CROSSWALKS

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

Controlled Publication. The table below shows a representative sample (6 of 59 total records). The complete control-by-control mapping register — full requirement-level traceability, evidence guidance and machine-readable export — is a Controlled Publication. Contact ODA3 Institute for access.
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