# ATP Drain Attack - Complexity Analysis
**Session #63**: Security Research
**Status**: ⚠️ VULNERABLE - Requires External Infrastructure

## Attack Vector Summary

**Attack Pattern** (ATTACK_VECTORS.md line 579-648):
```python
# Victim starts expensive action sequence
victim_sequence = create_sequence(
    actor="lct:victim:001",
    actions=[many_expensive_gpu_operations],
    total_atp=1000
)

# Attacker sabotages operations
# Methods: DoS, resource contention, dependency attacks
sabotage_action(victim_sequence, action_index=5)

# Victim loses ATP when sequence fails
# Attacker gains competitive advantage
```

**Impact**:
- Denial of service via ATP exhaustion
- Resource weaponization
- Competitive sabotage
- Economic griefing

## Why It's Complex

### 1. Failure Attribution Problem

**Core Challenge**: Determining WHO caused a failure

Current system tracks THAT a failure occurred:
```sql
-- action_sequences table
status VARCHAR(50)  -- active, converged, failed, timeout, cancelled
failure_reason TEXT
```

But NOT who/what caused it:
- Network failure? (attacker DoS or legitimate outage?)
- Resource unavailable? (attacker hoarding or natural scarcity?)
- Dependency failure? (attacker sabotage or bug?)
- Timeout? (attacker slowdown or overload?)

**Example Ambiguity**:
```
Sequence failed: "GPU operation timed out after 30s"

Possible causes:
1. Attacker DoS on GPU node (malicious)
2. GPU node legitimately overloaded (benign)
3. Network latency spike (environmental)
4. Victim's operation too complex (self-inflicted)

Which entity should be penalized?
```

### 2. Required Infrastructure

To implement proper failure attribution:

#### A. Distributed Tracing
```python
class FailureTrace:
    sequence_id: str
    failed_at: datetime
    operation: str

    # Attribution data
    involved_entities: List[str]  # All LCTs that touched this operation
    resource_provider: str         # Who provided the resource
    network_path: List[str]        # Network route

    # Evidence
    logs: List[LogEntry]
    metrics: Dict[str, Any]       # CPU, memory, network stats
    timing_analysis: TimingData

    # Determination
    attributed_to: str            # Which entity caused failure
    confidence: float             # 0.0-1.0
    reasoning: str
```

#### B. Resource Monitoring
```python
class ResourceMonitor:
    def track_operation(self, sequence_id, operation):
        # Monitor all entities involved
        return {
            'executor': self.monitor_lct_health(operation.executor),
            'providers': [self.monitor_lct_health(p) for p in operation.providers],
            'network': self.monitor_network_path(operation.route),
            'resources': self.monitor_resource_availability(operation.resources)
        }

    def attribute_failure(self, trace: FailureTrace) -> Attribution:
        # Machine learning / heuristic analysis
        # Determine most likely cause
        pass
```

#### C. Reputation-Based Defense
```python
# Require high reputation for expensive operations
def authorize_expensive_operation(lct_id: str, atp_cost: int):
    reputation = get_reputation(lct_id)

    if atp_cost > 100 and reputation.total_score < 1.5:
        raise InsufficientReputation("High-cost operations require reputation >= 1.5")

    # Reputation bond: lose rep if operation fails
    if atp_cost > 500:
        bond_reputation(lct_id, amount=0.1)
```

#### D. ATP Insurance
```python
class ATPInsurance:
    def purchase_insurance(self, sequence_id: str, coverage: int, premium: int):
        """
        Victim can purchase insurance against sabotage
        Premium: 10-20% of coverage
        Payout: If failure attributed to external attacker
        """
        insurance_pool.deposit(sequence_id, premium)
        coverage_map[sequence_id] = coverage

    def process_claim(self, sequence_id: str, failure_trace: FailureTrace):
        if failure_trace.attributed_to != sequence_owner:
            # External failure - pay out insurance
            refund_amount = min(coverage_map[sequence_id], actual_loss)
            return refund_amount
        return 0
```

#### E. Retry with ATP Protection
```python
def execute_with_retry(sequence_id: str, max_retries: int = 3):
    """
    Automatic retry with ATP protection
    Only charge ATP for successful operations
    """
    for attempt in range(max_retries):
        try:
            result = execute_sequence(sequence_id)
            charge_atp(sequence_id, full_amount)
            return result
        except FailureException as e:
            if is_transient_failure(e) and attempt < max_retries - 1:
                # Don't charge ATP for transient failures with retry
                log_retry(sequence_id, attempt, e)
                continue
            else:
                # Final failure or non-transient
                charge_partial_atp(sequence_id)
                raise
```

### 3. Why Database Schema Isn't Enough

Database can track:
- ✅ Sequence status (success/failure)
- ✅ ATP charged/refunded
- ✅ Timestamps
- ✅ Resource consumption

Database CANNOT determine:
- ❌ Which external entity caused failure
- ❌ Whether failure was malicious or legitimate
- ❌ Real-time network/resource conditions
- ❌ Distributed system health

**Analogy**: Database is like a ledger. It records transactions but can't investigate fraud.

## Current Mitigation Status

### Existing Protections (Session #62)

1. **Resource-Aware Refunds** ✅
   - Victims keep SOME ATP even on sabotage
   - Minimum 50% retention of consumed ATP
   - Reduces but doesn't eliminate drain impact

2. **Refund Rate Limiting** ✅
   - Max 10 refunds/day per LCT
   - Max 1000 ATP refunded/day
   - Limits rapid drain cycling

### Partial Mitigation

**Session #62 ATP Refund Fix provides PARTIAL protection**:
```
Without refund fix:
  Victim loses 1000 ATP → Gets 1000 ATP refund = 0 net loss (attacker gains nothing)

With refund fix:
  Victim loses 1000 ATP → Gets 500 ATP refund = 500 ATP net loss

Attacker perspective:
  Without fix: Sabotage is pointless (full refunds)
  With fix: Sabotage costs victim 500 ATP (but also costs attacker effort)
```

The refund fix actually makes ATP Drain WORSE for victims but LESS ATTRACTIVE for attackers (no free sabotage since victims don't get full refunds anyway).

## Recommended Implementation Path

### Phase 1: Basic Attribution (2-4 hours)
```sql
-- Extend action_sequences table
ALTER TABLE action_sequences ADD COLUMN failure_attributed_to VARCHAR(255);
ALTER TABLE action_sequences ADD COLUMN failure_attribution_confidence NUMERIC(3,2);
ALTER TABLE action_sequences ADD COLUMN involved_entities JSONB DEFAULT '[]'::jsonb;

-- Log all entities involved in operation
CREATE TABLE operation_participants (
    sequence_id VARCHAR(255) REFERENCES action_sequences(sequence_id),
    lct_id VARCHAR(255) REFERENCES lct_identities(lct_id),
    role VARCHAR(50), -- executor, resource_provider, dependency, network_node
    joined_at TIMESTAMP WITH TIME ZONE,
    left_at TIMESTAMP WITH TIME ZONE,
    success BOOLEAN
);
```

### Phase 2: Simple Heuristics (4-6 hours)
```python
def simple_failure_attribution(sequence_id: str) -> str:
    """
    Basic heuristic attribution:
    1. If one participant failed consistently → attribute to them
    2. If network timeout → attribute to network provider
    3. If resource unavailable → attribute to resource provider
    4. Otherwise → attribute to executor (self-inflicted)
    """
    participants = get_participants(sequence_id)

    for p in participants:
        failure_rate = get_recent_failure_rate(p.lct_id)
        if failure_rate > 0.5:  # Failing >50% of operations
            return p.lct_id  # Likely culprit

    return sequence_owner  # Default: blame executor
```

### Phase 3: Reputation Requirements (1-2 hours)
```sql
CREATE FUNCTION check_atp_operation_authorization(
    p_lct_id VARCHAR(255),
    p_atp_cost INTEGER
) RETURNS BOOLEAN AS $$
DECLARE
    total_reputation NUMERIC(4,3);
BEGIN
    SELECT (t3_score + v3_score) INTO total_reputation
    FROM reputation_scores
    WHERE lct_id = p_lct_id LIMIT 1;

    -- High-cost operations require high reputation
    IF p_atp_cost > 100 AND total_reputation < 1.0 THEN
        RAISE EXCEPTION 'Insufficient reputation for high-cost operation';
    END IF;

    IF p_atp_cost > 500 AND total_reputation < 1.5 THEN
        RAISE EXCEPTION 'Insufficient reputation for very high-cost operation';
    END IF;

    RETURN TRUE;
END;
$$ LANGUAGE plpgsql;
```

### Phase 4: ATP Insurance (6-8 hours)
```sql
CREATE TABLE atp_insurance_policies (
    policy_id BIGSERIAL PRIMARY KEY,
    sequence_id VARCHAR(255) REFERENCES action_sequences(sequence_id),
    insured_lct VARCHAR(255) REFERENCES lct_identities(lct_id),
    coverage_amount INTEGER NOT NULL,
    premium_paid INTEGER NOT NULL,
    purchased_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
    claim_status VARCHAR(50) DEFAULT 'active', -- active, claimed, expired, denied
    payout_amount INTEGER DEFAULT 0,
    claim_reason TEXT
);

CREATE FUNCTION purchase_insurance(
    p_sequence_id VARCHAR(255),
    p_coverage INTEGER
) RETURNS INTEGER; -- Returns premium cost
```

### Phase 5: Retry Mechanisms (2-3 hours)
```python
class ActionSequenceExecutor:
    def execute_with_protection(self, sequence_id, max_retries=3):
        atp_checkpoint = get_current_atp(sequence_id)

        for attempt in range(max_retries):
            try:
                return self.execute(sequence_id)
            except TransientFailure as e:
                if attempt < max_retries - 1:
                    # Restore ATP for retry
                    restore_atp(sequence_id, atp_checkpoint)
                    await asyncio.sleep(2 ** attempt)  # Exponential backoff
                else:
                    raise
```

## Estimated Total Effort

**Full ATP Drain Mitigation**: 15-23 hours
- Phase 1 (Basic Attribution): 2-4 hours
- Phase 2 (Simple Heuristics): 4-6 hours
- Phase 3 (Reputation Requirements): 1-2 hours
- Phase 4 (ATP Insurance): 6-8 hours
- Phase 5 (Retry Mechanisms): 2-3 hours

## Current Recommendation

**Status**: Deprioritize to P3 or defer to application layer

**Rationale**:
1. Session #62 refund fix provides partial protection (victims retain 50% of ATP)
2. Full solution requires distributed tracing infrastructure beyond database
3. Reputation system creates natural defense (high-rep required for expensive ops)
4. Attack economics unfavorable (attacker must expend significant resources)
5. No P1/P2 vulnerabilities remaining - system has no P1/P2 vulnerabilities at research scale

**Alternative**: Implement at application/orchestration layer rather than database layer
- Kubernetes/service mesh already has distributed tracing
- Application logs provide attribution evidence
- Monitoring systems detect anomalous failure patterns

## Conclusion

ATP Drain is a **real vulnerability** but requires **infrastructure beyond database schema**. The current Web4 authorization system has comprehensive protection against all database-level attacks. ATP Drain mitigation should be implemented at the application/orchestration layer where distributed system health and attribution data is available.

**Session #63 Status**: Analyzed and documented complexity. Recommend deferring to application layer or future session with orchestration infrastructure access.
