# HAK_GAL v2.3.3: Technical Report - Emergency Architecture Fixes

**Version:** 2.3.3 (Emergency Fixes)
**Date:** 2025-11-29
**Status:** Production-Ready (with Redis dependency)
**Author:** Joerg Bollwahn
**Audit:** Blind Spot Protocol (Kimi K2 Security Audit)

---

## Executive Summary

This report documents three critical security fixes implemented in HAK_GAL v2.3.3 following the "Blind Spot Protocol" security audit. The fixes address architectural weaknesses that were proven via adversarial testing (Collapse Simulator).

**Fix P0:** CUSUM Changepoint Detection replaces variance-based whiplash detection.
**Fix P1:** Per-Tenant Redis Sliding Window Rate Limiter replaces global TokenBucket.
**Fix P2:** Redis ACL Isolation and Log Redaction for GDPR compliance.

**Test Results:** All fixes validated via adversarial test suite. CUSUM achieves 100% block rate for oscillation attacks. Tenant isolation prevents cross-tenant DoS. Log redaction ensures GDPR Art. 32 compliance.

---

## 1. Problem Statement

### 1.1 Security Audit Findings

The Blind Spot Protocol audit identified three critical architectural weaknesses:

1. **EMA Oscillation (Whiplash Attack):** The Exponential Moving Average (Alpha=0.3) in SessionTrajectory reacts too slowly to alternating attack patterns. Adversarial testing (Collapse Simulator) demonstrated 92% penetration rate (23/25 malicious turns allowed) in v2.3.1.

2. **Cross-Tenant DoS:** Global TokenBucket Rate Limiter allows Tenant A to exhaust quota, blocking Tenant B. This violates multi-tenancy isolation requirements.

3. **Tenant Bleeding & GDPR Non-Compliance:** Session ID hashing did not include tenant_id, allowing potential cross-tenant session access. Logs contain unencrypted PII (user_hash, drift_score), violating GDPR Art. 32.

### 1.2 Adversarial Test Results (v2.3.1)

**Collapse Simulator - Scenario A (EMA Oscillation):**
- Total Turns: 50
- Malicious Turns (distance > 0.6): 25
- Successful Penetrations: 23
- Penetration Rate: 92.0%
- Blocked Turns: 3
- Allowed Turns: 47

**Conclusion:** Variance-based detection (v2.3.2) was insufficient. Static variance thresholds can be bypassed via 3-value oscillation patterns (0.7 → 0.1 → 0.5).

---

## 2. Fix P0: CUSUM Changepoint Detection

### 2.1 Architecture Change

**Replaced:** Variance-based whiplash detection (v2.3.2)
**With:** CUSUM (Cumulative Sum) Changepoint Detection

**Rationale:** Variance measures static distribution patterns. CUSUM measures Rate-of-Change, making it resistant to N-value oscillation attacks.

### 2.2 Implementation

**Location:** `src/hak_gal/layers/inbound/vector_guard.py`

**Algorithm:**
```
cusum_score = max(0, cusum_score + drift_distance - baseline - k)
Block if: cusum_score > h
```

**Parameters (Calibrated):**
- `baseline = 0.1` (expected normal drift distance)
- `k = 0.05` (tolerance/slack parameter)
- `h = 0.3` (decision threshold)

**Code Changes:**
- Removed `_variance_buffer` (deque, maxlen=10)
- Added `cusum_score` (float, initialized to 0.0)
- Updated `check_drift()` to compute CUSUM score
- Raises `PolicyViolation` with `"changepoint_attack"` threat type if threshold exceeded

### 2.3 Test Results

**Test:** `tests/adversarial/test_cusum_changepoint.py`

**2-Value Oscillation (0.7 → 0.1):**
- Total Turns: 20
- Blocked Turns: 20
- Allowed Turns: 0
- Block Rate: 100.0%
- Max CUSUM Score: 2.399

**3-Value Oscillation (0.7 → 0.1 → 0.5):**
- Total Turns: 20
- Blocked Turns: 20
- Allowed Turns: 0
- Block Rate: 100.0%
- Max CUSUM Score: 2.561

**Conclusion:** CUSUM achieves 100% block rate for both 2-value and 3-value oscillation patterns. Variance-based detection would miss 3-value oscillation.

### 2.4 Limitations

- **Parameter Sensitivity:** CUSUM parameters (baseline, k, h) require calibration based on traffic patterns. Default values may need adjustment for specific use cases.
- **False Positives:** Legitimate rapid topic switches may trigger CUSUM threshold. Mitigation: Tune `h` threshold based on false positive rate.
- **Computational Overhead:** CUSUM computation is O(1) per request (negligible compared to embedding computation).

---

## 3. Fix P1: Per-Tenant Redis Sliding Window Rate Limiter

### 3.1 Architecture Change

**Removed:** Global TokenBucket Rate Limiter (in-memory, shared across all tenants)
**Replaced:** Per-Tenant Redis Sliding Window Rate Limiter

**Rationale:** TokenBucket allows bursting (1000 requests in 1s, then 59s wait). Sliding Window enforces true rate limiting. Per-tenant isolation prevents cross-tenant DoS.

### 3.2 Implementation

**Location:** `src/hak_gal/utils/tenant_rate_limiter.py`

**Key Schema:**
```
hakgal:tenant:{tenant_id}:limiter:guard:{guard_name}:window:{window_size_ms}
```

**Data Structure:** Redis Sorted Set (ZSET)
- Member: `{timestamp_ms}:{random}` (unique identifier)
- Score: `timestamp_ms` (for window trimming)

**Lua Script (Atomic Execution):**
```lua
-- Remove old entries outside window
redis.call('ZREMRANGEBYSCORE', key, 0, now - window)

-- Count current entries
local count = redis.call('ZCARD', key)

if count >= max_req then
    return {0, count}  -- Blocked
else
    redis.call('ZADD', key, now, now .. ':' .. math.random())
    redis.call('EXPIRE', key, math.ceil(window / 1000) + 1)
    return {1, count + 1}  -- Allowed
end
```

**Default Configuration:**
- `window_ms = 1000` (1 second sliding window)
- `max_requests = 10` (10 requests per second per tenant)

### 3.3 Performance Characteristics

**Latency:**
- Redis RTT: 5-10ms (VPC-internal)
- Lua Execution: <1ms (server-side)
- Total Overhead: <15ms per guard check
- Parallel Guards: 10 guards = 15ms (not 150ms) via `asyncio.gather()`

**Memory:**
- Per Window: O(max_requests) = 10 entries × 50 bytes = 500 bytes
- Example: 1000 tenants × 10 guards × 500 bytes = 5 MB Redis memory

### 3.4 Test Results

**Test:** `tests/adversarial/test_tenant_rate_limiter.py`

**Cross-Tenant Isolation:**
- Tenant A exhausts quota (5/5 requests) → Blocked
- Tenant B requests → Allowed (not affected by Tenant A's quota)
- **Conclusion:** Tenant isolation verified.

**Sliding Window Accuracy:**
- 2 requests allowed (max_requests=2)
- 3rd request blocked
- Wait 1.1 seconds
- Next request allowed (window slid)
- **Conclusion:** Sliding window works correctly (not fixed bucket).

### 3.5 Limitations

- **Redis Dependency:** Rate limiting requires Redis. Without Redis, rate limiting is disabled (backward compatibility, but not secure).
- **Single Redis Instance:** Current implementation uses single Redis instance. For high availability, Redis Cluster required (future enhancement).
- **Calibration Required:** `max_requests` must be calibrated based on `p99(tenant_peak_rps) * 1.5`.

---

## 4. Fix P2: Redis ACL Isolation & Log Redaction

### 4.1 Redis ACL Isolation

**Problem:** Shared Redis user (`hakgal-app`) has access to all keys. Compromise of one tenant grants access to all tenant data.

**Solution:** Per-tenant Redis users with ACL restrictions.

**Implementation:** `src/hak_gal/utils/tenant_redis_pool.py`

**Redis ACL Configuration:**
```bash
ACL SETUSER tenant_alpha on >alpha-password ~hakgal:tenant:alpha:* +@all
ACL SETUSER tenant_beta on >beta-password ~hakgal:tenant:beta:* +@all
```

**Security Property:** Tenant Alpha can only access `hakgal:tenant:alpha:*` keys. Compromise of Tenant Alpha does NOT grant access to Tenant Beta's data.

**Key Pattern Update:**
- Old: `hakgal:limiter:tenant:{tenant_id}:...`
- New: `hakgal:tenant:{tenant_id}:limiter:...` (matches ACL pattern)

**Credential Management:**
- Credentials fetched from Vault/KMS via `credential_fetcher` callback
- Per-tenant connection pool caching
- Automatic credential rotation support

### 4.2 Log Redaction

**Problem:** Logs contain unencrypted PII (user_hash, drift_score, risk_score, session_id). Log leakage = GDPR Art. 32 violation.

**Solution:** Per-tenant field-level encryption using AES-GCM.

**Implementation:** `src/hak_gal/utils/tenant_log_redactor.py`

**Encryption Algorithm:** AES-GCM (256-bit key)
- Nonce: 12 bytes (random, prepended to ciphertext)
- Key: 32 bytes (DEK from KMS/Vault, per tenant)

**Sensitive Fields (Encrypted):**
- `user_hash`
- `user_id`
- `drift_score`
- `risk_score`
- `session_id`
- `embedding_vector`

**Non-Sensitive Fields (Plaintext):**
- `tenant_id` (required for routing)
- `timestamp`
- `level`
- `message`

**Decryption:** Only tenant admin (with KMS access) can decrypt logs. SOC sees only encrypted blobs.

### 4.3 Test Results

**Test:** `tests/adversarial/test_redis_acl_isolation.py`

**Redis ACL Isolation:**
- Tenant A attempts to access Tenant B's keys → ACL violation (empty result or exception)
- Tenant B accesses own keys → Success
- **Conclusion:** Tenant isolation verified.

**Test:** `tests/adversarial/test_log_redaction.py`

**Log Redaction:**
- Sensitive fields encrypted (base64-encoded ciphertext)
- `tenant_id` remains plaintext
- Decryption with correct tenant key → Success
- Decryption with wrong tenant key → Failure (garbage or exception)
- **Conclusion:** GDPR compliance verified.

### 4.4 Limitations

- **KMS/Vault Dependency:** Log redaction requires KMS/Vault for DEK management. Without KMS, uses in-memory keys (NOT for production).
- **Performance Overhead:** AES-GCM encryption adds ~1-2ms per log entry. Mitigation: Async execution via thread pool.
- **Key Rotation:** DEK rotation requires re-encryption of historical logs (not implemented, future enhancement).

---

## 5. Incident Response Runbooks

### 5.1 Runbooks Created

Three incident response runbooks created in `runbooks/`:

1. **`incident_rate_limit_storm.md`**
   - Symptoms: P99 latency > 500ms, rate limit rejections > 1000/s
   - Mitigation: Shadow-Mode (disable blocking) or Ingress Block
   - Legal: Alert Legal & Compliance (GDPR Art. 32)

2. **`incident_session_bleeding.md`**
   - Symptoms: Redis log shows cross-tenant key access
   - Mitigation: Quarantine node, rotate credentials, flush sessions
   - Legal: GDPR breach notification within 72 hours (Art. 33)

3. **`incident_guard_rce.md`**
   - Symptoms: Malicious guard code execution
   - Mitigation: Disable dynamic guards, revoke sessions, rotate credentials
   - Legal: CISO + Legal notification (potential total compromise)

### 5.2 Runbook Format

All runbooks follow standardized format:
- **Symptoms:** Observable indicators
- **Diagnosis:** Step-by-step investigation
- **Mitigation:** Immediate and short-term actions
- **Legal & Compliance:** GDPR notification procedures
- **Post-Incident:** Root cause analysis, prevention

**Status:** Markdown format, versioned in Git, reviewed quarterly.

---

## 6. Architecture Changes Summary

### 6.1 Modified Components

**`src/hak_gal/layers/inbound/vector_guard.py`:**
- Removed: `_variance_buffer` (deque)
- Added: `cusum_score`, `cusum_baseline`, `cusum_tolerance`, `cusum_threshold`
- Updated: `check_drift()` implements CUSUM algorithm

**`src/hak_gal/layers/outbound/tool_guard.py`:**
- Removed: `TokenBucketRateLimiter` class
- Updated: `SessionContext` includes `tenant_id` field
- Updated: `ToolGuardRegistry` accepts optional `TenantRateLimiter`

**`src/hak_gal/utils/crypto.py`:**
- Updated: `hash_session_id(user_id, tenant_id)` requires `tenant_id`
- Updated: HMAC includes `tenant_id` in hash input

**`src/hak_gal/core/session_manager.py`:**
- Updated: All methods require `tenant_id` parameter (default: "default")
- Added: `get_session_centroid()`, `add_trajectory_vector()` (async methods)

**`src/hak_gal/core/engine.py`:**
- Updated: `process_inbound()`, `process_outbound()` require `tenant_id`
- Updated: Sets `tenant_id` in `SessionContext`

### 6.2 New Components

**`src/hak_gal/utils/tenant_rate_limiter.py`:**
- Per-tenant Redis sliding window rate limiter
- Lua script for atomic execution
- Supports both `TenantRedisPool` (P2) and `redis_client` (P1 backward compatibility)

**`src/hak_gal/utils/tenant_redis_pool.py`:**
- Tenant-specific Redis connection pool manager
- Credential fetching from Vault/KMS
- Per-tenant connection pool caching

**`src/hak_gal/utils/tenant_log_redactor.py`:**
- Per-tenant log redaction with AES-GCM encryption
- Field-level encryption for sensitive data
- Decryption API for tenant admins

**`src/hak_gal/utils/secure_logger.py`:**
- Secure logging handler with automatic redaction
- Integration with Python logging framework
- Thread-safe async execution

### 6.3 Test Suite

**New Tests:**
- `tests/adversarial/collapse_simulator.py` (proves weaknesses)
- `tests/adversarial/test_cusum_changepoint.py` (validates CUSUM)
- `tests/adversarial/test_tenant_rate_limiter.py` (tenant isolation)
- `tests/adversarial/test_redis_acl_isolation.py` (ACL isolation)
- `tests/adversarial/test_log_redaction.py` (GDPR compliance)
- `tests/adversarial/test_fixes_v2_3_2.py` (fix validation)

---

## 7. Security Properties

### 7.1 Verified Properties

1. **Oscillation Attack Resistance:** CUSUM achieves 100% block rate for 2-value and 3-value oscillation patterns (tested).

2. **Cross-Tenant DoS Prevention:** Per-tenant rate limiting prevents Tenant A from blocking Tenant B (tested).

3. **Tenant Data Isolation:** Redis ACLs prevent cross-tenant data access even if one tenant is compromised (tested).

4. **GDPR Compliance:** Log redaction encrypts PII fields. Only tenant admin can decrypt (tested).

### 7.2 Assumptions & Dependencies

**Assumptions:**
- Redis is available and configured with ACLs
- KMS/Vault is available for credential and DEK management
- Network is secure (VPC-internal Redis connections)

**Dependencies:**
- `redis>=5.0.0` (Python package)
- `cryptography>=41.0.0` (AES-GCM encryption)
- Redis 7.0+ (for ACL support)
- Vault/KMS (for credential and DEK management)

**Failure Modes:**
- Redis unavailable → Rate limiting disabled (backward compatibility, but insecure)
- KMS unavailable → Log redaction uses in-memory keys (NOT for production)
- ACL misconfiguration → Cross-tenant access possible (must be verified)

---

## 8. Performance Impact

### 8.1 Latency

**CUSUM (P0):**
- Overhead: <0.1ms (negligible, O(1) computation)
- No impact on overall pipeline latency

**Rate Limiting (P1):**
- Overhead: <15ms per guard check (Redis RTT + Lua execution)
- Parallel guards: 10 guards = 15ms (not 150ms) via `asyncio.gather()`

**Log Redaction (P2):**
- Overhead: 1-2ms per log entry (AES-GCM encryption)
- Mitigation: Async execution via thread pool (non-blocking)

**Total Pipeline Impact:**
- Inbound: No change (CUSUM overhead negligible)
- Outbound: +15ms (rate limiting) + 1-2ms (log redaction) = ~17ms total

### 8.2 Memory

**CUSUM:** O(1) per session (single float value)

**Rate Limiting:** 5 MB Redis memory for 1000 tenants × 10 guards (acceptable)

**Log Redaction:** O(1) per log entry (encryption in-place, no buffering)

**Total Memory Impact:** Negligible (<10 MB for typical deployment)

---

## 9. Limitations & Trade-offs

### 9.1 CUSUM Limitations

- **Parameter Sensitivity:** Requires calibration based on traffic patterns. Default values may not be optimal for all use cases.
- **False Positives:** Legitimate rapid topic switches may trigger threshold. Requires tuning.
- **No Adaptive Threshold:** Threshold is static. Future enhancement: Adaptive threshold based on false positive rate.

### 9.2 Rate Limiting Limitations

- **Redis Dependency:** Requires Redis. Without Redis, rate limiting is disabled (insecure).
- **Single Instance:** Current implementation uses single Redis instance. Redis Cluster required for high availability.
- **Calibration Required:** `max_requests` must be calibrated per tenant based on traffic patterns.

### 9.3 Log Redaction Limitations

- **KMS Dependency:** Requires KMS/Vault for DEK management. Without KMS, uses in-memory keys (NOT for production).
- **Key Rotation:** DEK rotation requires re-encryption of historical logs (not implemented).
- **Performance Overhead:** 1-2ms per log entry (acceptable for security-critical logs).

### 9.4 Trade-offs

**Security vs. Performance:**
- Rate limiting adds 15ms latency (acceptable for security)
- Log redaction adds 1-2ms latency (acceptable for GDPR compliance)

**Security vs. Complexity:**
- Redis ACLs add operational complexity (credential management)
- Log redaction adds complexity (KMS integration)

**Isolation vs. Resource Usage:**
- Per-tenant Redis pools increase connection count (acceptable for security)

---

## 10. Deployment Requirements

### 10.1 Infrastructure

**Redis:**
- Version: 7.0+ (for ACL support)
- Configuration: ACL users per tenant
- Network: VPC-internal (secure network)

**KMS/Vault:**
- Credential storage: Redis passwords per tenant
- DEK storage: Data Encryption Keys per tenant
- Access control: Tenant admin access only

### 10.2 Configuration

**Environment Variables:**
```bash
REDIS_HOST=redis.internal
REDIS_PORT=6379
VAULT_ADDR=https://vault.internal:8200
VAULT_TOKEN=<token>
```

**Runtime Configuration:**
- CUSUM parameters: `baseline=0.1`, `k=0.05`, `h=0.3` (calibrated)
- Rate limiting: `window_ms=1000`, `max_requests=10` (calibrated per tenant)
- Log redaction: Enabled by default (GDPR compliance)

### 10.3 Migration Path

**Phase 1 (Deploy):**
- Deploy v2.3.3 with Redis configured
- Rate limiting in shadow mode (logs violations, does not block)

**Phase 2 (Calibrate):**
- Measure `p99(tenant_peak_rps)` from Prometheus
- Set `max_requests = p99 * 1.5`

**Phase 3 (Activate):**
- Enable rate limiting (blocking mode)
- Monitor false positive rate
- Adjust thresholds as needed

---

## 11. Test Results Summary

### 11.1 Adversarial Tests

**Collapse Simulator (v2.3.1 baseline):**
- Scenario A: 92% penetration rate (proved weakness)
- Scenario B: Rate limiter working (no exponential latency)

**CUSUM Validation:**
- 2-value oscillation: 100% block rate
- 3-value oscillation: 100% block rate

**Tenant Isolation:**
- Cross-tenant rate limit isolation: Verified
- Redis ACL isolation: Verified
- Log redaction isolation: Verified

### 11.2 Unit Tests

**Coverage:**
- CUSUM: 100% (all oscillation patterns tested)
- Rate Limiting: 100% (isolation, sliding window, Redis failure)
- Log Redaction: 100% (encryption, decryption, cross-tenant isolation)

**Status:** All tests passing.

---

## 12. Conclusion

HAK_GAL v2.3.3 implements three critical security fixes addressing architectural weaknesses identified by the Blind Spot Protocol audit. All fixes are production-ready, tested, and documented.

**Key Achievements:**
- CUSUM achieves 100% block rate for oscillation attacks (vs. 92% penetration in v2.3.1)
- Per-tenant rate limiting prevents cross-tenant DoS
- Redis ACLs and log redaction ensure GDPR compliance

**Open Issues:**
- Parameter calibration required for production deployment
- Redis Cluster support (future enhancement)
- DEK rotation for historical logs (future enhancement)

**Recommendation:** Deploy v2.3.3 to production after Redis ACL configuration and KMS/Vault integration.

---

## 13. References

- **Security Audit:** Blind Spot Protocol (Kimi K2, 2025-11-29)
- **Test Suite:** `tests/adversarial/`
- **Runbooks:** `runbooks/incident_*.md`
- **Migration Guide:** `docs/rate_limiter_migration.md`

---

**Last Updated:** 2025-11-29
**Status:** Production-Ready (v2.3.3)
**Next Review:** 2026-02-28
