Shadow AI Models
Shadow AI Models are a critical cybersecurity concern: any machine learning (ML) or artificial intelligence (AI) model developed, trained, and deployed within an organization's operational environment without the knowledge, approval, or governance of the central IT, security, or compliance teams.
These models are the core output of Shadow AI activity, in which individual departments or employees use readily available cloud services, open-source libraries, or low-code/no-code platforms to solve specific business problems quickly. Because they are unsanctioned, these models exist outside the company's official inventory and security lifecycle.
Detailed Cybersecurity Risks of Shadow AI Models
The hidden nature of Shadow AI Models creates severe cybersecurity risks:
Unknown Data Handling and Leakage:
The model may have been trained on, or handle, sensitive, proprietary, or regulated data (such as Personal Identifiable Information or Protected Health Information) that was copied from secure systems.
Since the security team is unaware of the model’s existence, no data loss prevention (DLP) controls, encryption policies, or access logs are in place, leaving the organization vulnerable to untracked data breaches.
Unmanaged Vulnerabilities and Model Poisoning:
Shadow AI Models are often built using outdated or unpatched versions of ML frameworks (e.g., TensorFlow, PyTorch). They are not regularly scanned for known Common Vulnerabilities and Exposures (CVEs), creating a persistent, exploitable entry point into the network.
They are susceptible to specific AI attacks, such as Model Poisoning, where an adversary injects malicious data into the model's training pipeline to degrade its performance or introduce backdoors.
Intellectual Property (IP) Theft and Logic Exposure:
Because the model's interface (endpoint) is often exposed without proper authentication or throttling, attackers can exploit this to perform a Model Stealing attack. This involves sending thousands of queries to reconstruct the model’s proprietary logic, effectively stealing the company’s valuable IP.
The model itself may inadvertently be configured to leak its training data or system prompt, exposing confidential business logic.
Operational Risk and Compliance Failures:
Unsanctioned models can generate biased, inaccurate, or non-compliant decisions. If they are used for critical tasks (like loan approvals or medical diagnoses), they introduce legal and regulatory risk, failing to meet standards like GDPR, HIPAA, or emerging AI regulations.
In summary, a Shadow AI Model is an unmanaged asset that executes powerful, hidden business logic on sensitive data and provides an unprotected endpoint that a sophisticated attacker can use to penetrate the corporate environment. ThreatNG, as an External Attack Surface Management (EASM) and Digital Risk Protection solution, is uniquely positioned to discover and assess Shadow AI Models by operating entirely from the attacker's perspective, outside the organizational network, without needing credentials or connectors. It focuses on finding the external-facing components and exposures that enable the breach of these unsanctioned models.
External Discovery
ThreatNG's External Discovery module is the foundation for locating Shadow AI Models by mapping the organization’s complete external digital footprint.
How it helps: Shadow AI Models are often hosted on untracked subdomains or cloud services. ThreatNG uses Subdomain Intelligence to uncover all associated subdomains and the platforms hosting them, including public-facing API endpoints or applications that interact with the AI models. The Technology Stack Identification module then provides exhaustive, unauthenticated discovery of nearly 4,000 technologies, critically identifying hundreds of technologies categorized as Artificial Intelligence, along with specific vendors in AI Model & Platform Providers and AI Development & MLOps. This discovery of AI-specific technology on an unmanaged subdomain is the confirmation of a Shadow AI Model.
Example of ThreatNG helping: ThreatNG discovers an unmanaged subdomain, genai-experiment.company.com, and the Technology Stack module identifies it as running an AI Model & Platform Provider service. This flags a potentially high-risk, unsanctioned Shadow AI Model for immediate security review.
External Assessment
ThreatNG performs unauthenticated external assessment to highlight the critical configuration flaws that make the discovered Shadow AI Model vulnerable to exploitation.
Highlight and Examples:
Model Theft and Access Compromise: The Non-Human Identity (NHI) Exposure Security Rating quantifies vulnerability from high-privilege machine identities, such as leaked API keys and service accounts. Shadow AI Models are often protected by these non-human credentials.
Example: The Sensitive Code Exposure investigation module scans public code repositories and mobile apps for Access Credentials and Security Credentials. Finding a leaked LLM access key or an Authorization Bearer token associated with the Shadow AI Model endpoint is definitive external evidence of compromise, enabling an attacker to bypass authentication and steal the model's logic.
Data Leakage Risk: The Data Leak Susceptibility rating is derived from uncovering external risks, specifically Cloud Exposure, including exposed open cloud buckets. Shadow AI Models often use these locations to store training data.
Example: ThreatNG flags a publicly open AWS S3 bucket associated with the organization. An analyst then finds that the bucket contains files labeled TrainingData-LLM-V2. This immediate, unauthenticated discovery confirms a severe data-exposure risk stemming from the unsanctioned model.
Continuous Monitoring
ThreatNG provides Continuous Monitoring of the external attack surface, digital risk, and security ratings, ensuring that newly deployed or temporarily exposed Shadow AI Models are found immediately.
How it helps: If a developer deploys a new version of a Shadow AI Model at a new subdomain, continuous monitoring immediately detects the new asset and tracks its security rating. This proactive approach minimizes the time an unmanaged, potentially vulnerable model remains hidden and exposed.
Investigation Modules
ThreatNG’s Investigation Modules provide the necessary tools and context to prioritize and act on the risks posed by discovered Shadow AI Models.
Highlight and Examples:
Sensitive Code Exposure: This module is crucial for tracking the unauthenticated code exposure that often accompanies Shadow AI.
Example: An analyst uses this module and finds an exposed configuration snippet on a development forum that references the specific path of a production GenAI API endpoint. This external finding directly informs the security team about the precise attack vector targeting the Shadow AI Model.
Online Sharing Exposure: This module identifies the presence of organizational entities on code-sharing platforms like Pastebin and GitHub Gist.
Example: ThreatNG identifies a public GitHub Gist containing a snippet of proprietary algorithm code for a new product classifier, which is a common artifact of a Shadow AI Model. This allows the security team to act before the model's proprietary logic is widely compromised.
Intelligence Repositories
ThreatNG’s Intelligence Repositories provide the risk context necessary to prioritize remediation efforts on the most dangerous Shadow AI Models.
How it helps: The Vulnerabilities repository integrates external intelligence from sources like NVD and KEV (Known Exploited Vulnerabilities). If a discovered Shadow AI Model is running an ML service with a known vulnerability, the KEV data confirms the threat as an immediate and proven risk, ensuring that this vulnerable model is prioritized over others.
Cooperation with Complementary Solutions
ThreatNG’s external visibility is vital for complementary internal solutions to gain context and accelerate remediation of Shadow AI Models.
Cooperation with AI Security Platforms: ThreatNG’s detection of a leaked credential via NHI Exposure that grants access to a Shadow AI Model endpoint is critical external proof of compromise.
Example: This external intelligence is passed to a complementary AI Security Platform, which uses the compromised key to prioritize internal red-teaming scenarios against the discovered Shadow AI Model, specifically testing for data leakage or unauthorized actions.
Cooperation with Cloud Security Posture Management (CSPM) Tools: ThreatNG flags externally visible cloud exposures that may contain Shadow AI artifacts.
Example: When ThreatNG discovers a publicly open S3 bucket containing AI training data, a complementary CSPM tool can use this external alert to automatically trigger an internal audit of all associated IAM roles and bucket policies, immediately tightening access controls on the storage feeding the Shadow AI Model.
The ability to map a firm's full external attack surface is key to addressing the growing threat of unmanaged AI, and this video coversThreatNG External Discovery in more detail.
