# Attack Vector Analysis: Reputation Gaming

**Date:** 2026-01-10
**Platform:** Legion (RTX 4090)
**Session:** Autonomous Web4 Research - Attack Vector Discovery
**Focus:** Reputation system vulnerabilities (Sessions 159, 179, 180, 162)

## Executive Summary

Reputation systems create economic incentives (cognitive credit in Session 179, epistemic confidence in Session 162). Whenever systems create value from reputation, gaming becomes economically rational for adversaries. This document analyzes potential attack vectors against Web4's reputation architecture before production deployment.

**Philosophy**: "Surprise is prize" - discovering vulnerabilities now is valuable research data, not cost.

## System Context

**Reputation Mechanisms:**
- **Session 159**: Federated reputation tracking across nodes
- **Session 179 (Thor)**: Cognitive credit (reputation → ATP efficiency bonus)
- **Session 180 (Thor)**: Persistent reputation storage
- **Session 162 (Legion)**: Epistemic confidence weighting in meta-learning

**Economic Value of Reputation:**
1. **Cognitive Credit**: High reputation = 30-43% ATP efficiency bonus
2. **Epistemic Weight**: High reputation = 2.0x learning weight
3. **Insight Confidence**: High reputation = +20% confidence bonus
4. **Network Trust**: Reputation affects delegation, verification burden

**Incentive Structure**: Reputation has tangible computational value → gaming is economically motivated.

## Attack Vector Categories

### 1. Sybil Reputation Farming ⚠️⚠️⚠️

**Threat Level**: CRITICAL

**Attack Description:**
Attacker creates multiple identities (Sybils) to artificially inflate reputation through self-validation loops.

**Mechanism:**
```
1. Attacker controls nodes A, B, C
2. Node A produces "thought"
3. Node B verifies as high quality (+reputation to A)
4. Node C verifies as high quality (+reputation to A)
5. Repeat with roles rotated
Result: All 3 nodes gain reputation through circular verification
```

**Economic Impact:**
- 3 Sybil nodes can reach "excellent" reputation (50+ score)
- Each gains 0.7x ATP multiplier (43% efficiency bonus)
- Collective ATP advantage grows exponentially
- Can dominate network through efficiency asymmetry

**Exploitation of Session 179:**
Session 179 grants cognitive credit based on reputation alone, without cross-validation of reputation source. Sybils can "print" ATP efficiency through circular validation.

**Current Defense**: **NONE** ❌

**Mitigation Strategies:**

**a) LCT Hardware Identity Anchoring** ⭐⭐⭐⭐⭐
- Reputation tied to hardware identity (TPM/TEE attestation)
- Sybils require physical hardware (expensive)
- Each LCT identity = unique hardware
- **Status**: Architecture supports this (LCT system)
- **Implementation**: Enforce LCT verification in reputation events

**b) Proof-of-Work for Reputation Events** ⭐⭐⭐⭐
- Reputation changes require PoW
- Makes reputation farming computationally expensive
- Sybil nodes must spend real resources
- **Status**: PoW system exists for network participation
- **Implementation**: Add PoW requirement to quality event recording

**c) Reputation Source Diversity Requirement** ⭐⭐⭐⭐
- Reputation only increases from diverse verifiers
- Track reputation sources (which nodes contributed)
- Discount reputation from same-source clusters
- **Status**: Not implemented
- **Implementation**: Add source tracking to reputation events

**d) Cross-Network Reputation Validation** ⭐⭐⭐
- Query multiple nodes for reputation verification
- Consensus on reputation score
- Prevents local manipulation
- **Status**: Federated architecture supports this
- **Implementation**: Reputation queries to multiple federation members

**Recommended Defense Stack:**
```
Layer 1: LCT hardware anchoring (prevents cheap Sybils)
Layer 2: PoW for reputation events (makes farming expensive)
Layer 3: Source diversity requirement (prevents circular validation)
Layer 4: Cross-network validation (prevents local manipulation)
```

---

### 2. Reputation Laundering Through Proxy Nodes ⚠️⚠️

**Threat Level**: HIGH

**Attack Description:**
Attacker builds reputation on "clean" nodes, then uses high-reputation nodes to validate malicious activity on separate attack nodes.

**Mechanism:**
```
1. Attacker legitimately builds reputation on Node A (patient farming)
2. Node A reaches "excellent" status (50+ reputation)
3. Attacker deploys attack Node B (malicious behavior)
4. Node A validates Node B's malicious output as "high quality"
5. Node B gains reputation boost from Node A's epistemic weight
6. Node B uses reputation for malicious purposes
```

**Economic Impact:**
- Node A's 2.0x learning weight (Session 162) amplifies Node B's reputation gain
- Node B reaches good/excellent status faster
- Node B can then validate other attack nodes
- Exponential reputation laundering cascade

**Exploitation of Session 162:**
Session 162's epistemic confidence weighting means high-reputation nodes have outsized influence on learning. A compromised high-reputation node becomes a "reputation factory" for attack nodes.

**Current Defense**: **PARTIAL** ⚠️
- Reputation is per-node (isolation)
- But: High-rep nodes influence others through learning weight

**Mitigation Strategies:**

**a) Reputation Decay Over Time** ⭐⭐⭐⭐⭐
- Reputation decreases if not maintained
- Prevents "reputation banking" for future attacks
- Forces continuous quality production
- **Status**: Not implemented
- **Implementation**: Add time-based decay to reputation score

**b) Verification Challenge Sampling** ⭐⭐⭐⭐
- Randomly challenge high-reputation nodes with known-quality thoughts
- Detect when high-rep node validates poor quality
- Immediate reputation penalty for false validation
- **Status**: Not implemented
- **Implementation**: Add challenge verification to federation protocol

**c) Reputation Stake Slashing** ⭐⭐⭐⭐⭐
- High-reputation nodes "stake" reputation on validations
- If validation proven incorrect, lose staked reputation
- Creates economic disincentive for malicious validation
- **Status**: Not implemented (requires reputation escrow)
- **Implementation**: Add stake mechanism to verification protocol

**d) Epistemic Weight Caps** ⭐⭐⭐
- Limit maximum learning weight (e.g., 2.0x → 1.5x max)
- Reduces impact of single high-rep node
- Requires broader consensus for learning
- **Status**: Could adjust Session 162 parameters
- **Implementation**: Cap `learning_weight` return value

**Recommended Defense Stack:**
```
Layer 1: Reputation decay (prevents banking)
Layer 2: Challenge sampling (detects false validation)
Layer 3: Reputation staking (economic disincentive)
Layer 4: Weight caps (limits single-node influence)
```

---

### 3. Strategic Quality Withholding (Adversarial Gaming) ⚠️

**Threat Level**: MEDIUM

**Attack Description:**
Attacker strategically withholds high-quality contributions until reputation is needed, then floods system with quality to quickly gain reputation and cognitive credit.

**Mechanism:**
```
1. Attacker observes network activity patterns
2. Identifies when high reputation would be valuable (e.g., critical decision)
3. Rapidly produces high-quality verifications to boost reputation
4. Gains cognitive credit quickly
5. Uses credit advantage for attack
6. Returns to low activity after attack
```

**Economic Impact:**
- Session 179's cognitive credit immediately valuable
- 5-10 quality events can move neutral → good (1.0x → 1.5x ATP)
- Strategic timing creates temporary advantage
- Can influence critical network decisions

**Exploitation of Session 162:**
Meta-learning quickly incorporates new high-quality patterns. An attacker can "spike" quality to gain learning weight influence, then inject biased patterns that get high epistemic confidence.

**Current Defense**: **MINIMAL** ⚠️
- Reputation based on cumulative score (can be gamed with bursts)
- No detection of strategic timing patterns

**Mitigation Strategies:**

**a) Reputation Velocity Limits** ⭐⭐⭐⭐
- Cap reputation gain per time period
- Prevents sudden reputation spikes
- Forces gradual reputation building
- **Status**: Not implemented
- **Implementation**: Add rate limiting to quality event processing

**b) Historical Quality Variance Tracking** ⭐⭐⭐⭐
- Track consistency of quality over time
- High variance = suspicious (strategic withholding)
- Reputation bonus for consistent quality
- **Status**: Not implemented
- **Implementation**: Add variance metric to reputation calculation

**c) Minimum Activity Requirements** ⭐⭐⭐
- Reputation requires sustained activity
- Inactive periods cause decay
- Prevents "on-demand" reputation building
- **Status**: Partially via decay (if implemented)
- **Implementation**: Tie reputation to activity frequency

**d) Quality History Depth Requirements** ⭐⭐⭐⭐
- High reputation levels require long history (e.g., 50+ events over 30+ days)
- Recent bursts can't reach "excellent" immediately
- **Status**: Could track in Session 180 persistent storage
- **Implementation**: Add time-depth requirements to reputation levels

**Recommended Defense Stack:**
```
Layer 1: Velocity limits (prevents spikes)
Layer 2: Variance tracking (detects strategic patterns)
Layer 3: Activity requirements (prevents on-demand gaming)
Layer 4: History depth (requires sustained excellence)
```

---

### 4. Reputation Inheritance Attack ⚠️⚠️

**Threat Level**: HIGH (if federation allows identity transfer)

**Attack Description:**
Attacker acquires or impersonates high-reputation node identity to inherit reputation and cognitive credit.

**Mechanism:**
```
1. Target identifies high-reputation node (excellent status)
2. Acquires identity through:
   a) Physical theft of hardware (LCT)
   b) Compromise of private keys
   c) Social engineering of identity transfer
   d) Purchase of identity (black market)
3. Inherits full reputation score
4. Immediately gains cognitive credit (0.7x multiplier)
5. Uses reputation for malicious purposes
```

**Economic Impact:**
- Immediate 43% ATP advantage (Session 179)
- 2.0x learning weight influence (Session 162)
- Network trusts inherited reputation
- Can poison meta-learning with high-confidence patterns

**Exploitation of Session 180:**
Persistent reputation (Session 180) makes identity theft more valuable. A single compromise gives permanent advantage until detected.

**Current Defense**: **STRONG** ✅ (if LCT enforced)
- LCT ties identity to hardware (TPM/TEE)
- Hardware theft detectable (physical security)
- Private key compromise would require TPM break

**Mitigation Strategies:**

**a) LCT Hardware Binding** ⭐⭐⭐⭐⭐ (CRITICAL)
- Reputation strictly bound to hardware identity
- TPM/TEE attestation required for every reputation event
- Identity transfer impossible without hardware
- **Status**: Architecture supports (LCT system)
- **Implementation**: ENFORCE LCT verification (currently optional?)

**b) Reputation Non-Transferability** ⭐⭐⭐⭐⭐
- Reputation explicitly non-transferable in protocol
- Identity change resets reputation to neutral
- Prevents reputation marketplace
- **Status**: Should be protocol rule
- **Implementation**: Add transfer prohibition to reputation spec

**c) Hardware Change Detection** ⭐⭐⭐⭐
- Monitor for hardware identity changes
- Flag sudden identity transfers
- Quarantine reputation until verified
- **Status**: LCT system can detect hardware changes
- **Implementation**: Add monitoring to Session 180 persistence

**d) Behavior Continuity Analysis** ⭐⭐⭐⭐
- Track behavior patterns over time
- Sudden behavior change = potential compromise
- Temporarily reduce reputation until verified
- **Status**: Not implemented
- **Implementation**: Add behavioral fingerprinting to reputation

**Recommended Defense Stack:**
```
Layer 1: LCT hardware binding (CRITICAL - prevents identity theft)
Layer 2: Non-transferability (protocol rule)
Layer 3: Hardware change detection (monitors transfers)
Layer 4: Behavior analysis (detects compromise)
```

---

### 5. Selective Verification Bias (Gaming Meta-Learning) ⚠️⚠️

**Threat Level**: MEDIUM-HIGH

**Attack Description:**
Attacker strategically chooses which verifications to perform based on predicted success, artificially inflating success rate and reputation.

**Mechanism:**
```
1. Attacker analyzes verification requests
2. Identifies "easy" verifications (high success probability)
3. Only performs verifications likely to succeed
4. Rejects/ignores difficult verifications
5. Builds reputation through selective participation
6. Session 160/161 meta-learning sees high success rate
7. Gains cognitive credit without true quality
```

**Economic Impact:**
- Inflated success rate → higher reputation
- Session 162 grants higher learning weight
- Meta-learning sees biased patterns (cherry-picked data)
- Network learns incorrect depth/mode preferences

**Exploitation of Session 160/161:**
Meta-learning assumes representative sampling of verifications. Selective participation creates biased learned insights that appear high-confidence.

**Current Defense**: **NONE** ❌

**Mitigation Strategies:**

**a) Mandatory Verification Assignments** ⭐⭐⭐⭐⭐
- Nodes cannot choose verification tasks
- Random assignment of verifications
- Refusal to verify penalizes reputation
- **Status**: Not implemented (currently voluntary?)
- **Implementation**: Add verification assignment to federation protocol

**b) Difficulty-Adjusted Reputation** ⭐⭐⭐⭐
- Track verification difficulty
- Weight reputation by task difficulty
- Hard verifications worth more reputation
- **Status**: Not implemented
- **Implementation**: Add difficulty scoring to verification protocol

**c) Participation Rate Requirements** ⭐⭐⭐⭐
- Reputation requires X% participation in assigned verifications
- Low participation = reputation decay
- Prevents selective cherry-picking
- **Status**: Not implemented
- **Implementation**: Track participation rate in reputation

**d) Verification Request Randomization** ⭐⭐⭐
- Nodes cannot predict verification difficulty
- Prevents pre-selection strategy
- Forces representative sampling
- **Status**: Could add to federation protocol
- **Implementation**: Blind verification requests

**Recommended Defense Stack:**
```
Layer 1: Mandatory assignments (prevents selection)
Layer 2: Difficulty adjustment (rewards hard tasks)
Layer 3: Participation requirements (forces engagement)
Layer 4: Request randomization (prevents prediction)
```

---

### 6. Reputation Pump-and-Dump ⚠️

**Threat Level**: MEDIUM

**Attack Description:**
Attacker builds legitimate high reputation, then "cashes out" by performing malicious actions that exploit cognitive credit before reputation decay catches up.

**Mechanism:**
```
1. Attacker legitimately builds excellent reputation (50+) over weeks
2. Gains 0.7x ATP multiplier (43% efficiency bonus)
3. Performs malicious verification flood using ATP advantage
4. Malicious verifications accepted due to high reputation trust
5. By time reputation degrades, damage is done
6. Attacker abandons identity, starts fresh
```

**Economic Impact:**
- One-time exploitation of built reputation
- ATP advantage enables large-scale malicious activity
- Network temporarily trusts malicious verifications
- Reputation decay too slow to prevent damage

**Exploitation of Session 179:**
Cognitive credit is immediate benefit from reputation. Once excellent status reached, node can exploit ATP multiplier for rapid malicious activity before reputation penalties accumulate.

**Current Defense**: **WEAK** ⚠️
- Reputation degrades based on poor quality events
- But: Damage occurs before decay catches up

**Mitigation Strategies:**

**a) Real-Time Reputation Adjustment** ⭐⭐⭐⭐⭐
- Reputation updates immediately on quality events
- Bad verification → immediate reputation drop
- Cognitive credit adjusted in real-time
- **Status**: Current system updates per-event
- **Implementation**: Ensure no lag in reputation application

**b) Reputation Volatility Damping** ⭐⭐⭐⭐
- Excellent reputation harder to lose quickly (inertia)
- But: Malicious spike triggers faster decay
- Asymmetric decay (faster down for sudden bad quality)
- **Status**: Not implemented
- **Implementation**: Add volatility detection to reputation

**c) ATP Multiplier Lag** ⭐⭐⭐
- Cognitive credit updates delayed (e.g., 10 verifications)
- Prevents immediate exploitation of reputation spikes
- Smooths ATP advantage over time
- **Status**: Not implemented (currently immediate)
- **Implementation**: Add lag to multiplier calculation (Session 179)

**d) Reputation Escrow for High-Stakes Actions** ⭐⭐⭐⭐
- Critical verifications require "staking" reputation
- Reputation held in escrow until validation
- Failed validation slashes staked reputation
- **Status**: Not implemented
- **Implementation**: Add escrow mechanism to protocol

**Recommended Defense Stack:**
```
Layer 1: Real-time adjustment (immediate feedback)
Layer 2: Volatility damping (detects suspicious patterns)
Layer 3: Multiplier lag (prevents instant exploitation)
Layer 4: Escrow for high-stakes (economic deterrent)
```

---

### 7. Coordinated Reputation Attack (Eclipse) ⚠️⚠️⚠️

**Threat Level**: CRITICAL (network-level attack)

**Attack Description:**
Multiple colluding attackers coordinate to dominate network reputation landscape, control meta-learning, and influence consensus through collective cognitive credit.

**Mechanism:**
```
1. Attackers deploy N colluding nodes
2. Nodes build reputation through mutual validation
3. Collective cognitive credit advantage grows
4. High-reputation attackers dominate meta-learning (Session 162)
5. Network learns biased patterns from attacker majority
6. Attackers control verification consensus
7. Network trust poisoned by coordinated attack
```

**Economic Impact:**
- Multiple excellent-reputation nodes = massive ATP advantage
- Session 162 learning dominated by attacker patterns
- Meta-learning converges on attacker preferences
- Network consensus controlled by reputation cartel
- Legitimate nodes can't compete on ATP efficiency

**Exploitation of Session 162:**
Epistemic confidence weighting means high-reputation attackers have outsized influence on collective learning. If attackers control majority of high-reputation nodes, they control what the network learns.

**Current Defense**: **PARTIAL** ⚠️
- Eclipse defense exists (Session 156: min_peers=3)
- But: No reputation-specific eclipse detection

**Mitigation Strategies:**

**a) Reputation Diversity Requirements** ⭐⭐⭐⭐⭐
- Network health requires reputation diversity
- No single entity controls >X% of high-reputation nodes
- Detect reputation concentration
- **Status**: Not implemented
- **Implementation**: Add diversity monitoring to federation

**b) Decentralized Reputation Verification** ⭐⭐⭐⭐⭐
- Reputation validated by multiple independent nodes
- Consensus required for reputation changes
- Prevents coordinated reputation inflation
- **Status**: Architecture supports (federated)
- **Implementation**: Add reputation consensus to protocol

**c) Reputation Source Attribution** ⭐⭐⭐⭐
- Track which nodes contributed to reputation
- Detect circular reputation patterns
- Flag coordinated reputation building
- **Status**: Not implemented
- **Implementation**: Add source tracking to Session 180

**d) Learning Weight Diversification** ⭐⭐⭐⭐
- Session 162 limits influence from correlated sources
- If N nodes have similar patterns, weight as group not individuals
- **Status**: Not implemented
- **Implementation**: Add correlation detection to Session 162

**e) ATP Advantage Caps (Economic Fairness)** ⭐⭐⭐
- Limit maximum ATP efficiency advantage
- Prevents runaway ATP dominance
- Keeps competition viable for lower-reputation nodes
- **Status**: Could cap Session 179 multipliers
- **Implementation**: Add max/min bounds to cognitive credit

**Recommended Defense Stack:**
```
Layer 1: Reputation diversity (prevents concentration)
Layer 2: Decentralized verification (prevents collusion)
Layer 3: Source attribution (detects coordination)
Layer 4: Learning diversification (limits group influence)
Layer 5: ATP caps (prevents economic dominance)
```

---

## Attack Surface Summary

| Attack Vector | Threat Level | Current Defense | Priority |
|---------------|--------------|-----------------|----------|
| **1. Sybil Reputation Farming** | CRITICAL | None | 🔴 URGENT |
| **2. Reputation Laundering** | HIGH | Partial | 🟠 HIGH |
| **3. Strategic Withholding** | MEDIUM | Minimal | 🟡 MEDIUM |
| **4. Identity Theft** | HIGH | Strong (if LCT enforced) | 🟠 HIGH |
| **5. Selective Verification** | MEDIUM-HIGH | None | 🟠 HIGH |
| **6. Pump-and-Dump** | MEDIUM | Weak | 🟡 MEDIUM |
| **7. Coordinated Eclipse** | CRITICAL | Partial | 🔴 URGENT |

## Recommended Defense Implementation Priority

### Phase 1: Critical (Pre-Production) 🔴

**Must implement before production deployment:**

1. **LCT Hardware Binding Enforcement** (Attack 1, 4)
   - Strict hardware identity verification
   - Reputation tied to TPM/TEE
   - Non-transferability protocol rule

2. **Reputation Source Diversity** (Attack 1, 7)
   - Track reputation sources
   - Require diverse verifiers
   - Detect circular validation

3. **Decentralized Reputation Consensus** (Attack 7)
   - Multi-node reputation verification
   - Consensus on reputation changes
   - Prevents local manipulation

### Phase 2: High Priority (Launch Month) 🟠

4. **Reputation Decay Implementation** (Attack 2, 6)
   - Time-based decay
   - Activity requirements
   - Prevents reputation banking

5. **Challenge Verification Sampling** (Attack 2)
   - Random quality challenges
   - Detect false validation
   - Immediate reputation penalty

6. **Mandatory Verification Assignments** (Attack 5)
   - Prevent cherry-picking
   - Random task assignment
   - Participation requirements

7. **Reputation Velocity Limits** (Attack 3)
   - Cap reputation gain rate
   - Prevent sudden spikes
   - Force gradual building

### Phase 3: Medium Priority (Post-Launch) 🟡

8. **Reputation Staking for Verifications** (Attack 2)
   - Stake reputation on validations
   - Slashing for false verification
   - Economic deterrent

9. **Difficulty-Adjusted Reputation** (Attack 5)
   - Weight by task difficulty
   - Reward hard verifications
   - Prevent easy-task farming

10. **ATP Advantage Caps** (Attack 7)
    - Limit max/min multipliers
    - Economic fairness
    - Prevent dominance spirals

### Phase 4: Advanced (Research) 🔵

11. **Behavioral Fingerprinting** (Attack 4)
    - Detect behavior pattern changes
    - Flag potential compromises
    - Quarantine suspicious reputation

12. **Correlation-Based Learning Weight** (Attack 7)
    - Detect coordinated patterns
    - Weight correlated sources as group
    - Prevent epistemic cartels

## Testing Recommendations

### Attack Simulation Framework

Build attack simulation test suite:

```python
# Pseudocode structure
class ReputationAttackSimulator:
    def test_sybil_farming(self, num_sybils=10):
        """Simulate Sybil reputation farming attack"""

    def test_reputation_laundering(self, clean_nodes=3, attack_nodes=5):
        """Simulate laundering through proxy nodes"""

    def test_strategic_withholding(self, withhold_ratio=0.7):
        """Simulate selective quality contribution"""

    def test_coordinated_eclipse(self, attacker_ratio=0.4):
        """Simulate coordinated network takeover"""

    def measure_attack_success_rate(self):
        """Measure % of attacks that succeed"""

    def measure_detection_time(self):
        """Measure how long attacks go undetected"""

    def measure_damage_scope(self):
        """Measure impact of successful attacks"""
```

### Red Team Exercises

1. **Attempt each attack** in controlled test environment
2. **Measure success rate** and damage scope
3. **Validate defenses** catch attacks
4. **Iterate defenses** based on red team findings

## Conclusion

**Key Findings:**

1. **Reputation has tangible economic value** → gaming is economically rational
2. **Most critical vulnerabilities** are preventable with proper LCT enforcement
3. **Defense-in-depth required** - no single mitigation sufficient
4. **Attack surface manageable** with systematic defense implementation

**Risk Assessment:**

- **Without defenses**: System vulnerable to coordinated reputation attacks that could dominate network
- **With Phase 1 defenses**: Major threats mitigated, residual risk acceptable for test deployment
- **With Phase 1-2 defenses**: Production-ready security posture
- **With Phase 1-3 defenses**: Hardened against sophisticated adversaries

**Recommendation**: Implement Phase 1 defenses before ANY production deployment. Reputation gaming could undermine entire Web4 trust model if not addressed at architectural level.

**Research Value**: "Surprise is prize" - these vulnerabilities discovered in research phase, not production. Each attack vector informs robust defense design. Failed attacks in testing = prevented attacks in production.

---

**Status**: Attack vector analysis complete. Recommendations prioritized for implementation.
**Next Steps**:
1. Implement Phase 1 critical defenses
2. Build attack simulation framework
3. Red team testing
4. Iterate based on findings
