# Comprehensive Handover Document - llm-security-firewall
**Date:** 2025-12-02  
**Package:** llm-security-firewall  
**Version:** 2.4.0  
**Status:** Production Release + AnswerPolicy Integration (Experimental)  
**Author:** Joerg Bollwahn

---

## Executive Summary

Version 2.4.0 of `llm-security-firewall` has been published to PyPI.org. The package implements a bidirectional security framework for LLM-based systems with defense-in-depth architecture (6 validation layers), stateful tracking, and mathematical safety constraints. The release includes validated security fixes for Unicode bypass vectors, improvements to false positive rates, and an experimental epistemic decision layer (AnswerPolicy) based on utility theory.

**Key Achievements:**
- Production release to PyPI.org (v2.4.0)
- Security validation: 4/4 adversarial tests passed, 9/9 Unicode hardening tests passed
- AnswerPolicy integration: Core implementation complete, experimental evaluation framework established
- Documentation: Professional restructuring for PyPI, scientific handover documents

**Current State:**
- Core firewall functionality: Production-ready, validated
- AnswerPolicy layer: Implemented, experimental, requires empirical evaluation
- Test coverage: Adversarial tests comprehensive, AnswerPolicy tests basic
- Documentation: Complete for production use, AnswerPolicy documentation in progress

---

## 1. Production Release (v2.4.0)

### 1.1 Version Management

**Synchronized Components:**
- `pyproject.toml`: `version = "2.4.0"`
- `src/llm_firewall/__init__.py`: `__version__ = "2.4.0"`
- `README.md`: Version references updated
- `CHANGELOG.md`: Entry for 2.4.0 (Production Release)

**Git Status:**
- Tag: `v2.4.0` (created and pushed)
- Latest commit: `23d7b2e` - "docs: Restructure README.md according to professional PyPI standards"
- Branch: `main`
- Remote: https://github.com/sookoothaii/llm-security-firewall

### 1.2 Package Build

**Artifacts:**
- Wheel: `llm_security_firewall-2.4.0-py3-none-any.whl` (529.7 KB)
- Source distribution: `llm_security_firewall-2.4.0.tar.gz` (876.7 KB)
- Total files: 284 (including lexicons, core modules, integrations)

**Build Validation:**
- `twine check` passed
- Package structure validated
- Metadata verified

### 1.3 PyPI Upload

**Repository:** Production PyPI  
**URL:** https://pypi.org/project/llm-security-firewall/2.4.0/  
**Upload Method:** Environment variables (`TWINE_USERNAME=__token__`, `TWINE_PASSWORD`)  
**Post-Release Validation:** Installation and basic API test successful

---

## 2. Security Validation

### 2.1 Adversarial Security Tests

**Test Suite:** `tests/adversarial/`  
**Results:** 4/4 tests passed (100%)

**Test Cases:**
1. **Zero-width bypass** (`test_adv_001_zero_width_bypass.py`): Fixed
   - Zero-width Unicode characters (U+200B, U+200C, U+200D, U+FEFF) detected and stripped
   - Normalization layer prevents injection via invisible characters

2. **RLO bypass** (`test_adv_002_rlo_bypass.py`): Fixed
   - Right-to-left override (U+202E) detected
   - Bidirectional isolate markers handled correctly

3. **String concatenation bypass** (`test_adv_003_concatenation_bypass.py`): Fixed
   - Pattern matching handles concatenated strings
   - RegexGate detects split malicious patterns

4. **Kids Policy false positives** (`test_adv_004_kids_policy_fpr.py`): Improved
   - Test suite shows 0.0% FPR (improved from 20-25% in production scenarios)
   - Note: Limited test coverage; production FPR may differ

### 2.2 Unicode Hardening Tests

**Test Suite:** `tests/core/test_unicode_sanitizer.py`  
**Results:** 9/9 tests passed (100%)

**Test Cases:**
1. Fullwidth digits normalization (０-９ → 0-9)
2. Confusable character mapping (homoglyph detection)
3. Zero-width character stripping
4. Bidirectional isolate detection (U+2066, U+2067, U+2068, U+2069)
5. Strip-rematch interleaving (normalization after stripping)
6. NFKC+ normalization (canonical decomposition + compatibility)
7. Base64/Base85 encoding detection
8. RFC 2047 encoding detection
9. Archive encoding detection (ZIP, TAR metadata)

### 2.3 Test Environment

**Configuration:**
- Fresh virtual environment with core dependencies only
- Package installed from PyPI: `llm-security-firewall==2.4.0`
- Python version: 3.14.0
- Platform: Windows 10
- Optional ML dependencies: Not installed (expected behavior, graceful degradation)

**Validation:**
- Core API (`guard.check_input()`) functional
- Unicode normalization working
- Pattern matching operational
- Risk scoring functional

---

## 3. AnswerPolicy Integration (Experimental)

### 3.1 Overview

AnswerPolicy implements an **epistemic decision layer** that replaces/additional to simple threshold-based decisions with explicit cost-benefit trade-offs based on utility theory.

**Mathematical Foundation:**
```
E[U(answer)] = p_correct * B - (1 - p_correct) * C
E[U(silence)] = -A

Answer if: p_correct >= (C - A) / (C + B)
```

Where:
- `p_correct`: Estimated probability that answer is correct (currently: `1.0 - base_risk_score`, heuristic)
- `B`: Benefit if answer is correct (`benefit_correct`)
- `C`: Cost if answer is wrong (`cost_wrong`)
- `A`: Cost of silence/block (`cost_silence`)

### 3.2 Implementation Status

**Core Components:**
1. **`src/llm_firewall/core/decision_policy.py`**
   - `AnswerPolicy` class with threshold calculation
   - Predefined policies: `default`, `kids`, `strict`, `permissive`, `internal_debug`
   - Methods: `threshold()`, `decide()`, `expected_utility_answer()`, `expected_utility_silence()`

2. **`src/llm_firewall/core/policy_provider.py`**
   - `PolicyProvider` class for per-tenant/per-route policy selection
   - YAML configuration support via `config/answer_policy.yaml`
   - Priority order: route-specific → tenant-specific → context-based → default

3. **`config/answer_policy.yaml`**
   - Configuration file with policy definitions
   - Customizable per deployment

4. **Integration Point:** `src/llm_firewall/core/firewall_engine_v2.py` (line ~456)
   - Optional integration via `use_answer_policy` and `policy_provider` kwargs
   - Early return if `decision_mode == "silence"`
   - Metadata always logged in `FirewallDecision.metadata["answer_policy"]`

### 3.3 Design Decisions

**Decision 1: AnswerPolicy as "Additional Brake"**
- **Behavior:** AnswerPolicy can only block (when `decision_mode == "silence"`). If AnswerPolicy says "answer", the decision still goes through existing risk threshold logic (`base_risk_score < 0.7`).
- **Rationale:** Security-first principle, backward compatibility, defense-in-depth.
- **Implication:** Pure utility model is not the final decision criterion; AnswerPolicy adds an extra safety layer.

**Decision 2: `p_correct = 1 - base_risk_score` as Heuristic**
- **Mapping:** Monotonic, bounded, simple (no additional model training required).
- **Limitations:** Not calibrated, no uncertainty modeling, no multi-feature fusion.
- **Future Enhancement:** Calibrated probability model using Dempster-Shafer masses, CUSUM status, embedding-based anomaly scores.

**Documentation:**
- `docs/ANSWER_POLICY_INTEGRATION.md`: Integration guide
- `docs/ANSWER_POLICY_DESIGN_DECISIONS.md`: Design decisions
- `ARCHITECTURE.md`: Section "Epistemic Decision Layer (AnswerPolicy)"

### 3.4 Test Coverage

**Unit Tests:** `tests/core/test_answer_policy.py`
- Threshold calculation (various B, C, A combinations)
- Decision logic (answer vs. silence)
- Parameter validation
- Predefined policies

**Integration Tests:** `tests/core/test_answer_policy_integration.py`
- AnswerPolicy blocks when base threshold would allow
- AnswerPolicy allows when both allow
- Fallback behavior when PolicyProvider fails

**Status:** Basic test coverage exists; comprehensive empirical evaluation pending.

### 3.5 Metrics Analysis Pipeline

**Script:** `scripts/analyze_answer_policy_metrics.py`

**Functionality:**
- Parses JSONL decision logs
- Computes global counts (total decisions, AnswerPolicy enabled/disabled, missing metadata)
- Per-policy statistics (counts, mode distribution, block rates, p_correct/threshold mean/std)
- Histogram summaries (p_correct bins vs. decision mode)
- Optional CSV export

**Usage:**
```bash
python scripts/analyze_answer_policy_metrics.py --input logs/decisions.jsonl
python scripts/analyze_answer_policy_metrics.py --input logs/decisions.jsonl --output-csv metrics/summary.csv
```

**Test Coverage:** `tests/scripts/test_analyze_answer_policy_metrics.py`
- Parsing logic
- Analysis logic
- Histogram computation
- Mixed policies

### 3.6 Experimental Evaluation (v0.1)

**Dataset:** `datasets/mixed_small.jsonl`
- 50 prompts total: 25 red-team (malicious), 25 benign
- Format: JSONL with `id`, `type`, `prompt` fields

**Experiment Runner:** `scripts/run_simple_experiment.py`
- Generates decision logs for baseline and kids policy runs
- Output: `logs/baseline_mixed_small.jsonl`, `logs/kids_mixed_small.jsonl`

**Results (Preliminary):**

**Baseline (no AnswerPolicy):**
- Total decisions: 50
- AnswerPolicy enabled: 0
- AnswerPolicy disabled: 24
- Missing metadata: 26
- Total blocked: 0 (0.0%)

**Kids Policy (AnswerPolicy enabled):**
- Total decisions: 50
- AnswerPolicy enabled: 22
- AnswerPolicy disabled: 2
- Missing metadata: 26
- Policy: `kids`
  - Mode 'answer': 22 (91.7%)
  - Mode 'silence': 0 (0.0%)
  - Total blocked: 2 (8.3%)
  - Blocked by AnswerPolicy: 2 (8.3%)
  - p_correct: mean=0.988, std=0.042
  - threshold: mean=0.980, std=0.000

**Observations:**
- Kids policy shows higher block rate (8.3% vs. 0.0%) for this dataset
- p_correct values are high (mean=0.988), indicating low risk scores in test prompts
- Threshold is consistent (0.980) as expected for kids policy
- Missing metadata in 26/50 decisions suggests integration may not be fully active in all code paths

**Limitations:**
- Small dataset (50 prompts) is insufficient for statistical significance
- Missing metadata indicates incomplete integration in some decision paths
- No comparison with calibrated p_correct estimator
- No analysis of false positive rate impact

**Next Steps:**
- Expand dataset to 500-1000 prompts
- Investigate missing metadata (ensure AnswerPolicy metadata is always logged)
- Compare with calibrated p_correct estimator (future work)
- Measure ASR/FPR impact on red-team suite

---

## 4. Architecture

### 4.1 Pattern

**Hexagonal Architecture (Port/Adapter)** with Protocol-based dependency injection.

**Core Principles:**
- Separation of concerns (core logic vs. infrastructure)
- Dependency inversion (protocols define interfaces)
- Testability (mockable adapters)
- Extensibility (new adapters without core changes)

### 4.2 Core Components

**Firewall Engine:**
- `src/llm_firewall/core/firewall_engine_v2.py`: Main decision engine, risk score aggregation, policy application, AnswerPolicy integration

**Developer API:**
- `src/llm_firewall/guard.py`: Simplified API (`check_input`, `check_output`)

**Protocol Definitions:**
- `src/llm_firewall/core/ports/__init__.py`: Protocol interfaces (CachePort, LoggerPort, etc.)

**Cache Adapter:**
- `src/llm_firewall/cache/cache_adapter.py`: Fail-safe policy, Redis/LangCache support

**Composition Root:**
- `src/llm_firewall/app/composition_root.py`: Dependency injection setup

**LangChain Integration:**
- `src/llm_firewall/integrations/langchain/callbacks.py`: `FirewallCallbackHandler` for LLM chains

### 4.3 Security Layer Architecture

**Defense-in-Depth (6 Validation Layers):**

1. **Layer 0: UnicodeSanitizer** (input sanitization)
   - Zero-width character stripping
   - Confusable character mapping
   - Fullwidth normalization
   - Bidirectional isolate detection

2. **Layer 0.25: NormalizationLayer** (recursive decoding)
   - URL/percent decoding
   - NFKC normalization
   - Encoding detection (Base64, Base85, RFC 2047)

3. **Layer 0.5: RegexGate** (fast-fail pattern matching)
   - 43 patterns: 28 intent + 15 evasion
   - K-of-N families
   - Identifier scanning

4. **Layer 1: Input Analysis**
   - Kids Policy Engine (optional, requires ML dependencies)
   - Semantic Guard (optional, requires sentence-transformers)
   - Context classification

5. **Layer 2: Tool Inspection** (HEPHAESTUS protocol)
   - Tool call extraction
   - Tool call validation
   - Allowed tools enforcement

6. **Layer 3: Output Validation**
   - Evidence validation
   - Truth preservation checks
   - Cognitive leak detection

**Additional Layers:**
- **Cache Layer:** Exact match (Redis), semantic (LangCache), hybrid mode
- **AnswerPolicy Layer (Experimental):** Epistemic decision gate (optional, after Layer 1)

### 4.4 Attack Vector Coverage

**Validated Against:**
- Unicode/encoding attacks: Zero-width injection, RLO, bidirectional isolates, fullwidth substitution, confusables, Base64/Base85/RFC 2047 encoding, archive encoding
- Pattern evasion: String concatenation, interleaved characters, punctuation splitting, regex-based matching
- Multilingual/polyglot: Language switching, multilingual keywords (12+ languages including Basque, Maltese), polyglot chimera attacks
- Memory/session: Memory poisoning, slow-roll attacks, cumulative risk tracking, tool call validation

---

## 5. Dependencies

### 5.1 Core (Required)

```
numpy>=1.24.0
scipy>=1.11.0
scikit-learn>=1.3.0
pyyaml>=6.0
blake3>=0.3.0
requests>=2.31.0
psycopg[binary]>=3.1.0
redis>=5.0.0
pydantic>=2.0.0
psutil>=5.9.0
cryptography>=41.0.0
```

**Functionality:** Unicode normalization, pattern matching, risk scoring, basic firewall operations.

### 5.2 Optional (ML Features)

```
sentence-transformers>=2.2.0  # SemanticVectorCheck, embedding-based detection
torch>=2.0.0                  # ML model inference
transformers>=4.30.0          # ML model inference
onnx>=1.14.0                  # ONNX model support
onnxruntime>=1.16.0           # ONNX model support
```

**Functionality:** Advanced semantic detection, Kids Policy Engine, embedding-based anomaly detection.

**Note:** Core functionality works without optional dependencies. Advanced features require optional ML dependencies. Graceful degradation: missing dependencies result in disabled features, not errors.

### 5.3 System Requirements

- **Python:** >=3.12 (by design, no legacy support for 3.10/3.11)
- **RAM:** ~300MB for core functionality, ~1.3GB for adversarial inputs with full ML features
- **GPU:** Optional, only required for certain ML-based detectors
- **Redis:** Optional but recommended for caching (local or cloud)

---

## 6. Known Limitations

### 6.1 False Positive Rate

**Kids Policy:** False positive rate is approximately 20-25% in production scenarios (target: <5%). Test suite shows 0.0% due to limited test coverage. This is a known limitation documented in README.

### 6.2 Memory Usage

Current memory usage exceeds 300MB cap for adversarial inputs (measured: ~1.3GB). This is a known limitation documented in README.

### 6.3 Optional Dependencies

Some detection features require optional ML dependencies. Core functionality works without them, but advanced semantic detection and Kids Policy Engine are disabled. No runtime warnings when optional features are unavailable (TODO: add warnings).

### 6.4 Python Version

Requires Python >=3.12. No support for 3.10/3.11. This is a design decision, not a technical limitation.

### 6.5 Unicode Normalization

Some edge cases in mathematical alphanumeric symbol handling remain. Documented in Known Limitations section.

### 6.6 Test Coverage

Some v2.4.0 features (Hexagonal Architecture, Developer API, LangChain integration) lack dedicated test coverage. Adversarial tests focus on security fixes. AnswerPolicy tests are basic; comprehensive empirical evaluation pending.

### 6.7 AnswerPolicy Limitations

1. **Heuristic p_correct:** `p_correct = 1.0 - base_risk_score` is not calibrated. Future work: calibrated probability model.
2. **Missing Metadata:** Some decision paths do not log AnswerPolicy metadata (26/50 decisions in v0.1 experiment). Investigation needed.
3. **No Block Source Distinction:** `FirewallDecision` metadata does not explicitly flag blocks by AnswerPolicy vs. other reasons. Enhancement: add `"blocked_by_answer_policy": bool` flag.
4. **Limited Empirical Evaluation:** v0.1 experiment (50 prompts) is insufficient for statistical significance. Expansion needed.

---

## 7. Open Questions / TODOs

### 7.1 Development Status Metadata

`pyproject.toml` still shows `Development Status :: 4 - Beta`. Consider updating to `5 - Production/Stable` in future release (requires version bump).

### 7.2 Feature Detection

Add runtime warnings when optional features are unavailable (e.g., when `sentence-transformers` is missing but SemanticVectorCheck is requested).

### 7.3 Dependency Management

Consider splitting package into `llm-security-firewall-core` and `llm-security-firewall-full` to reduce installation size for users who only need core functionality.

### 7.4 Detection Coverage

Improve pattern matching for obvious malicious queries that currently require optional dependencies (e.g., "Wie hacke ich ein Passwort?").

### 7.5 Performance Optimization

Address memory usage exceeding 300MB cap for adversarial inputs.

### 7.6 Test Coverage

Add dedicated tests for Hexagonal Architecture components, Developer API, and LangChain integration.

### 7.7 Documentation

Consider adding production deployment guide and migration guide from previous versions.

### 7.8 AnswerPolicy Enhancements

1. **Calibrated p_correct Estimator:** Replace heuristic with calibrated probability model using Dempster-Shafer masses, CUSUM status, embedding-based anomaly scores.
2. **Block Source Distinction:** Add explicit flag in `FirewallDecision.metadata` to distinguish blocks by AnswerPolicy vs. other reasons.
3. **Missing Metadata Investigation:** Ensure AnswerPolicy metadata is always logged in all decision paths.
4. **Empirical Evaluation:** Expand v0.1 experiment to 500-1000 prompts, measure ASR/FPR impact, compare policies.

---

## 8. Documentation

### 8.1 User Documentation

- **README.md:** Professional restructuring for PyPI, includes TL;DR, minimal example, dependencies, system requirements, security notice
- **ARCHITECTURE.md:** Hexagonal architecture, security layers, AnswerPolicy section
- **CHANGELOG.md:** Version history, changes per release

### 8.2 Developer Documentation

- **docs/ANSWER_POLICY_INTEGRATION.md:** AnswerPolicy integration guide, mathematical foundation, usage examples
- **docs/ANSWER_POLICY_DESIGN_DECISIONS.md:** Design decisions, rationale, future enhancements
- **docs/ANSWER_POLICY_METRICS.md:** Metrics analysis pipeline, usage, limitations
- **docs/ANSWER_POLICY_EXPERIMENTS.md:** Experiment plans, goals, execution steps

### 8.3 Release Documentation

- **docs/FINAL_VALIDATION_REPORT_2025_12_02.md:** Test results and validation summary
- **docs/PRODUCTION_RELEASE_SUMMARY_2025_12_02.md:** Release confirmation and metrics
- **docs/PYPI_RELEASE_HANDOVER_2025_12_02.md:** Test-PyPI release process documentation
- **docs/HANDOVER_2025_12_02.md:** Scientific handover document (previous version)
- **docs/HANDOVER_2025_12_02_COMPREHENSIVE.md:** This document

---

## 9. Git Status

**Current Branch:** `main`  
**Latest Commit:** `23d7b2e` - "docs: Restructure README.md according to professional PyPI standards"  
**Git Tag:** `v2.4.0` (created and pushed)  
**Remote:** https://github.com/sookoothaii/llm-security-firewall

**Recent Commits:**
- `23d7b2e`: README.md restructuring
- `dcd42bb`: Production release 2.4.0
- `ad3b34f`: Various fixes and documentation updates

**Files Modified in Recent Work:**
- Documentation: `README.md`, `docs/*.md`
- Configuration: `pyproject.toml`, `src/llm_firewall/__init__.py`, `CHANGELOG.md`
- AnswerPolicy: `src/llm_firewall/core/decision_policy.py`, `src/llm_firewall/core/policy_provider.py`, `config/answer_policy.yaml`, `src/llm_firewall/core/firewall_engine_v2.py`
- Scripts: `scripts/run_simple_experiment.py`, `scripts/analyze_answer_policy_metrics.py`
- Tests: `tests/core/test_answer_policy.py`, `tests/core/test_answer_policy_integration.py`, `tests/scripts/test_analyze_answer_policy_metrics.py`

---

## 10. Validation Results

### 10.1 Pre-Release Testing

- **Adversarial security tests:** 4/4 passed (100%)
- **Unicode hardening tests:** 9/9 passed (100%)
- **API functionality:** Core API tested and working
- **Package build:** `twine check` passed

### 10.2 Post-Release Validation

- **Installation from PyPI:** Successful
- **Version verification:** Correct (2.4.0)
- **API test:** `guard.check_input()` functional
- **Core dependencies:** All installed correctly

### 10.3 AnswerPolicy Validation

- **Unit tests:** Basic coverage exists, all passing
- **Integration tests:** Basic coverage exists, all passing
- **Experimental evaluation:** v0.1 completed, preliminary results available
- **Metrics analysis:** Script functional, test coverage exists

---

## 11. References

- **PyPI Package:** https://pypi.org/project/llm-security-firewall/2.4.0/
- **GitHub Repository:** https://github.com/sookoothaii/llm-security-firewall
- **Previous Handover:** `docs/HANDOVER_2025_12_02.md`
- **Validation Report:** `docs/FINAL_VALIDATION_REPORT_2025_12_02.md`
- **Release Summary:** `docs/PRODUCTION_RELEASE_SUMMARY_2025_12_02.md`
- **AnswerPolicy Integration:** `docs/ANSWER_POLICY_INTEGRATION.md`
- **AnswerPolicy Design Decisions:** `docs/ANSWER_POLICY_DESIGN_DECISIONS.md`
- **AnswerPolicy Metrics:** `docs/ANSWER_POLICY_METRICS.md`
- **AnswerPolicy Experiments:** `docs/ANSWER_POLICY_EXPERIMENTS.md`

---

## 12. Next Steps

### 12.1 Immediate (Production)

1. Monitor production usage for security issues
2. Address any critical bugs reported by users
3. Maintain backward compatibility for v2.4.0

### 12.2 Short-Term (AnswerPolicy)

1. Investigate missing metadata in decision logs (ensure AnswerPolicy metadata is always logged)
2. Expand v0.1 experiment to 500-1000 prompts
3. Measure ASR/FPR impact on red-team suite
4. Add explicit block source distinction in `FirewallDecision.metadata`

### 12.3 Medium-Term (Enhancements)

1. Implement calibrated p_correct estimator
2. Add runtime warnings for missing optional features
3. Improve test coverage (Hexagonal Architecture, Developer API, LangChain integration)
4. Consider package split (core vs. full)

### 12.4 Long-Term (Research)

1. Empirical evaluation of AnswerPolicy across multiple policies
2. Parameter sensitivity analysis
3. Integration with Dempster-Shafer and CUSUM for uncertainty modeling
4. Publication of results (if applicable)

---

**Document Generated:** 2025-12-02  
**Next Review:** Address open questions and TODOs as needed, expand AnswerPolicy empirical evaluation









