Exposed Vector Database Discovery
The Exposed Vector Database Discovery is a specialized cybersecurity process that aims to identify and locate a Vector Database (or its associated data storage) that has been inadvertently left publicly accessible to external, unauthenticated users. This discovery is a critical concern because Vector Databases are the memory layer for many modern AI applications, particularly those using Retrieval-Augmented Generation (RAG).
Detailed Breakdown of the Vector Database
A Vector Database stores numerical representations, called embeddings, of an organization's proprietary or sensitive data (e.g., documents, code, financial records). When a user queries an RAG-enabled LLM, the model queries this database to retrieve relevant context.
The exposure is defined by an attacker gaining unauthenticated access to this critical data layer.
The Entry Point (Exposure): The vulnerability typically lies in a severe configuration error during database deployment or its hosting infrastructure. This includes:
Open Network Ports: The database's network port (often different from a traditional database port) is exposed directly to the public internet, without protection from a firewall or API gateway.
Misconfigured Cloud Service: The managed cloud service (e.g., Azure Database, AWS RDS, specialized vector database services) is set with overly permissive security group rules or public IP assignments, allowing external connection attempts.
The Discovery Mechanism: Attackers perform discovery using techniques similar to traditional database enumeration, but tailored for vector technology:
Port Scanning: Probing known ports used by popular vector database vendors (e.g., Pinecone, Weaviate, Milvus).
Service Fingerprinting: Identifying the running service by analyzing banner information or API responses when attempting an unauthenticated connection.
Leaked Connection Strings: Searching public code repositories or file-sharing sites for configuration files that contain the database's public IP address, port, and connection details.
Cybersecurity Implications: Successful discovery of an exposed Vector Database leads to direct access to the LLM's proprietary knowledge base, enabling devastating attacks:
Data Exfiltration: The attacker can run simple database queries to extract the proprietary embeddings. While the data is numerical, sophisticated attackers can infer or partially reconstruct the original sensitive documents (Model Inversion) or use the vector data to map out the organization’s proprietary knowledge structure.
Vector Poisoning: If the attacker gains write access (possible through misconfiguration or a zero-day exploit), they can inject malicious vectors or false embeddings into the database. This subverts the LLM's grounding, leading the RAG application to return false, biased, or malicious information to users (LLM09:2025 Misinformation).
Bypassing LLM Guardrails: The attacker gains complete knowledge of the LLM's available information. This enables them to craft exact and effective Prompt Injection attacks because they know exactly which context the model will retrieve.
The Exposed Vector Database Discovery turns the core intelligence of an RAG application into a public liability, providing the attacker with both the organization's IP and a blueprint for attack. ThreatNG addresses Exposed Vector Database Discovery by focusing on external misconfigurations and credential leaks that enable attackers to compromise the cloud storage where AI training data resides. The solution's approach is to identify and validate these externally visible exposure points from an unauthenticated perspective.
External Discovery
ThreatNG's External Discovery is the essential first step, as cloud storage misconfigurations often lead to exposures that are invisible to internal security teams. ThreatNG performs this discovery using no connectors.
How it helps: The core of the vector is an exposed cloud asset. ThreatNG uses its Subdomain Intelligence and Domain Record Analysis to map all subdomains hosted on major cloud platforms like AWS, Microsoft Azure, and Google Cloud Platform. It also uses the Technology Stack Identification module to identify technologies categorized as Cloud & Infrastructure (specifically Storage & CDN, such as AWS/S3, CloudFront, Microsoft Azure). This inventory confirms which cloud assets are part of the organization's external attack surface and are therefore targets for poisoning.
Example of ThreatNG helping: ThreatNG identifies a subdomain hosted on an AWS service. This discovery confirms the presence of an S3 environment that requires immediate inspection for public access.
External Assessment
ThreatNG’s external assessment directly looks for the two misconfigurations required for the poisoning vector: exposed buckets and access keys.
Highlight and Examples:
Exposed Open Cloud Buckets: The Data Leak Susceptibility Security Rating (A–F scale) is derived directly from uncovering external risks like Cloud Exposure (specifically exposed open cloud buckets). The Cloud and SaaS Exposure investigation module actively identifies and validates these open buckets across the major cloud providers.
Example: ThreatNG identifies an unauthenticated read/write vulnerability on a Google Cloud Platform storage bucket. This finding proves that an attacker has an entry point to upload or modify files, including AI training data, successfully enabling the Cloud Bucket Poisoning Vector.
Leaked Write Access Credentials: The Non-Human Identity (NHI) Exposure Security Rating quantifies the vulnerability from high-privilege machine identities. These leaked credentials often grant read/write access to data pipelines and storage systems.
Example: The Sensitive Code Discovery and Exposure capability scans public code repositories for exposed AWS Secret Access Key credentials. Finding this secret provides Legal-Grade Attribution, allowing an attacker to upload malicious data directly into a private training data bucket and execute the poisoning attack.
Continuous Monitoring and Reporting
ThreatNG provides Continuous Monitoring of the external attack surface, ensuring that a bucket's security remains intact over time.
How it helps: If a secure bucket's permissions are accidentally reverted to public access due to an automated deployment script or human error (Configuration Drift), continuous monitoring immediately alerts the security team, minimizing the window during which the Cloud Bucket Poisoning Vector is viable.
Reporting: The reports provide prioritized risk views. A finding of Cloud Exposure contributing to a low Data Leak Susceptibility score will be flagged as a high priority with clear Reasoning and Recommendations.
Investigation Modules
These modules gather granular evidence to prove that sensitive AI data is present or that the infrastructure is exposed.
Highlight and Examples:
Archived Web Pages: This module searches web archives for various file types and directories.
Example: ThreatNG discovers an archived internal document file that contains a direct connection URL or file path pointing to the exact cloud bucket name (mycompany-ai-train-data-v1) that was flagged as exposed, definitively linking the misconfigured bucket to the critical AI training data.
Online Sharing Exposure: This module identifies the presence of organizational entities on public code and file-sharing platforms.
Example: An analyst uses this module to find a developer's GitHub Gist code snippetthat includes the logic for uploading files to the exposed cloud bucket. This confirms that the exposed bucket is an active part of the data ingestion pipeline, confirming the risk of poisoning.
Subdomain Intelligence (Ports): This module's Custom Port Scanning capability is vital for directly finding an exposed vector database instance.
Example: ThreatNG discovers an exposed subdomain that responds on a non-standard database port (e.g., 19530, used by some vector DBs), proving that the database itself is directly accessible from the public internet without proper firewall protection.
Cooperation with Complementary Solutions
ThreatNG's external validation accelerates the defense actions of internal security tools against the poisoning vector.
Cooperation with Cloud Security Posture Management (CSPM) Tools: ThreatNG's high-certainty finding of an exposed open cloud bucket is passed to a complementary CSPM tool.
Example: When ThreatNG identifies a publicly accessible bucket, the CSPM tool must immediately check the internal access policies for thatbucket and enforce stricter controls that revoke public read/write access, thereby eliminating the Cloud Bucket Poisoning Vector at its source.
Cooperation with Data Loss Prevention (DLP) Systems: The external signal of a data leak risk informs the internal data security team.
Example: ThreatNG flags the exposed bucket, instructing the complementary DLP system to execute an immediate, internal content inspection. The DLP confirms that the bucket contains files classified as proprietary intellectual property (PII), elevating the priority and triggering automated data quarantine to protect the sensitive training data.
