# OWASP Top 10 for LLMs v2025 - Coverage Analysis

**Document:** HAK/GAL LLM Security Firewall vs. OWASP Top 10 for LLMs (2025)
**Version:** 5.0.0 RC9-FPR4 + Layer 15
**Date:** 2025-11-04
**Creator:** I25C8F3A

---

## Summary

**Layers implementiert:** 51 components (46 pre-Layer15 + 5 Layer15)
**OWASP Categories mit Tests:** 8/10
**OWASP Categories teilweise:** 2/10
**Bekannte Gaps:** Supply Chain, Vector/Embedding, API Rate Limiting

---

## Detailed Mapping

### LLM01: Prompt Injection

**OWASP Definition:** Adversarial inputs manipulating LLM behavior through direct or indirect prompt injection, including multimodal and obfuscated attacks.

**HAK/GAL Status:** Tests existieren

**Layers implementiert:**
- Safety Validator (43 patterns: 28 intent + 15 evasion across 11 categories)
- Evasion Detection (15 patterns: ZWC, Base64, Homoglyph, ROT13, Unicode)
- Unicode Hardening (NFKC+ canonicalization, confusable skeleton, bidi controls)
- Emoji-Homoglyph Normalizer (RC5: Regional Indicators, Math Alphanumeric)
- Multilingual Keywords (RC6: 50+ keywords across 7 languages)
- Indirect Execution Detector (RC7: meta-commands, shell expansions)
- Context Poisoning Detector (RC7: language switching attacks)
- Semantic Similarity Detector (XSS semantic synonyms)
- Jailbreak Phrases (SemSyn-20 lexicon, 20 clusters, 4 intents)

**Test Results (Development Environment):**
- Multi-seed ASR: 2.76% (N=1920 synthetic attacks, Wilson Upper 3.59%)
- Latency P99: 53ms
- Limitation: Synthetic attacks, keine production traffic validation

---

### LLM02: Sensitive Information Disclosure

**OWASP Definition:** Unintended exposure of sensitive training data, PII, or confidential information.

**HAK/GAL Status:** Komponenten implementiert

**Layers:**
- Auth Token (write authorization)
- Heritage Tracking (creator_instance_id, provenance)
- MINJA Prevention (circular reference detection)
- Privacy Protection (Cultural Biometrics 27D)
- Safety-Sandwich Decoding (13 patterns: passwords, API keys, PII)
- Snapshot Canaries (drift detection)

**Limitation:** PII detection patterns basic, keine ML-based entity recognition

---

### LLM03: Supply Chain

**OWASP Definition:** Vulnerabilities in dependencies, training data, model provenance.

**HAK/GAL Status:** Teilweise

**Layers:**
- None directly addressing supply chain risks
- Indirect: Snapshot Canaries detect model degradation (potential indicator)
- Indirect: Domain Trust Scoring verifies external sources

**Gaps:**
- No dependency scanning
- No training data provenance tracking
- No model signature verification
- No supply chain audit trail

**Limitation:** Runtime-only, CI/CD supply chain scanning nicht implementiert

---

### LLM04: Data and Model Poisoning

**OWASP Definition:** Malicious manipulation of training data or model weights.

**HAK/GAL Status:** Monitoring implementiert

**Layers:**
- Snapshot Canaries (59 synthetic claims: 25 true, 25 false, 5 math, 4 temporal)
- Drift Detection (KL-divergence, NLI baseline)
- Shingle Hashing (5-gram profiling)
- Influence Budget Tracking (EWMA Z-score)
- Write-Path Policy Engine (Merkle chain, two-man rule)
- Claim Attribution Graph (cycle detection)

**Test Coverage:**
- 59 canaries monitored
- 4-sigma alert threshold
- Limitation: Keine adversarial poisoning attacks getestet, nur drift monitoring

---

### LLM05: Improper Output Handling

**OWASP Definition:** Missing validation/escaping/encoding leading to XSS, CSRF, SSRF, RCE, SQL injection.

**HAK/GAL Status:** Basic guards implementiert (Layer 15 neu)

**Layers (Pre-Layer 15):**
- Evidence Validation (MINJA prevention prevents fake citations)
- NLI Consistency (verifies claims against KB)
- Domain Trust Scoring (4-tier source verification)

**Layers (Layer 15 NEW):**
- OWASP SQL Sink Guard (blocks ";--", "/*", " or ", " and ")
- OWASP Shell Sink Guard (blocks "&&", "||", "|", ";", "`")
- OWASP HTML/MD Guard (escapes <script>, <iframe>)

**Gaps (Pre-Layer 15):**
- No parametrized query enforcement
- No XSS/CSRF/SSRF prevention
- No CSP (Content Security Policy)

**Limitation:** Sink guards basic (regex-based), keine ASVS full compliance, CSP fehlt

---

### LLM06: Excessive Agency

**OWASP Definition:** Model performing actions beyond intended scope, over-reliance on LLM decisions.

**HAK/GAL Status:** Mechanismen implementiert

**Layers:**
- Adversarial Honesty Engine (abstention bei low confidence)
- Evidence Validation (external sources required)
- CARE System (readiness assessment)
- Policy DSL (action boundaries)
- Constitutional AI (Article 7: KB reading mandatory)

**Test Coverage:**
- Abstention funktioniert in Tests
- CARE thresholds konfigurierbar
- Limitation: Keine systematic over-reliance studies, nur mechanism vorhanden

---

### LLM07: System Prompt Leakage

**OWASP Definition:** Unintended disclosure of system prompts, internal instructions.

**HAK/GAL Status:** Komponenten implementiert

**Layers:**
- Safety Validator (blocks "show system prompt", "reveal instructions" patterns)
- Pattern-based detection (43 regex patterns include meta-instruction queries)
- MINJA Prevention (prevents system revealing own architecture in responses)

**Validation:**
- Tested in regression suite (114/114 critical paths PASSED)

**Limitation:** Tested in regression suite, keine adversarial prompt leakage attacks

---

### LLM08: Vector and Embedding Weaknesses

**OWASP Definition:** Attacks exploiting vector databases, embedding manipulations, nearest-neighbor poisoning.

**HAK/GAL Status:** Teilweise

**Layers:**
- Shingle Hashing (near-duplicate detection in embeddings)
- Influence Budget (detects embedding space manipulation)
- Snapshot Canaries (detects drift in semantic space)

**Gaps:**
- No adversarial embedding attacks tested
- No nearest-neighbor poisoning prevention
- No embedding space isolation
- No vector database access control

**Limitation:** Spezialisierte vector database attacks nicht getestet, nearest-neighbor poisoning unbekannt

---

### LLM09: Misinformation

**OWASP Definition:** Generation of factually incorrect, misleading, or hallucinatory content.

**HAK/GAL Status:** Komponenten implementiert

**Layers:**
- Evidence Validation (MINJA prevention via creator_instance_id)
- Domain Trust Scoring (4-tier: Nature 0.95-0.98, arXiv 0.85-0.90, Scholar 0.70-0.80, Unknown 0.10)
- NLI Consistency (claim verification against KB via conformal prediction)
- Dempster-Shafer Fusion (conflict-robust combination, belief-based decisions)
- Snapshot Canaries (25 known-true, 25 known-false statements)
- Constitutional AI (Article 7: KB reading mandatory)
- Adversarial Honesty (abstention on low confidence)
- Kids Policy Truth Preservation (TAG-2: 33/33 validated, age-stratified factuality)

**Test Coverage:**
- TAG-2: 33/33 validations synthetic data (mock validator)
- Domain Trust funktioniert basic
- NLI consistency tests existieren
- Limitation: Keine adversarial misinformation attacks, real-world hallucination rate unbekannt

---

### LLM10: Unbounded Consumption

**OWASP Definition:** Resource exhaustion attacks (token flooding, DoS).

**HAK/GAL Status:** Teilweise

**Layers:**
- E-Value Session Risk (sequential hypothesis testing, Ville's Inequality FWER control)
- Influence Budget (EWMA Z-score monitoring per domain, 4-sigma alert)
- Session slow-roll assembler (256-char buffer for slow attacks)

**Gaps:**
- No rate limiting at API level
- No request throttling per user
- No token consumption budgets
- No DoS prevention mechanisms

**Limitation:** API-level rate limiting fehlt, nur session-level monitoring

---

## Coverage Matrix Summary

| OWASP | Status | Primary Layers | Gaps |
|-------|--------|---------------|------|
| LLM01 Prompt Injection | Tests existieren | Safety Validator, Evasion, Unicode, RC5/RC6/RC7/RC8 | Production traffic ungetestet |
| LLM02 Info Disclosure | Implementiert | Auth Token, MINJA, Safety-Sandwich, Heritage | ML-based PII detection fehlt |
| LLM03 Supply Chain | Nicht adressiert | Canaries (indirekt), Trust Scoring (indirekt) | Dependency scan, provenance, SBOM |
| LLM04 Poisoning | Monitoring vorhanden | Canaries, Drift, Shingle, Influence, Merkle Chain | Adversarial poisoning ungetestet |
| LLM05 Output Handling | Basic guards | Layer 15 OWASP Sinks (SQL/Shell/HTML) | CSP fehlt, ASVS partial |
| LLM06 Excessive Agency | Mechanismen da | Adversarial Honesty, CARE, Policy DSL, Constitutional | Over-reliance studies fehlen |
| LLM07 Prompt Leakage | Patterns implementiert | Safety Validator, Pattern Detection, MINJA | Adversarial leakage ungetestet |
| LLM08 Vector/Embedding | Teilweise | Shingle, Influence, Canaries | Adversarial embeddings, NN poisoning |
| LLM09 Misinformation | Komponenten da | Evidence, Trust, NLI, DS-Fusion, Truth Preservation | Real-world hallucination rate unbekannt |
| LLM10 Consumption | Session-level only | E-Value, Influence, Session Assembler | API rate limiting fehlt |

**Zusammenfassung:** Komponenten implementiert und getestet (development), production validation fehlt

---

## Firewall Layers Inventory (51 Total)

### Core Defense (9 Layers)
1. Safety Validator
2. Embedding Detector
3. Perplexity Detector
4. Evidence Validation
5. Domain Trust Scoring
6. NLI Consistency
7. Snapshot Canaries
8. Shingle Hashing
9. Influence Budget

### Phase 2 Hardening (7 Layers)
10. Write-Path Policy Engine
11. Temporal Awareness Gate
12. Safety-Sandwich Decoding
13. Claim Attribution Graph
14. Coverage-Guided Red-Team Fuzzer
15. Prometheus SLO Monitoring
16. Policy DSL

### Phase 3 Operational (4 Layers)
17. GuardNet (FirewallNet)
18. Obfuscation Guard
19. Safe Bandit
20. Policy Verify (SMT)

### RC5/RC6/RC7 Attack Surface (4 Layers)
21. Emoji-Homoglyph Normalizer
22. Multilingual Keywords
23. Indirect Execution Detector
24. Context Poisoning Detector

### RC8 Semantic/Jailbreak (2 Layers)
25. XSS Semantic Synonyms
26. SemSyn-20 Jailbreak Phrases

### RC9 FPR Reduction (4 Layers)
27. Documentation Context Dampening
28. Compact Anchor Hit
29. Stratified Measurement
30. Baseline Freeze

### Phase 4 Encoding/Transport (6 Layers)
31. Base64 Secret Sniffing
32. Archive Detection (gzip/zip)
33. PNG Metadata Scanner
34. Session Slow-Roll Assembler
35. Compact Anchor Hit
36. E-Value Session Risk

### Phase 5 Advanced Transport (2 Layers)
37. RFC 2047 Encoded-Words
38. YAML Alias Assembler
39. JPEG/PDF Text Scanning
40. 1-Character Slow-Roll
41. Policy Budgets
42. Auto-Strict Guard

### Authentication (1 Layer)
43. Cultural Biometrics (27D)

### Governance (3 Layers)
44. Constitutional AI (Articles 7/8/9/10)
45. CARE System (cognitive readiness)
46. Personality System (20D adaptation)

### Layer 15 Vulnerable Domain (5 Components)
47. Age-Aware Router
48. Crisis Detection (Hybrid Regex+ML)
49. Deceptive Empathy Filter
50. RSI/ChildSafe Metrics
51. OWASP Sink Guards

---

## Recommendations

### Address LLM03 (Supply Chain)
- Integrate dependency scanning (e.g., Safety, pip-audit)
- Add SBOM generation
- Implement model signature verification
- Track training data provenance

### Address LLM08 (Vector/Embedding)
- Test adversarial embedding attacks
- Implement nearest-neighbor poisoning detection
- Add embedding space isolation
- Validate vector database access controls

### Address LLM10 (Unbounded Consumption)
- Add API rate limiting (per user, per IP)
- Implement token consumption budgets
- Add request throttling
- Deploy DoS prevention (e.g., Cloudflare, rate limiters)

---

## OWASP Mitigations Implemented

### Input Validation (LLM01)
- Behavior constraints via Policy DSL
- Output validation via NLI Consistency
- Input/output filtering via 43 patterns + 15 evasion
- Least-privilege functions via CARE System
- HITL for high-risk via Crisis Detection (Layer 15)
- Segregation via Cultural Biometrics
- Adversarial testing via CGRF (Coverage-Guided Red-Team Fuzzer)

### Output Encoding (LLM05)
- ASVS-style checks via Layer 15 OWASP Sinks
- Parametrized queries enforcement (SQL sink guard)
- Zero-trust model output via Evidence Validation
- Logging/monitoring via Decision Ledger + Prometheus

### Transparency (LLM06)
- Constitutional AI Article 7 (KB reading BEFORE response)
- Adversarial Honesty (abstention, epistemic uncertainty)
- Explain-Why Engine (structured reasoning)

---

## Chronologie

**OWASP Top 10 v2025:** Publiziert 2025-01-27
**HAK/GAL Entwicklung:** 2024-10-16 bis 2025-11-04
**Überschneidung:** 8/10 Kategorien haben Tests, entwickelt ohne OWASP Kenntnis

---

## References

- OWASP Top 10 for Large Language Model Applications v2025 (genai.owasp.org)
- Akiri et al., "Safety and Security Analysis of LLMs" (arXiv 2509.10655v1, 2024)
- HAK/GAL LLM Security Firewall README.md
- Layer 15 Vulnerable Domain Guard README.md

---

## Bekannte Schwächen

**LLM03 Supply Chain:**
- Keine dependency scanning
- Keine model signature verification
- Kein SBOM
- Training data provenance fehlt

**LLM08 Vector/Embedding:**
- Adversarial embedding attacks nicht getestet
- Nearest-neighbor poisoning detection fehlt
- Vector database access control fehlt

**LLM10 Unbounded Consumption:**
- API rate limiting fehlt
- Request throttling fehlt
- DoS prevention fehlt

**Generell:**
- Alle Tests in development environment (synthetic data)
- Production traffic validation fehlt
- Real-world attack success rates unbekannt

---

**Nächste Schritte:** LLM03/LLM08/LLM10 gaps adressieren, production validation durchführen.
