AI Supply Chain Risk
AI Supply Chain Risk, in the context of cybersecurity, refers to the potential threats, vulnerabilities, and compromises that can be introduced or leveraged across the entire lifecycle and dependency network of an Artificial Intelligence (AI) system. This risk acknowledges that the final security posture of a deployed AI product depends on the security of every preceding component and external partner.
This is a holistic concept that encompasses three significant categories of failure:
Data Integrity and Provenance Risk: This involves the security and trustworthiness of the data used to train, test, and operate the model. The risk includes data poisoning, in which an attacker compromises data sources to subtly introduce flawed or biased information, thereby sabotaging the model's accuracy or creating a security vulnerability. It also includes the risk of data leakage of proprietary training datasets at rest or in transit, especially if third-party data providers are involved.
Model and Code Dependency Risk: This focuses on the extensive use of third-party software and pre-trained models in modern AI systems. The risk arises from the use of open-source libraries (e.g., TensorFlow, Hugging Face models) that might contain unpatched vulnerabilities (CVEs) or even intentionally malicious code introduced by a compromised contributor. A security flaw in a single shared library can immediately propagate to hundreds of dependent AI applications.
Infrastructure and Operational Risk: This involves the security posture of the environments and services used to build, manage, and deploy the AI. Risks include the compromise of MLOps platforms, exposure of configuration secrets or API keys that grant access to the model, and the lack of governance over specialized cloud infrastructure (such as vector databases). A breach of a cloud service provider used by a model can halt its operation or expose its sensitive inputs and outputs.
Ultimately, AI Supply Chain Risk is the aggregate measure of how susceptible an organization's AI assets are to compromise due to a security weakness within their development, infrastructure, or data lineage.
ThreatNG, an all-in-one external attack surface management, digital risk protection, and security ratings solution, provides crucial external visibility to address AI Supply Chain Risk by focusing on unauthenticated exposures and dependencies across the AI environment's infrastructure, code, and vendors.
External Discovery and Inventory
ThreatNG’s foundation is its ability to perform purely external, unauthenticated discovery without connectors, which is essential for mapping the external footprint of every component in the AI supply chain.
Technology Stack Identification: This directly aids in identifying third-party and internal components of the AI supply chain. ThreatNG uncovers nearly 4,000 technologies, detailing specific vendors used, including those in the Artificial Intelligence category (265 technologies), as well as AI Model & Platform Providers and AI Development & MLOps tools.
Domain and Vendor Enumeration: The Supply Chain & Third-Party Exposure Security Rating is based on the unauthenticated enumeration of vendors within Domain Records, SaaS Identification of vendors identified within Cloud and SaaS Exposure, and analysis of the Technology Stack.
Example of ThreatNG Helping: ThreatNG’s Technology Stack module discovers that a critical data labeling vendor used in your AI supply chain is running an exposed web service using an outdated technology. This immediate, unauthenticated discovery identifies a weak link in your AI supply chain that could be exploited.
External Assessment for Supply Chain Risk
ThreatNG assesses the exposure risk of supply chain components without requiring internal audit access, providing an objective view of their security posture.
Supply Chain & Third-Party Exposure Rating: This dedicated rating quantifies the risk posed by external parties. It is based on findings across Cloud Exposure (externally identified cloud environments and exposed open cloud buckets), SaaS Identification, and Technology Stack analysis.
Data Leak Susceptibility: This rating is derived from uncovering external digital risks across Cloud Exposure, specifically exposed open cloud buckets. If a third-party vendor accidentally stores proprietary AI training data in a misconfigured public bucket, ThreatNG flags this critical data integrity risk.
Web Application Hijack Susceptibility: ThreatNG assesses this by analyzing the presence or absence of key security headers on subdomains. Since many supply chain attacks leverage compromised vendor web applications, this assessment helps identify external entry points that could lead to unauthorized access to your AI environment.
Example of ThreatNG Helping: ThreatNG flags a high Cyber Risk Exposure rating for a key software vendor in your AI pipeline due to exposed sensitive ports and known vulnerabilities on their public-facing systems, which directly affects your supply chain risk.
Reporting and Continuous Monitoring
ThreatNG provides a proactive, ongoing view of your supply chain risk by continuously monitoring the external attack surface, digital risk, and security ratings of all organizations, including third-party vendors.
Reporting: ThreatNG provides Security Ratings (A-F) and External GRC Assessment mappings, enabling security leaders to track and measure the effectiveness of collaborative security initiatives with vendors.
External GRC Assessment: This provides a continuous, outside-in evaluation of an organization's GRC posture. It maps external findings directly to relevant GRC frameworks, such as NIST CSF and ISO 27001, helpingensure that AI supply chain components meet regulatory compliance standards.
Investigation Modules
ThreatNG's Investigation Modules allow for targeted reconnaissance to validate specific supply chain weaknesses.
Sensitive Code Exposure: This module discovers public code repositories and explicitly looks for Access Credentials (various API Keys and Access Tokens) and Configuration Files. If a vendor in the AI supply chain accidentally pushes a key for an AI platform to a public repository, ThreatNG immediately discovers the leaked secret.
Dark Web Presence (DarCache Dark Web): This module monitors organizational mentions, including associated Ransomware Events tracked in DarCache Ransomware. This provides advanced warning if a critical vendor in your AI supply chain is being targeted or has already been compromised.
Subdomain Takeover Susceptibility: ThreatNG performs checks by identifying associated subdomains and looking for CNAME records pointing to third-party services that are inactive or unclaimed. A dangling DNS record on a vendor’s domain is a critical supply chain risk.
Example of ThreatNG Helping: The Dark Web Presence module identifies a threat mentioning a recent compromise of a company that is a critical data source in your AI supply chain. This intelligence is cross-referenced with DarCache Ransomware, providing advanced threat intelligence that helps your team preemptively isolate the affected data.
Complementary Solutions
ThreatNG's external, unauthenticated intelligence on vendor risks provides objective data that significantly enhances complementary solutions like Third-Party Risk Management (TPRM) Platforms and Software Composition Analysis (SCA) Tools.
Complementary Solutions (TPRM Platforms): ThreatNG's Supply Chain & Third-Party Exposure Security Rating and detailed external findings (e.g., exposed ports, unpatched web servers) can be automatically fed to a TPRM platform. This external data validates or contradicts vendor-supplied security questionnaires, allowing the TPRM platform to immediately prioritize the riskiest vendors, especially those exposing AI-related assets, for deeper due diligence.
Complementary Solutions (SCA Tools): ThreatNG's discovery of an exposed AI model endpoint or a vendor's code repository via Sensitive Code Exposure provides an SCA tool with a new, externally validated asset to scan internally. If ThreatNG flags an exposed configuration file for a vendor, the SCA tool can use this finding to prioritize an internal scan of that configuration and its associated open-source dependencies within your code base, preventing a dependency-based supply chain attack.
