# Session 120 Track 3: Pattern Federation Security Analysis

**Date**: 2026-01-03
**Session**: 120 (autonomous Web4 research)
**Focus**: Attack vectors in federated pattern systems

## Overview

With Phases 1-3 of pattern federation now complete (integrated Thor + Legion approaches), we need to identify security vulnerabilities before production deployment.

## Threat Model

### Adversary Capabilities
1. **Pattern Submission**: Can submit malicious patterns to corpus
2. **Context Manipulation**: Can lie about pattern context/provenance
3. **Corpus Poisoning**: Can flood corpus with bad patterns
4. **Gradient/Privacy Attack**: Can infer training data from patterns
5. **Resource Exhaustion**: Can submit patterns designed to waste ATP

### Assets to Protect
1. **Agent Survival**: ATP management must not kill agents
2. **Decision Quality**: Pattern matching must provide good recommendations
3. **Privacy**: Patterns shouldn't leak sensitive information
4. **System Resources**: ATP, compute, memory must not be exhausted
5. **Trust Network**: Reputation/trust scores must be accurate

## Identified Attack Vectors

### 1. Pattern Poisoning Attack

**Attack**: Submit patterns that recommend bad decisions

**Example**:
```python
malicious_pattern = {
    "context": {"emotional": {"frustration": 0.1, "stress": 0.1, "complexity": 0.2}},
    "prediction": {"recommendation": "proceed"},  # Low stress context
    "outcome": {"success": False, "atp_change": -50},  # But bad outcome
    "provenance": {"quality_weight": 1.0}  # Claim high quality
}
```

**Impact**: Agents learn bad associations (low stress → proceed → death)

**Mitigations**:
- **Provenance verification**: Cryptographically sign patterns with source identity
- **Reputation weighting**: Weight patterns by submitter's reputation
- **Outlier detection**: Reject patterns far from corpus distribution
- **Outcome validation**: Verify claimed outcomes match actual results
- **Gradual integration**: Test new patterns in sandbox before production

**Severity**: HIGH (can kill agents)

**Status**: ⚠️ UNMITIGATED - Need to implement provenance signatures

### 2. Context Tag Forgery

**Attack**: Lie about pattern's application context to bypass routing

**Example**:
```python
# Consciousness pattern tagged as ATP management to contaminate corpus
forged_pattern = {
    "context": {...},  # Actually from consciousness regulation
    "context_tag": {
        "application": "atp_resource_management",  # FORGED
        "source_system": "web4"  # FORGED
    }
}
```

**Impact**: Bypass Phase 3 contextual routing, reintroduce Session 115's 100% death

**Mitigations**:
- **Cryptographic binding**: Sign context tags with pattern
- **Source verification**: Verify source system via authenticated channel
- **Anomaly detection**: Detect patterns inconsistent with claimed context
- **Trusted sources only**: Only accept patterns from authenticated sources
- **Context inference**: Infer context from pattern content, compare to tag

**Severity**: CRITICAL (bypasses main safety mechanism)

**Status**: ⚠️ UNMITIGATED - Phase 3 trusts context tags without verification

### 3. Provenance Quality Inflation

**Attack**: Claim patterns are "decision" type when actually "observation"

**Example**:
```python
# Observation pattern (low quality) claimed as decision (high quality)
inflated_pattern = {
    "context": {...},
    "provenance": {
        "provenance_type": "decision",  # FORGED (actually observation)
        "quality_weight": 1.0,  # FORGED (actually 0.6)
        "was_deciding_domain": True  # FORGED (actually False)
    }
}
```

**Impact**: Low-quality observation patterns weighted as high-quality decisions

**Mitigations**:
- **Provenance derivation**: Compute provenance from verifiable facts, not claims
- **Decision logging**: Log actual decisions, verify pattern claims
- **Quality auditing**: Periodically audit pattern quality vs claims
- **Reputation decay**: Penalize sources submitting inflated patterns

**Severity**: MEDIUM (degrades quality but doesn't kill)

**Status**: ⚠️ PARTIALLY MITIGATED - Quality computed from claims, not verified

### 4. Corpus Flooding Attack

**Attack**: Submit massive numbers of patterns to dominate corpus

**Example**:
- Adversary submits 10,000 "proceed" patterns for low-stress contexts
- Corpus becomes biased toward proceeding regardless of risk
- Phase 2 distributional balancing dilutes legitimate patterns

**Impact**: Corpus distribution skewed, decision quality degraded

**Mitigations**:
- **Rate limiting**: Limit patterns per source per time period
- **Reputation gating**: Require minimum reputation to submit patterns
- **Source diversity**: Limit maximum percentage from single source
- **Quality thresholding**: Reject patterns below quality threshold
- **Anomaly detection**: Detect sudden corpus composition changes

**Severity**: MEDIUM (degrades quality over time)

**Status**: ⚠️ UNMITIGATED - No rate limiting or source diversity enforcement

### 5. Privacy Leakage via Pattern Inference

**Attack**: Infer private information from submitted patterns

**Example**:
- Agent A has private strategy: "never interact when ATP < 40"
- Submits 100 patterns, all show abort when ATP < 40
- Adversary infers Agent A's private threshold

**Impact**: Private strategies/thresholds leaked to competitors

**Mitigations**:
- **Differential privacy**: Add noise to pattern contexts before submission
- **k-anonymity**: Only submit patterns indistinguishable from k others
- **Aggregation**: Only submit aggregate statistics, not individual patterns
- **Pattern generalization**: Generalize context values (bins instead of exact)
- **Selective sharing**: Only share patterns with trusted federates

**Severity**: LOW-MEDIUM (competitive disadvantage, not immediate harm)

**Status**: ⚠️ UNMITIGATED - Patterns submitted verbatim without privacy protection

### 6. Resource Exhaustion via Pattern Complexity

**Attack**: Submit patterns with extreme contexts to waste matching compute

**Example**:
```python
expensive_pattern = {
    "context": {
        "emotional": {"frustration": 1e10, "stress": 1e10, ...},  # Extreme values
        "quality": {...},  # Force evaluation of all domains
        "attention": {...}
    }
}
```

**Impact**: Pattern matching becomes computationally expensive

**Mitigations**:
- **Context bounds**: Reject patterns with out-of-bounds context values
- **Complexity limits**: Limit pattern evaluation time per query
- **Pattern caching**: Cache frequently-matched patterns
- **Early termination**: Stop matching when sufficient matches found

**Severity**: LOW (DoS, but easily rate-limited)

**Status**: ✅ PARTIALLY MITIGATED - Canonical normalization bounds values to 0-1

### 7. Sybil Attack on Provenance

**Attack**: Create many fake identities to appear trustworthy

**Example**:
- Adversary creates 100 fake "devices"
- Each submits small number of patterns (appears legitimate)
- Collectively dominates corpus

**Impact**: Adversary patterns appear diverse and trustworthy

**Mitigations**:
- **Identity verification**: Require proof-of-work or stake for identity
- **LCT-based identity**: Use Web4 LCT identity with trust attestations
- **Social verification**: Require existing agents to vouch for new ones
- **Reputation bootstrapping**: New identities start with low weight
- **Behavioral analysis**: Detect coordinated behavior across "diverse" sources

**Severity**: HIGH (defeats source diversity protections)

**Status**: ⚠️ UNMITIGATED - No identity verification system yet (future: LCT identity)

### 8. Gradient Attack on Private Patterns

**Attack**: Query patterns strategically to infer exact private patterns

**Example**:
- Query with contexts similar to suspected private pattern
- Observe which patterns match
- Binary search on context to find exact pattern

**Impact**: Reconstruct private patterns from match results

**Mitigations**:
- **Match randomization**: Return random subset of matches
- **Minimum match set**: Always return at least k matches (k-anonymity)
- **Query rate limiting**: Limit queries per source
- **Match obfuscation**: Add decoy matches to result set

**Severity**: MEDIUM (privacy leakage, but requires many queries)

**Status**: ⚠️ UNMITIGATED - Pattern matching returns all matches

## Risk Summary

| Attack Vector | Severity | Status | Priority |
|--------------|----------|---------|----------|
| Pattern Poisoning | HIGH | Unmitigated | P0 |
| Context Tag Forgery | CRITICAL | Unmitigated | P0 |
| Provenance Inflation | MEDIUM | Partial | P1 |
| Corpus Flooding | MEDIUM | Unmitigated | P1 |
| Privacy Leakage | LOW-MEDIUM | Unmitigated | P2 |
| Resource Exhaustion | LOW | Partial | P3 |
| Sybil Attack | HIGH | Unmitigated | P0 |
| Gradient Attack | MEDIUM | Unmitigated | P2 |

## Priority Mitigations (Before Production)

### P0: Critical (Must Implement)

1. **Cryptographic Pattern Signatures**
   - Sign patterns with source identity
   - Bind context tags to pattern content
   - Verify signatures before accepting patterns
   - Prevents: Context forgery, provenance inflation

2. **Source Identity Verification**
   - Implement Web4 LCT-based identity
   - Require trust attestations for pattern submission
   - Prevents: Sybil attacks, untrusted sources

3. **Context Tag Verification**
   - Verify context tags match pattern content
   - Reject patterns with inconsistent tags
   - Prevents: Context forgery (Session 115 100% death)

### P1: Important (Should Implement)

4. **Corpus Diversity Enforcement**
   - Limit max percentage from single source (e.g., 20%)
   - Rate limit pattern submissions per source
   - Prevents: Corpus flooding

5. **Pattern Quality Auditing**
   - Verify provenance claims against logs
   - Detect and penalize quality inflation
   - Maintains corpus quality

6. **Outlier Detection**
   - Reject patterns far from corpus distribution
   - Detect anomalous corpus composition changes
   - Prevents: Poisoning attacks

### P2: Nice to Have (Future Work)

7. **Differential Privacy**
   - Add noise to pattern contexts
   - Implement k-anonymity for patterns
   - Protects: Private strategies

8. **Match Obfuscation**
   - Return randomized match subsets
   - Add decoy matches
   - Prevents: Gradient attacks

## Recommendations

### Short-Term (This Session)
- ✅ Document attack vectors (this file)
- ⚠️ Design cryptographic signature scheme
- ⚠️ Design context tag verification algorithm

### Medium-Term (Next Sessions)
- Implement P0 mitigations (signatures, identity, verification)
- Integrate with Web4 LCT identity system
- Add corpus diversity enforcement

### Long-Term (Multi-Session)
- Implement P1 mitigations (auditing, outlier detection)
- Add differential privacy protections
- Full security audit before production deployment

## Conclusion

Pattern federation (Phases 1-3) provides **technical capability** for safe cross-system pattern sharing. However, **security mechanisms** are needed before production deployment to prevent attacks.

**Most Critical Gap**: Context tag forgery (CRITICAL severity, P0 priority)
- Current Phase 3 trusts context tags without verification
- Adversary can bypass contextual routing by forging tags
- Would reintroduce Session 115's 100% death scenario

**Recommendation**: Implement cryptographic binding of context tags before deploying federated patterns in production.

**Status**: Pattern federation is **research-ready** but **not production-ready** until P0 mitigations implemented.
