AI Model Footprinting
AI Model Footprinting, in the context of cybersecurity, is a passive reconnaissance technique used to gather intelligence about a target organization's deployed Artificial Intelligence (AI) and machine learning (ML) models from an external perspective. The goal is to collect non-sensitive, observable data that helps an attacker understand the nature, architecture, and potential vulnerabilities of the operational AI system before launching an active attack.
This process involves identifying the "fingerprint" of the deployed model, often without directly interacting with its predictive capabilities. The information gathered typically includes:
Technology Stack Identification: Observing publicly exposed APIs or endpoints to identify the underlying infrastructure, frameworks, or cloud services used to host and serve the model (e.g., detecting the presence of specific MLOps tools, containerization technologies, or API gateways).
Model Type Inference: Analyzing public documentation, error messages, or response formats to infer the general category of the model (e.g., is it a large language model, a computer vision model, a time-series predictor, or a recommendation engine).
Data Source Clues: Examining misconfigured or exposed elements, such as public-facing cloud storage bucket names or metadata, which can provide hints about the type of data the model was trained on or is currently using.
Version and Service Detection: Identifying specific software versions or known services running on the host infrastructure, which can be cross-referenced with public vulnerability databases (CVEs) to find exploitable weaknesses in the supporting environment.
Successful AI Model Footprinting creates a comprehensive map of the AI attack surface, informing the attacker on where to focus their efforts, whether through exploiting a vulnerability in the serving infrastructure or by designing a targeted prompt-injection attack against a specific model type.
AI Model Footprinting is fundamentally a reconnaissance activity, which relies on gathering clues from public sources to understand a hidden target. ThreatNG is ideally suited to this, as its entire function is to provide an unauthenticated, attacker-centric view of an organization’s external digital footprint. ThreatNG can help with AI Model Footprinting by detecting the telltale signs of the model's presence and supporting infrastructure.
External Discovery
ThreatNG’s capability to perform purely external, unauthenticated discovery without connectors is the starting point for AI Model Footprinting. An attacker performs footprinting by scanning the internet for clues, which is precisely what ThreatNG automates.
Technology Stack Identification: This is the most direct way ThreatNG aids footprinting. It provides exhaustive, unauthenticated discovery of nearly 4,000 technologies, including the hundreds of technologies categorized as Artificial Intelligence, as well as vendors in AI Model & Platform Providers and AI Development & MLOps. If an organization is running a publicly exposed API endpoint, ThreatNG identifies the underlying technology stack, revealing the model's framework or serving mechanism.
Subdomain Intelligence: ThreatNG discovers all associated subdomains and identifies the technologies running on them, including cloud hosting platforms. This helps footprint an attacker's potential entry points, revealing exposed staging or development environments that might host unhardened versions of the model.
Example of ThreatNG Helping: ThreatNG discovers a subdomain, beta-llm.company.com, running a technology identified in its Technology Stack as Hugging Face (an AI Model & Platform Provider). This single finding allows the security team to complete the footprinting phase and confirm the organization has a publicly exposed Generative AI asset.
External Assessment
ThreatNG's external assessments reveal configuration weaknesses that an attacker would use to deepen their footprint.
Cyber Risk Exposure (Sensitive Code): This rating is based on findings that include Sensitive Code Discovery and Exposure (code secret exposure). Finding a publicly exposed configuration file via this assessment could provide an attacker with a direct path to model parameters, or even API keys used by the model, completing a critical part of the model's footprint.
Data Leak Susceptibility (Cloud Exposure): This rating is derived from uncovering external digital risks across Cloud Exposure, specifically exposed open cloud buckets. These buckets often contain the source training data or proprietary model artifacts. Discovering a public bucket named model-artifact-backup-v3 completes a crucial data-related part of the model's footprint.
Example of ThreatNG Helping: ThreatNG's Cyber Risk Exposure flags an exposed .env file via Sensitive Code Discovery. This file is found to contain a reference to a custom model endpoint URL and a specific header required for API access, allowing the security team to see exactly what an attacker would use to interact with and further fingerprint the model.
Reporting and Continuous Monitoring
ThreatNG provides Continuous Monitoring of the external attack surface and digital risk.
Continuous Monitoring: Since footprinting is often a time-consuming process for attackers, continuous monitoring ensures that the moment a development team accidentally exposes a new AI API or model endpoint, ThreatNG instantly flags the change.
Reporting (Security Ratings): Findings related to model footprinting, such as exposed APIs or leaked credentials, contribute to poor Security Ratings, including Cyber Risk Exposure and Data Leak Susceptibility. This converts a technical footprinting observation into a prioritized business risk that requires immediate action.
Investigation Modules
ThreatNG's Investigation Modules allow security teams to gather detailed OSINT (Open-Source Intelligence) that completes the model's footprint.
Subdomain Intelligence (Header Analysis and Ports): This module performs Header Analysis and identifies Exposed Ports. An attacker uses specific ports or server headers to infer the type of service running on a server. ThreatNG automates this by flagging standard database ports or specific server technologies that host models.
Online Sharing Exposure: This module identifies the presence of organizational entities on online code-sharing platforms such as Pastebin and GitHub Gist. An attacker can find proprietary prompts, configuration snippets, or even model I/O definitions in these posts, which are invaluable for completing the model's footprint.
Reconnaissance Hub: This unifies Overwatch (cross-entity vulnerability intelligence) with Advanced Search. This allows a security professional to actively query the entire external footprint to find, validate, and prioritize threats like CVEs in minutes. This can be used to quickly check if the specific infrastructure identified during footprinting has any publicly known, exploitable vulnerabilities.
Example of ThreatNG Helping: Using the Reconnaissance Hub, a security analyst searches for a specific technology name identified by the Technology Stack module. The search reveals a linked Ransomware Event in DarCache Ransomware related to that technology, providing critical, real-time threat intelligence that immediately prioritizes the exposed AI model as an urgent risk.
Complementary Solutions
ThreatNG's external, unauthenticated footprinting data can be used by complementary solutions such as Vulnerability Management (VM) platforms and Security Information and Event Management (SIEM) systems to enhance their coverage.
Complementary Solutions (Vulnerability Management): ThreatNG's Technology Stack and Subdomain Intelligence discover the existence and external configuration of an exposed AI model's supporting infrastructure (e.g., a specific version of a web server or an exposed container service). This external footprint data can be automatically fed into a VM platform, providing it with a target that it previously missed and instructing it to run a deeper, authenticated scan on the internal network segment associated with that newly discovered IP. For example, ThreatNG finds an exposed API gateway linked to a cloud IP, and the VM platform is then directed to scan that IP for known CVEs related to its exposed components.
Complementary Solutions (SIEM Systems): ThreatNG's identification of a highly sensitive asset (like a publicly exposed AI model endpoint) and its associated risk (e.g., a high NHI Exposure rating) provides critical business context to the SIEM. If the SIEM later detects suspicious internal network activity or a login attempt targeting that asset, it can use ThreatNG's external footprinting data to instantly elevate the risk score of that internal alert, turning a low-priority log entry into an immediate incident.
