External Library Vulnerabilities
Specification
Define, implement and evaluate processes, procedures and technical measures to identify updates for applications which use third party or open source libraries according to the organization's vulnerability management policy.
Threat coverage
Architectural relevance
Lifecycle
Not applicable
Guardrails
Evaluation, Validation/Red Teaming, Re-evaluation
Orchestration, AI Services supply chain, AI applications
Operations, Maintenance, Continuous monitoring, Continuous improvement
Data deletion, Model disposal
Ownership / SSRM
PI
Shared Cloud Service Provider-Model Provider (Shared CSP-MP)
The CSP and MP are jointly responsible and accountable for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with Large Language Model (LLM)/GenAI technologies in the context of the services or products they develop and offer.
Model
Owned by the Model Provider (MP)
The model provider (MP) designs, develops, and implements the control as part of their services or products to mitigate security, privacy, or compliance risks associated with the Large Language Model (LLM). Model Providers are entities that develop, train, and distribute foundational and fine-tuned AI models for various applications. They create the underlying AI capabilities that other actors build upon. Model Providers are responsible for model architecture, training methodologies, performance characteristics, and documentation of capabilities and limitations. They operate at the foundation layer of the AI stack and may provide direct API access to their models. Examples: OpenAI (GPT, DALL-E, Whisper), Anthropic(Claude), Google(Gemini), Meta(Llama), as well as any customized model.
Orchestrated
Owned by the Orchestrated Service Provider (OSP)
The Orchestrated Service Provider (OSP) is responsible for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with Large Language Model (LLM)/GenAI technologies in the context of the services or products they develop and offer. The OSP is responsible and accountable for the implementation of the control within its own infrastructure/environment. If the control has downstream implications on the users/customers, the OSP is responsible for enabling the customer and/or upstream partner in the implementation/configuration of the control within their risk management approach. The OSP is accountable for ensuring that its providers upstream (e.g MPs) implement the control as it relates to the service/product the develop and offered by the OSP. This refers to entities that create the technical building blocks and management tools that enable AI implementation. This can include platforms, frameworks, and tools that facilitate the integration, deployment, and management of AI models within enterprise workflows. These providers focus on model orchestration and offer services like API access, automated scaling, prompt management, workflow automation, monitoring, and governance rather than end-user functionality or raw infrastructure. They help businesses implement AI in a structured and efficient manner. Examples: AWS, Azure, GCP, OpenAI, Anthropic, LangChain (for AI workflow orchestration), Anyscale (Ray for distributed AI workloads), Databricks (MLflow), IBM Watson Orchestrate, and developer platforms like Google AI Studio.
Application
Owned by the Application Provider (AP)
The Application Provider (AP) is responsible for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with Large Language Model (LLM)/GenAI technologies in the context of the services or products they develop and offer. The AP is responsible and accountable for the implementation of the control within its own infrastructure/environment. If the control has downstream implications on the users/customers, the AP is responsible for enabling the customer and/or upstream partner in the implementation/configuration of the control within their risk management approach. The AP is accountable for carrying out the due diligence on its upstream providers (e.g MPs, Orchestrated Services) to verify that they implement the control as it relates to the service/product develop and offered by the AP. These providers build and offer end-user applications that leverage generative AI models for specific tasks such as content creation, chatbots, code generation, and enterprise automation. These applications are often delivered as software-as-a-service (SaaS) solutions. These providers focus on user interfaces, application logic, domain-specific functionality, and overall user experience rather than underlying model development. Example: OpenAI (GPTs,Assistants), Zapier, CustomGPT, Microsoft Copilot (integrated into Office products), Jasper (AI-driven content generation), Notion AI (AI-enhanced productivity tools), Adobe Firefly (AI-generated media), and AI-powered customer service solutions like Amazon Rufus, as well as any organization that develops its AI-based application internally.
Implementation guidelines
Auditing guidelines
1. Verify that the Cloud Services Provider (CSP) has defined processes, procedures, and technical measures to identify and implement updates for applications that use third party or open source libraries, in order to mitigate risks of compromise associated with the exploitation of vulnerabilities within such libraries. Ensure that the processes are documented in detail, covering scope, objectives, roles and responsibilities. 2. Examine the above-mentioned processes, procedures, and technical measures to confirm their compliance with the organization's vulnerability management policy, as well as with relevant regulatory requirements and industry best practices. 3. Confirm that the above-mentioned processes, procedures, and technical measures are concretely and appropriately applied by involved parties in their day-to-day operations. 4. Inspect whether the above-mentioned processes, procedures, and technical measures are monitored against sets of efficacy and efficiency metrics / indicators. 5. Inspect whether the above-mentioned processes, procedures, and technical measures are periodically reviewed and updated by responsible parties. 6. Verify the CSP is responsible for managing vulnerabilities in the external libraries used by their own managed cloud services. 7. Confirm the CSP provides tools (e.g., native SCA scanners) to help customers identify and manage vulnerabilities in the open-source libraries they use in their own applications deployed on the cloud.
Standards mappings
42001: A.10.3 Supply Chain 27001: 6.1.2 Identifying risks from third party libraries 27001: A.8.8 Management of technical vulnerabilities 27002: 8.8 Technical vulnerability management 27002: 8.12 Secure software development 27002: 8.9 Configuration management 27002: 5.7 Threat intelligence
Addendum
Introduce technical vulnerability management controls for third-party components, similar to ISO/IEC 27002 but tailored to AI-specific software stacks
Article 15 Annex IV (1) (c)
Addendum
N/A
No Mapping
Addendum
Explicitly require the tracking and maintenance of third-party/open-source libraries in AI applications. Integrate with enterprise vulnerability management programs. Mandate technical tools like SCA or SBOM validation. Include evaluation and lifecycle governance for those measures
C4 DQ-01 C4 SR-02 C5 DEV-01 C5 DEV-02 C5 DEV-05
Addendum
N/A
AI-CAIQ questions (1)
Are processes, procedures, and technical measures defined, implemented, and evaluated to identify updates for applications that use third party or open source libraries according to the organization's vulnerability management policy?