AI Supply Chain Vendor Risk
The AI Supply Chain Vendor Risk is a significant cybersecurity threat that arises when an organization relies on external, third-party components to build, fine-tune, or deploy its Artificial Intelligence and Machine Learning (AI/ML) models. It is the aggregate risk introduced by the various external sources that contribute to the final operational system.
This risk extends beyond traditional software supply chain issues (like vulnerable code libraries) to include components unique to AI: pre-trained models, datasets, and specialized development platforms.
Detailed Sources of AI Supply Chain Vendor Risk
Vulnerable Pre-Trained Models: Organizations frequently use pre-trained models from public repositories (like Hugging Face) or third-party vendors. These models can be binary "black boxes" that are difficult to inspect statically and may contain hidden vulnerabilities, malicious features, or backdoors inserted by the original developer or a malicious actor who tampered with the model after publication.
Compromised External Data and Fine-Tuning Components: The supply chain includes the data used to train or refine a model. If this data comes from an unverified external source, it may be susceptible to Data Poisoning attacks, in which malicious content or specific triggers are introduced to compromise the model's integrity or performance. Furthermore, popular fine-tuning techniques like LoRA adapters are external components that can be compromised and merged with an existing LLM, introducing covert vulnerabilities.
Weak Model Provenance and Integrity: There is currently insufficient assurance regarding a published model's origin and integrity. An attacker can exploit this by compromising a supplier's account on a model repository or publishing a fake, tampered version of a popular model with the same name, exploiting user trust.
Vulnerable Development and Deployment Platforms: The risk extends to the platforms and tools used to manage the AI lifecycle. This includes third-party Development & DevOps tools, Cloud & Infrastructure services, and CI/CD pipelines. If an attacker exploits a vulnerable platform component, they can gain access to the model, its data, or the keys used to deploy it.
Legal and Compliance Exposure (T&Cs): Third-party model operators and data suppliers often have their own opaque Terms and Conditions (T&Cs) and data privacy policies. This introduces a risk that an organization's sensitive application data may be inadvertently used by the supplier for model training, leading to sensitive information disclosure and potential legal or compliance violations.
In essence, the AI Supply Chain Vendor Risk is the composite danger that any external element—from the original training code to the final deployment platform—may be compromised, allowing an attacker to inject vulnerabilities, steal intellectual property, or manipulate the model's output on the organization's behalf.
ThreatNG is designed to provide comprehensive protection against the various components of the AI Supply Chain Vendor Risk by utilizing its purely external, unauthenticated discovery and intelligence to identify and prioritize third-party and vendor-related exposures that can lead to compromise.
External Discovery
ThreatNG's External Discovery is crucial for mapping the full extent of the AI supply chain by identifying all visible vendors and third-party components used by the organization.
How it helps: The Technology Stack Investigation Module performs exhaustive, unauthenticated discovery of nearly 4,000 technologies, including hundreds categorized as Artificial Intelligence, as well as specific vendors in AI Model & Platform Providers and AI Development & MLOps. It also identifies general vendors across Development & DevOps, Cloud Infrastructure, and SaaS. This establishes the inventory of all external parties contributing to the AI system.
Example of ThreatNG helping: ThreatNG identifies that the organization is using a PaaS & Serverless vendor like Heroku for deployment, an AI Model & Platform Provider like Hugging Face, and a Version Control service like GitHub. This comprehensive mapping is the first step in managing the complex AI supply chain.
External Assessment
ThreatNG quantifies the risk introduced by these vendors through specialized security ratings and modules.
Highlight and Examples:
Vendor Compromise/Vulnerable Platforms: The Supply Chain & Third Party Exposure Security Rating (A-F scale) is based on the enumeration of vendors in Domain Records, identified technologies, and Cloud Exposure.
Example: ThreatNG uses the Domain Record Analysis and Technology Stack modules to identify that a core component of the AI system relies on an AI Development & MLOps vendor. If ThreatNG also flags the associated endpoint as having Invalid Certificates or Exposed Ports (findings related to the Cyber Risk Exposure rating), it indicates a misconfiguration in the third-party infrastructure, which is a direct supply chain risk.
Leaked Credentials/IP Theft Vector: The Non-Human Identity (NHI) Exposure Security Rating is a critical metric for finding high-privilege credentials that an attacker could use to compromise a vendor component or steal a proprietary model.
Example: The Sensitive Code Discovery and Exposure module discovers public code repositories and specifically searches for credentials like GitHub Access Token, Heroku API Key, or AWS Access Key ID. The exposure of an Artifactory API Token would be a direct supply chain risk, as an attacker could potentially tamper with or steal a model artifact stored in that repository.
Continuous Monitoring
ThreatNG provides Continuous Monitoring of the external attack surface, digital risk, and security ratings of all organizations.
How it helps: This ensures that security teams are alerted immediately if a third-party vendor used in the AI supply chain changes its security posture. If a vendor's endpoint used for model deployment suddenly begins serving unencrypted traffic or exposes a new administrative port, ThreatNG detects this Configuration Drift in real time, preventing a stealthy supply-chain compromise.
Investigation Modules
These modules provide the granular evidence needed to validate and prioritize the risks originating from third-party vendors and components.
Highlight and Examples:
Online Sharing Exposure: This module identifies an organization's presence on online code-sharing platforms such as GitHub Gist and Pastebin.
Example: An analyst uses this module to find a developer's comment or a configuration file referencing a private third-party model dependency, such as a LoRA adapter or a specific Python file. This finding helps validate the integrity risk of that third-party component before it is integrated into a production model.
Username Exposure: This module conducts Passive Reconnaissance across high-risk forums and development sites.
Example: ThreatNG identifies an internal developer's username on a Developer Forum, such as GitHub or Bitbucket, discussing a problem related to a specific AI Development & MLOps tool. This allows the security team to monitor that user's external digital footprint for additional information leaks that could compromise the supply chain.
Intelligence Repositories
ThreatNG’s Intelligence Repositories (DarCache) are essential for adding real-world context and prioritization to vendor vulnerabilities.
How it helps: The Vulnerabilities (DarCache Vulnerability) repository integrates NVD (severity), EPSS (likelihood), and KEV (Known Exploited Vulnerabilities) data.
Example: If ThreatNG identifies that a third-party containerization platform like Docker is used in the AI deployment pipeline, and a new critical vulnerability in Docker is listed in the DarCache KEV, the organization is immediately alerted that their AI supply chain is exposed to an actively exploited threat, allowing for rapid mitigation.
Cooperation with Complementary Solutions
ThreatNG's external focus enables robust, cross-platform security for the AI supply chain.
Cooperation with Internal Vulnerability Management (VM) Tools: ThreatNG’s discovery of a critical, actively exploited vulnerability in a third-party component (using DarCache KEV data) can be shared with a complementary VM tool.
Example: ThreatNG confirms that an AI Development & MLOps vendor's tool is running on a server and is affected by a KEV-listed vulnerability. This external finding is used to force the internal VM tool to run an authenticated scan on the hosting server immediately and to prioritize patching that specific supply chain component.
Cooperation with Cloud Security Posture Management (CSPM) Tools: ThreatNG identifies external cloud exposures.
Example: ThreatNG identifies an unauthenticated AWS/S3 exposure used by a third-party vendor for data exchange. This external validation is routed to a complementary CSPM tool, which then checks the internal access policies for that specific bucket and enforces stricter configurations, eliminating the external vulnerability from the supply chain.
