AI threat catalogue Supply Chain and Provenance Supply Chain
Supply Chain – Infrastructure
Attackers compromise the technical environment in which an AI system is developed and operated: cloud services, development tools, and third-party software components. This gives them access to systems, data, and models.
Description
An AI system is never built in isolation: it relies on cloud platforms, code libraries, development tools, and services from external providers. Any of these components can contain vulnerabilities or be deliberately manipulated. Attackers exploit, for example, vulnerable or counterfeit software packages, openly reachable AI servers, or unsecured container registries (storage locations for ready-to-run software packages) to slip in unnoticed. Once inside, they can take over systems and networks, extract data, or alter the model itself. The risk spans the entire chain from training to live operation and often originates outside your own organization, with a provider or supplier.
Possible impact
A compromised infrastructure can disrupt or halt the AI system and destroy the integrity of the model, so its results can no longer be relied on. Confidential data such as training data, access credentials, or customer records can leak. The fallout includes recovery and investigation costs, notification and liability exposure under the GDPR and the EU AI Act, and reputational damage with customers and partners.
Example
A development team installs a popular AI code library. Attackers have slipped a counterfeit dependency into the official package channel that silently sends credentials and system information to an external server during installation. This is exactly what happened to users of a preview build of a widely used AI framework.
Recommended mitigations (4)
Compare effects and implementation levels. Expand each entry for the additional classification and rationale.
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Use trusted suppliers
Contracts & third-party management- Effect
- Preventive
- Implementation level
- Supply chain
Classification and rationale
Complementary control typeOrganizational & process-based
Primarily a contracts and third-party management control: Selection, verifiable commitments, and audit or enforcement rights toward third parties enable the protective effect; complemented by binding workflows.
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Infrastructure hardening
Technical- Effect
- Preventive
- Implementation level
- Infrastructure
Classification and rationale
Primarily technical: Safe formats, restrictive defaults, or protective layers reduce unsafe execution paths and exploitable attack surface.
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Supply chain security audits
Contracts & third-party management- Effect
- Preventive, Detective
- Implementation level
- Supply chain
Classification and rationale
Complementary control typeOrganizational & process-based
Primarily a contracts and third-party management control: Contractual audit, information, and remediation rights enable control; binding audit workflows put those rights into practice.
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Continuous monitoring of hosting environments
Technical- Effect
- Detective
- Implementation level
- Infrastructure, Use & operations
Classification and rationale
Complementary control typeOrganizational & process-based
Primarily technical: Software or analytical tools systematically produce and evaluate measurements, deviations, or attack indicators; complemented by binding workflows.
Framework mappings
Verified locations in OWASP, NIST AI RMF, MITRE ATLAS, the EU AI Act and further frameworks. The mappings are taxonomic, not evidence of compliance.
Verified references (14)
Every reference states the framework, the exact location and the publishing organisation.
- OWASP LLM Top 10 LLM04:2026 Supply ChainLLM04:2026 Supply Chain, OWASP Top 10 for LLM Applications 2026 v1.0, pp. 27-32 (doi:10.5281/zenodo.22109015); canonical text 2026/final/LLM04_SupplyChain.md at commit 7e144e05 OWASP FoundationOriginal
- NIST AI RMF Section 2.12 Value Chain and Component IntegrationSection 2.12, p. 12 National Institute of Standards and Technology (NIST)Original
- NIST AI RMF GOVERN 6.1 GOVERN 6.1GOVERN 6.1, p. 24 National Institute of Standards and Technology (NIST)Original
- NIST AI RMF MAP 4.1 MAP 4.1MAP 4.1, p. 27 National Institute of Standards and Technology (NIST)Original
- NIST AI RMF NISTAML.05 Supply Chain AttacksTaxonomy Index, pp. x–xi; Section 3.2, pp. 41–43 National Institute of Standards and Technology (NIST)Original
- MITRE ATLAS AML.T0010 AI Supply Chain CompromiseATLAS-2026.09.yaml technique object with id AML.T0010 (content release 2026.09, format-version 6.0.0) MITREOriginal
- MITRE ATLAS AML.T0132 Misconfigured or Publicly Exposed AI ServicesATLAS-2026.09.yaml technique object with id AML.T0132 (content release 2026.09, format-version 6.0.0) MITREOriginal
- EU AI Act Article 25(4) Responsibilities along the AI value chainArticle 25(4), first subparagraph, as replaced by Article 1(12)(b) of Regulation (EU) 2026/1744 European Union (EUR-Lex)Original
- EU AI Act Article 53(1)(a) Obligations for providers of general-purpose AI modelsArticle 53(1)(a) and Annex XI European Union (EUR-Lex)Original
- EU AI Act Article 55(1)(d) Obligations of providers of general-purpose AI models with systemic riskArticle 55(1)(d) European Union (EUR-Lex)Original
- BSI R1 Abhängigkeit vom entwickelnden/betreibenden Unternehmen (Text, Bild, Video)Kap. 4, R1, p. 13 Bundesamt für Sicherheit in der Informationstechnik (BSI)Original
- BIML BIML-LLM inference:9 HostingPDF p. 19, [inference:9:hosting] Berryville Institute of Machine Learning (BIML)Original
- BIML BIML78 inference:4 HostingPDF p. 20, [inference:4:hosting] Berryville Institute of Machine Learning (BIML)Original
- BIML BIML78 raw:3 StoragePDF p. 10, [raw:3:storage] Berryville Institute of Machine Learning (BIML)Original
EU AI Act obligations related to this threat
The obligations from the obligations compass whose articles the catalogue maps to this threat. Each leads to its entry with core, action step and evidence; whether it applies to you depends on role and risk class.
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Support downstream providers
Art. 25 Abs. 2, Art. 25 Abs. 4 · applies from Annex III: 2 December 2027 · Annex I: 2 August 2028 · amended by Reg. 2026/1744
Statutory roles: Provider, Provider of a general-purpose AI model. Classes: High-risk under Annex III, High-risk as a product or safety component.
Regulation (EU) 2026/1744 spelled out the initial provider duty to cooperate and at the same time created an exception: it does not apply where the initial provider has clearly specified that their system must not be turned into a high-risk system. Infringements of Art. 25(2) and (4) are now expressly subject to fines.
This reference is a review prompt. A threat match alone does not establish applicability.
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Draw up the technical documentation of the model
Art. 53 Abs. 1 Buchst. a, Anhang XI · applies from 2 August 2025
Statutory roles: Provider of a general-purpose AI model. Classes: General-purpose AI model, Model with systemic risk.
This reference is a review prompt. A threat match alone does not establish applicability.
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Provide information to downstream providers
Art. 53 Abs. 1 Buchst. b, Anhang XII · applies from 2 August 2025
Statutory roles: Provider of a general-purpose AI model. Classes: General-purpose AI model, Model with systemic risk.
This reference is a review prompt. A threat match alone does not establish applicability.
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Respect copyright and summarise the training content
Art. 53 Abs. 1 Buchst. c und d · applies from 2 August 2025
Statutory roles: Provider of a general-purpose AI model. Classes: General-purpose AI model, Model with systemic risk.
For models released under a free and open-source licence with publicly available parameters, the obligations under points (a) and (b) do not apply, but the copyright policy and the summary do. Models with systemic risk are excluded from this relief.
This reference is a review prompt. A threat match alone does not establish applicability.
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Evaluate and mitigate systemic risks
Art. 55 · applies from 2 August 2025
Statutory roles: Provider of a general-purpose AI model. Classes: Model with systemic risk.
This reference is a review prompt. A threat match alone does not establish applicability.
All 62 obligations by role, risk class and deadline in the obligations compass
Related threats
More entries from the topic group Supply Chain and Provenance.
Matching use cases
This list shows where to review this threat, rather than how high the risk is.
Belongs on the checklist for every AI system. What to review for every AI system
Frequently asked questions
- What is Supply Chain – Infrastructure?
- Attackers compromise the technical environment in which an AI system is developed and operated: cloud services, development tools, and third-party software components. This gives them access to systems, data, and models.
- Which mitigations help against Supply Chain – Infrastructure?
- The catalogue lists 4 mitigations, each with control type, effect and implementation level. First among them are Use trusted suppliers, Infrastructure hardening and Supply chain security audits.
- Which EU AI Act obligations does Supply Chain – Infrastructure touch?
- The catalogue maps this threat to Art. 25 Abs. 2, Art. 53 Abs. 1 Buchst. a, Art. 53 Abs. 1 Buchst. b, Art. 53 Abs. 1 Buchst. c und d and Art. 55 of the EU AI Act. In the obligations compass this leads to: Support downstream providers, Draw up the technical documentation of the model, Provide information to downstream providers, Respect copyright and summarise the training content and Evaluate and mitigate systemic risks. Whether an obligation applies depends on role, risk class and deadline.
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Cite this entry
For reports, policies or internal documents; the link leads directly to this entry.
“Supply Chain – Infrastructure”. Versatile AI Risk Assessment, AI threat catalogue, as of October 2026. https://www.versatile-ai-risk-assessment.com/en/wissensbasis/threats/supply-chain-infrastructure/