Measure impact** quantitatively (ATP generated, storage used, etc.)
4. **Implement mitigations** in order of priority
5. **Re-test** to verify fixes

**Session #38 Goal:** Complete attack testing framework with 10+ test cases.
