GAISSF to OWASP LLM Top 10 Crosswalk
Public crosswalk publication mapping GAISSF v1.0 to OWASP LLM Top 10, with scope, method, limitations and traceability.
Document control and source baseline
| Source | Version / date | Role |
|---|---|---|
| GAISSF Framework Standard | GAISSF-NOR-001 v1.0 | Normative framework and conformance baseline |
| GAISSF Control Catalogue | GAISSF-NOR-004 v1.0 | Authoritative 59-control source |
| OWASP Top 10 for LLM Applications | 2025 edition; 17 November 2024 | Ten risk categories used as external mapping targets |
1. Executive summary
This crosswalk evaluates all 59 GAISSF v1.0 controls against the ten OWASP Top 10 for LLM Applications 2025 risk categories. It contains 59 forward mapping records and a complete ten-risk reverse coverage register.
GAISSF supplies a broader auditable control architecture across model integrity, runtime security, agents, supply chain, content safety, governance, societal harm, regulatory alignment, and physical AI. OWASP supplies a concise vulnerability-oriented prioritization resource. The two artefacts are complementary but not equivalent.
2. Purpose, scope and intended use
Support AI application threat modelling and control selection.
Identify reusable GAISSF evidence for OWASP-oriented reviews.
Expose residual implementation and testing gaps.
Support assessment planning without claiming OWASP certification or vulnerability elimination.
3. Mapping methodology
Mappings were made at GAISSF control level to OWASP risk-category level. Decisions considered shared attack surface, preventive and detective outcomes, lifecycle position, evidence expectations, and residual application-specific work. Similar terminology alone was not sufficient.
| Code | Meaning |
|---|---|
| SP | Strong partial: substantial risk-reduction relationship; material implementation work remains. |
| P | Partial: meaningful overlap with unmatched elements. |
| S | Supporting: indirect or enabling contribution. |
| N | No material relationship. |
| O | Outside scope. |
| U | Unable to determine. |
4. OWASP 2025 risk inventory
| Identifier | Risk | Working summary |
|---|---|---|
| LLM01:2025 | Prompt Injection | Crafted direct or indirect inputs can alter model behavior, bypass instructions, expose data, or trigger unauthorized actions. |
| LLM02:2025 | Sensitive Information Disclosure | Models and applications can reveal confidential, personal, proprietary, credential, or otherwise sensitive information. |
| LLM03:2025 | Supply Chain | Compromised models, datasets, components, services, dependencies, or provenance can undermine application security and integrity. |
| LLM04:2025 | Data and Model Poisoning | Manipulated pre-training, fine-tuning, retrieval, embedding, or model data can introduce backdoors, bias, unsafe behavior, or integrity failures. |
| LLM05:2025 | Improper Output Handling | Insufficient validation, sanitization, encoding, or contextual handling of model outputs can create downstream exploits. |
| LLM06:2025 | Excessive Agency | Excessive functionality, permissions, autonomy, or insufficient human approval can allow harmful or unintended actions. |
| LLM07:2025 | System Prompt Leakage | System prompts or embedded instructions may be exposed and reveal sensitive logic, controls, or operational context. |
| LLM08:2025 | Vector and Embedding Weaknesses | Weaknesses in retrieval, vector stores, embeddings, access controls, segmentation, and data provenance can cause disclosure or manipulation. |
| LLM09:2025 | Misinformation | Incorrect, fabricated, misleading, or insufficiently verified model outputs can undermine decisions and safety. |
| LLM10:2025 | Unbounded Consumption | Uncontrolled model, token, tool, compute, or service consumption can cause denial of service, cost escalation, degradation, or theft-related abuse. |
5. Reverse coverage register
| OWASP risk | Mapped GAISSF controls | Coverage | Confidence | Residual limitation |
|---|---|---|---|---|
| LLM01:2025 Prompt Injection | D1-CTL-03, D1-CTL-05, D1-CTL-06, D1-CTL-08, D2-CTL-01, D2-CTL-02, D2-CTL-03, D2-CTL-04, D2-CTL-05, D2-CTL-06, D6-CTL-02, D6-CTL-03 | Indirectly Supported | Low | OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation. |
| LLM02:2025 Sensitive Information Disclosure | D5-CTL-05 | Indirectly Supported | Low | OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation. |
| LLM03:2025 Supply Chain | D4-CTL-01, D4-CTL-02, D4-CTL-03, D4-CTL-04, D4-CTL-05, D4-CTL-06, D4-CTL-07, D6-CTL-06, D8-CTL-02 | Indirectly Supported | Low | OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation. |
| LLM04:2025 Data and Model Poisoning | D1-CTL-01, D1-CTL-04, D1-CTL-07, D1-CTL-09, D5-CTL-01, D5-CTL-02, D5-CTL-03, D5-CTL-04, D5-CTL-06, D8-CTL-03, D9-CTL-07 | Indirectly Supported | Low | OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation. |
| LLM05:2025 Improper Output Handling | Indirectly Supported | Low | OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation. | |
| LLM06:2025 Excessive Agency | D3-CTL-01, D3-CTL-02, D3-CTL-03, D3-CTL-04, D3-CTL-05, D3-CTL-06, D3-CTL-07, D6-CTL-01, D9-CTL-02 | Indirectly Supported | Low | OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation. |
| LLM07:2025 System Prompt Leakage | Indirectly Supported | Low | OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation. | |
| LLM08:2025 Vector and Embedding Weaknesses | D7-CTL-H01 | Indirectly Supported | Low | OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation. |
| LLM09:2025 Misinformation | D7-CTL-H03, D7-CTL-H04 | Indirectly Supported | Low | OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation. |
| LLM10:2025 Unbounded Consumption | D1-CTL-02, D6-CTL-04, D6-CTL-07, D7-CTL-H02 | Indirectly Supported | Low | OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation. |
6. Risk-by-risk analysis
LLM01:2025 Prompt Injection
Crafted direct or indirect inputs can alter model behavior, bypass instructions, expose data, or trigger unauthorized actions.
Primary GAISSF controls: D1-CTL-03, D1-CTL-05, D1-CTL-06, D1-CTL-08, D2-CTL-01, D2-CTL-02, D2-CTL-03, D2-CTL-04, D2-CTL-05, D2-CTL-06, D6-CTL-02, D6-CTL-03
Assessment conclusion: Indirectly Supported; confidence Low.
Residual gap: OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation.
LLM02:2025 Sensitive Information Disclosure
Models and applications can reveal confidential, personal, proprietary, credential, or otherwise sensitive information.
Primary GAISSF controls: D5-CTL-05
Assessment conclusion: Indirectly Supported; confidence Low.
Residual gap: OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation.
LLM03:2025 Supply Chain
Compromised models, datasets, components, services, dependencies, or provenance can undermine application security and integrity.
Primary GAISSF controls: D4-CTL-01, D4-CTL-02, D4-CTL-03, D4-CTL-04, D4-CTL-05, D4-CTL-06, D4-CTL-07, D6-CTL-06, D8-CTL-02
Assessment conclusion: Indirectly Supported; confidence Low.
Residual gap: OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation.
LLM04:2025 Data and Model Poisoning
Manipulated pre-training, fine-tuning, retrieval, embedding, or model data can introduce backdoors, bias, unsafe behavior, or integrity failures.
Primary GAISSF controls: D1-CTL-01, D1-CTL-04, D1-CTL-07, D1-CTL-09, D5-CTL-01, D5-CTL-02, D5-CTL-03, D5-CTL-04, D5-CTL-06, D8-CTL-03, D9-CTL-07
Assessment conclusion: Indirectly Supported; confidence Low.
Residual gap: OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation.
LLM05:2025 Improper Output Handling
Insufficient validation, sanitization, encoding, or contextual handling of model outputs can create downstream exploits.
Primary GAISSF controls:
Assessment conclusion: Indirectly Supported; confidence Low.
Residual gap: OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation.
LLM06:2025 Excessive Agency
Excessive functionality, permissions, autonomy, or insufficient human approval can allow harmful or unintended actions.
Primary GAISSF controls: D3-CTL-01, D3-CTL-02, D3-CTL-03, D3-CTL-04, D3-CTL-05, D3-CTL-06, D3-CTL-07, D6-CTL-01, D9-CTL-02
Assessment conclusion: Indirectly Supported; confidence Low.
Residual gap: OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation.
LLM07:2025 System Prompt Leakage
System prompts or embedded instructions may be exposed and reveal sensitive logic, controls, or operational context.
Primary GAISSF controls:
Assessment conclusion: Indirectly Supported; confidence Low.
Residual gap: OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation.
LLM08:2025 Vector and Embedding Weaknesses
Weaknesses in retrieval, vector stores, embeddings, access controls, segmentation, and data provenance can cause disclosure or manipulation.
Primary GAISSF controls: D7-CTL-H01
Assessment conclusion: Indirectly Supported; confidence Low.
Residual gap: OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation.
LLM09:2025 Misinformation
Incorrect, fabricated, misleading, or insufficiently verified model outputs can undermine decisions and safety.
Primary GAISSF controls: D7-CTL-H03, D7-CTL-H04
Assessment conclusion: Indirectly Supported; confidence Low.
Residual gap: OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation.
LLM10:2025 Unbounded Consumption
Uncontrolled model, token, tool, compute, or service consumption can cause denial of service, cost escalation, degradation, or theft-related abuse.
Primary GAISSF controls: D1-CTL-02, D6-CTL-04, D6-CTL-07, D7-CTL-H02
Assessment conclusion: Indirectly Supported; confidence Low.
Residual gap: OWASP risk exposure depends on architecture, implementation, threat context, testing depth, and operational effectiveness; documentary mapping alone cannot demonstrate mitigation.
7. Evidence reuse guidance
Threat models and abuse-case registers
Prompt-injection and adversarial test results
Data classification and disclosure-control records
Supplier, model, dataset, and component provenance records
Poisoning and integrity validation reports
Output-validation and downstream-execution controls
Agent tool inventories, permission models, and human-approval records
RAG, vector-store, embedding, and tenant-isolation tests
Accuracy, grounding, provenance, and human-review evidence
Rate-limit, quota, cost-monitoring, availability, and abuse-detection records
8. Limitations
Mapping is not OWASP endorsement, certification, equivalence, or proof of secure implementation.
OWASP Top 10 is a prioritized risk-awareness resource, not a complete security standard or exhaustive threat catalogue.
GAISSF controls can support multiple risks; actual mitigation depends on architecture, implementation, testing, monitoring, and evidence.
The crosswalk does not replace application threat modelling, penetration testing, red teaming, or secure software engineering.
Revalidation is required when GAISSF or OWASP source baselines change.
9. Notably absent
No OWASP certification or endorsement mechanism for this crosswalk was identified.
No basis exists for claiming that documentary mapping proves a vulnerability is absent.
No basis exists for treating the Top 10 as an exhaustive security standard.
No universal compliance percentage, safe harbour, or automatic regulatory conclusion is asserted.
Annex A - Complete GAISSF-to-OWASP mapping register
| Record | GAISSF control | OWASP risk | Rel. | Confidence | Rationale | Residual gap |
|---|---|---|---|---|---|---|
| CRO024-MAP-0001 | D1-CTL-01 Dataset Provenance & Poisoning Prevention | LLM04:2025 Data and Model Poisoning | S | Low | GAISSF D1-CTL-01 establishes AI-system controls or evidence that materially reduce exposure to LLM04:2025 Data and Model Poisoning. The relationship is preventive or detective support; it is not proof that the vulnerability is absent. | Application-specific threat modelling, adversarial testing, implementation verification, and operating-effectiveness evidence remain necessary. |
| CRO024-MAP-0002 | D1-CTL-02 Model Extraction Resistance | LLM10:2025 Unbounded Consumption | S | Low | GAISSF D1-CTL-02 establishes AI-system controls or evidence that materially reduce exposure to LLM10:2025 Unbounded Consumption. The relationship is preventive or detective support; it is not proof that the vulnerability is absent. | Application-specific threat modelling, adversarial testing, implementation verification, and operating-effectiveness evidence remain necessary. |
| CRO024-MAP-0003 | D1-CTL-03 Behavioral Drift Detection | LLM01:2025 Prompt Injection | S | Low | GAISSF D1-CTL-03 establishes AI-system controls or evidence that materially reduce exposure to LLM01:2025 Prompt Injection. The relationship is preventive or detective support; it is not proof that the vulnerability is absent. | Application-specific threat modelling, adversarial testing, implementation verification, and operating-effectiveness evidence remain necessary. |
| CRO024-MAP-0004 | D1-CTL-04 Federated Learning Poisoning Prevention | LLM04:2025 Data and Model Poisoning | S | Low | GAISSF D1-CTL-04 establishes AI-system controls or evidence that materially reduce exposure to LLM04:2025 Data and Model Poisoning. The relationship is preventive or detective support; it is not proof that the vulnerability is absent. | Application-specific threat modelling, adversarial testing, implementation verification, and operating-effectiveness evidence remain necessary. |
| CRO024-MAP-0005 | D1-CTL-05 Embedding Space Robustness | LLM01:2025 Prompt Injection | S | Low | GAISSF D1-CTL-05 establishes AI-system controls or evidence that materially reduce exposure to LLM01:2025 Prompt Injection. The relationship is preventive or detective support; it is not proof that the vulnerability is absent. | Application-specific threat modelling, adversarial testing, implementation verification, and operating-effectiveness evidence remain necessary. |
| CRO024-MAP-0006 | D1-CTL-06 Post-Quantum Model Signing & Crypto Hardening | LLM01:2025 Prompt Injection | S | Low | GAISSF D1-CTL-06 establishes AI-system controls or evidence that materially reduce exposure to LLM01:2025 Prompt Injection. The relationship is preventive or detective support; it is not proof that the vulnerability is absent. | Application-specific threat modelling, adversarial testing, implementation verification, and operating-effectiveness evidence remain necessary. |
Annex B - Change and verification record
| Item | Status |
|---|---|
| GAISSF controls inventoried | 59 |
| OWASP 2025 risks inventoried | 10 |
| Forward mappings | 59 |
| Reverse records | 10 |
| Old 2023/24 numbering mixed into baseline | No |
| Independent SME approval | Required before final publication |