AI Model Exposure Detection
AI Model Exposure Detection is a dedicated cybersecurity process focused on continuously identifying and assessing all potential ways an organization's proprietary or sensitive Artificial Intelligence (AI) and Machine Learning (ML) models—along with their associated intellectual property and data—are exposed to the public internet or vulnerable to exploitation.
It is a critical component of AI Attack Surface Management, specifically concerned with the output of the ML development process: the model itself, as it is being served for inference.
Key Exposures Targeted by Detection
Detection efforts focus on the external interfaces and components that an attacker would target to interact with or steal the model.
1. API and Endpoint Exposure
Models are typically served to users or applications via an API (Application Programming Interface) endpoint. Detection focuses on:
API Misconfiguration: Identifying exposed API endpoints that lack proper authentication, authorization (like strong API keys or OAuth), or rate limiting. An unauthenticated endpoint is a direct path to model abuse.
Shadow API Discovery: Finding undocumented or forgotten API endpoints hosting development, staging, or retired model versions that have not been adequately secured or decommissioned.
Vulnerable Gateways: Detecting vulnerabilities in the web servers, containers, or API gateways that sit in front of the model, which could allow an attacker to bypass security controls and reach the underlying model-serving infrastructure.
2. Intellectual Property (IP) Leakage
This focuses on unintentional disclosure of the model's core components:
Code Repository Leaks: Scanning public code repositories for accidental commits of the model's source code, weights, architecture details, or sensitive configuration files that reveal how the model was trained or how it operates.
Configuration File Exposure: Identifying exposed configuration files (e.g., JSON, YAML) in cloud storage buckets or on web servers that contain model-specific details, such as the feature set, the version, or the specific cloud environment path.
Model Artifact Storage: Detecting public or improperly secured cloud storage locations (like AWS S3 buckets or Azure Blobs) where the final, trained model files (artifacts) or their checkpoints are stored, making them vulnerable to outright theft.
3. Adversarial Attack Susceptibility
Detection goes beyond standard vulnerabilities to assess how the exposed model itself can be manipulated.
Inference Monitoring: Continuously monitoring the model's input and output channels for patterns indicative of adversarial techniques.
Model Extraction: Detecting unusually high volumes of queries or rapid, systematic changes in query patterns, which suggests an attacker is attempting to reverse-engineer or steal a functional copy of the model.
Evasion Attempts: Monitoring for subtly manipulated inputs (e.g., altered images, complex text prompts) designed to confuse the model and force an incorrect or malicious prediction.
Prompt Injection Testing: For large language models (LLMs), detection includes probing the exposed API interface for susceptibility to prompts that override the model's intended safety and operational instructions, potentially leading to data leakage or unauthorized code execution.
AI Model Exposure Detection is an essential discipline for protecting the business value and security integrity of modern AI-powered applications.
ThreatNG is a powerful tool for AI Model Exposure Detection because it provides an attacker's external view of the digital perimeter, which is the primary vector for model theft, manipulation, and data leakage. It focuses on the exposed interfaces and artifacts that reveal the model's presence and its underlying vulnerabilities.
External Discovery and Continuous Monitoring
ThreatNG's External Discovery performs purely external unauthenticated discovery using no connectors, making it perfect for finding the exposed components of a deployed model.
API Endpoint Discovery: The platform continuously maps the organization’s entire external digital footprint. This is essential for finding the APIs and Subdomains that serve the final model predictions, which are the primary targets for attacks like model extraction. ThreatNG identifies these exposed endpoints, providing the initial visibility required to secure the model serving layer.
Artifact Leakage in Archived Web Pages: ThreatNG's Archived Web Pages capability searches for files that have been archived on the organization's online presence, including JSON Files, Python Files, and Document Files. This can reveal accidental exposures where a developer left a model configuration file, an uncompiled model artifact (e.g., a .pkl or .h5 file), or a document detailing the model's inner workings on an external-facing server before it was pulled down. This finding immediately flags an IP leakage risk.
Continuous Monitoring: Since a new model version or a testing API can be deployed rapidly, ThreatNG’s Continuous Monitoring ensures that as soon as a new external-facing asset related to the model appears, it is discovered and assessed, preventing blind spots.
Investigation Modules and Technology Identification
ThreatNG’s Investigation Modules provide the granular context required to link an external exposure to a high-value AI model.
Detailed Investigation Examples
DNS Intelligence and AI/ML Technology Identification: The DNS Intelligence module includes Vendor and Technology Identification. This allows ThreatNG not just to find a subdomain, but to identify that it is running an API Management system, or more specifically, an AI Model & Platform Provider like Hugging Face or an AI Development & MLOps platform like MLflow or Pinecone. Identifying the Technology Stack confirms that the exposure is a high-value AI asset, not just a standard web server. For instance, detecting a public IP hosting a service identified as Triton Inference Server is a direct flag for an exposed model.
Code Repository Exposure for Model Secrets: The Code Repository Exposure module is critical for detecting Intellectual Property leakage. It searches public repositories for Configuration Files and Access Credentials. An example is finding a publicly committed configuration file that specifies the URL and access token for the model's internal API, or the Amazon AWS S3 Bucket where the final, proprietary model weights are stored. This finding provides an attacker with the keys to steal the entire model.
Search Engine Exploitation for Inference Errors: The Search Engine Attack Surface can discover logs or error messages that were indexed by search engines. For a deployed model, this might include exposed Errors that contain stack traces revealing the internal architecture of the model-serving microservices, giving an attacker a blueprint for finding an exploit path.
External Assessment and IP Risk
ThreatNG's assessment scores quantify the model exposure risk, facilitating rapid prioritization.
Detailed Assessment Examples
Cyber Risk Exposure: This score incorporates Certificate and TLS Health and Known Vulnerabilities. A poor score here indicates that the API gateway serving the model has a weak security perimeter. For example, suppose the model API endpoint uses an expired or misconfigured SSL certificate. In that case, an attacker knows the organization is lax on basic security, signaling an easy target for a larger attack.
Data Leak Susceptibility: This is informed by Cloud and SaaS Exposure. If the model's training or inference data is stored in an open Open Exposed Cloud Bucket, ThreatNG flags this. This is a crucial finding for model exposure, as this data can be used to execute data poisoning or model inversion attacks against the exposed model.
Web Application Hijack Susceptibility: This score considers factors like Domain Intelligence and can reveal if the domain hosting the model API is vulnerable to attack, allowing an attacker to intercept or redirect user queries intended for the model.
Intelligence Repositories and Reporting
ThreatNG’s intelligence and reporting structure translate raw exposure data into actionable security intelligence.
DarCache Vulnerability and Prioritization: ThreatNG DarCache Vulnerability correlates vulnerabilities in the model-serving infrastructure (like the web server or container platform) with the KEV (Known Exploited Vulnerabilities). This allows the security team to prioritize patching the one vulnerability that directly exposes their high-value ML model and is actively being exploited by adversaries.
Reporting: Reports are Prioritized (High, Medium, Low) and include Reasoning and Recommendations. This ensures that the security team focuses on high-impact risks, such as an identified publicly exposed model configuration file, and receives explicit guidance on how to secure or decommission the exposed asset.
Complementary Solutions
ThreatNG's external model exposure intelligence can work seamlessly with complementary internal security solutions.
MLOps Security Platforms (Runtime Monitoring): When ThreatNG identifies an exposed, unmanaged model API, a complementary runtime monitoring platform uses external discovery data. The runtime tool can then prioritize its internal log analysis and adversarial attack detection on the specific asset that ThreatNG has flagged as high-risk and externally accessible, enhancing the detection of model extraction attempts.
Cloud Security Posture Management (CSPM) Tools: ThreatNG’s finding of an Open Exposed Cloud Bucket storing model artifacts or training data is shared with a complementary CSPM solution. This synergy allows the CSPM to take immediate internal action, such as automatically modifying the bucket policy to private or quarantining the sensitive data, enforcing a secure posture identified by the external discovery.
Digital Risk Protection (DRP) Tools: ThreatNG's Dark Web Presence and DarCache Rupture may identify Associated Compromised Credentials linked to a developer's account. This intelligence is fed to a complementary DRP vendor, which can immediately use the data to initiate a forced password reset and monitor for further use of those credentials in black markets, protecting the credentials that could otherwise be used to compromise the model's integrity.