Eliminating Shadow AI and Vector Database Exposure

External Attack Surface Management (EASM)

Digital Risk Protection (DRP)

Security Ratings

Third-Party Risk Management

Brand Protection

What is Eliminating Shadow AI and Vector Database Exposure?

Eliminating Shadow AI and Vector Database Exposure in cybersecurity is the proactive, outside-in discovery, technical validation, and automated containment of unsanctioned Artificial Intelligence (AI) orchestrators, local Large Language Model (LLM) serving runtimes, agentic automation frameworks, Model Context Protocols (MCP), and public-facing vector databases across multi-cloud environments, developer sandboxes, and third-party supplier perimeters.

As enterprise engineering and operational teams accelerate generative AI adoption, they frequently bypass formal governance, architecture reviews, and identity controls to stand up rapid AI proofs-of-concept. This creates a critical new attack surface characterized by:

  • The "Embeddings are Safe" Fallacy: Engineering teams mistakenly treat vector databases as simple mathematical indexes containing harmless floating-point arrays. In reality, Retrieval-Augmented Generation (RAG) architectures store original unencrypted source text, Personally Identifiable Information (PII), proprietary source code, internal knowledge base documents, and customer support transcripts directly alongside high-dimensional vector embeddings in metadata fields.

  • Weak Security Defaults and Local-First Runtimes: Emerging AI tools—such as Ollama, LM Studio, Langflow, n8n, AnythingLLM, LiteLLM, Qdrant, Milvus, ChromaDB, and DuckDB—were originally engineered for local prototyping. When deployed to public cloud instances (AWS, Azure, GCP) or staging environments, they default to open HTTP REST or gRPC APIs with authentication disabled by default, exposing internal RAG pipelines directly to public search engines and automated botnets.

  • Agentic Privilege Sprawl and Shadow MCPs: Autonomous agentic frameworks (such as Clawdbot, Moltbot, and Model Context Protocol servers) are routinely provisioned with long-lived API keys, external tool-execution privileges, and unvetted database connectors, creating unmonitored lateral pivot pathways into core enterprise backends.

  • Severe Statutory Non-Compliance: Unmonitored shadow AI pipelines directly violate emerging global AI regulations—such as the EU AI Act (carrying fines of up to €35 million or 7% of global annual turnover), the NIST AI Risk Management Framework (AI RMF), ISO/IEC 42001, and statutory privacy mandates (GDPR, HIPAA)—by exposing sensitive training and context data without auditable oversight.

Traditional security tools fail to stop Shadow AI because internal Endpoint Detection and Response (EDR) sensors and Cloud Security Posture Management (CSPM) agents are rarely installed on ephemeral developer sandboxes, and legacy Web Application Firewalls (WAFs) cannot differentiate authorized API queries from unauthenticated vector searches. Eliminating Shadow AI and vector database exposure requires an unauthenticated, outside-in scouting strategy that discovers these dynamic endpoints as soon as they become reachable on the public internet.

How ThreatNG Solves Shadow AI and Vector Database Exposure

ThreatNG solves this challenge by functioning as an unauthenticated external scout that delivers The Connectorless Ground Truth. By unifying External Attack Surface Management (EASM), Digital Risk Protection (DRP), and continuous Security Ratings into a single platform, ThreatNG discovers, evaluates, categorizes, and monitors an enterprise’s dynamic digital perimeter and third-party supplier ecosystems from an adversary-centric perspective.

ThreatNG executes pure outside-in discovery without requiring software agents, cloud connectors, API keys, or pre-configured asset registries. By combining its patented recursive discovery engine with specialized Subdomain Infrastructure Exposure modules, evaluating weaponization through its 4-Dimensional (4D) Data Model, and modeling complex exploit trajectories via DarChain, ThreatNG replaces subjective compliance checklists with deterministic evidence and Legal-Grade Attribution, shutting down Shadow AI conduits before external threat actors exploit them.

External Discovery for Shadow AI and Vector Infrastructure

Addressing Shadow AI requires an autonomous discovery engine that can uncover unmanaged endpoints, ephemeral developer hosts, and exposed AI infrastructure across the global internet. ThreatNG establishes this inventory baseline through connectorless external discovery.

  • Patented Recursive Discovery for Unmanaged Cloud Environments: Starting from core corporate seeds (apex domains, brand names, or Autonomous System Numbers/ASNs), ThreatNG iteratively expands outward. As new subdomains, DNS records, or netblocks emerge, the engine feeds them back in as fresh discovery seeds. This recursive process uncovers forgotten developer staging sandboxes, QA testing servers, and regional subsidiary portals hosted across AWS, Azure, GCP, and regional VPS providers that lack corporate logging agents.

  • Subdomain Infrastructure Exposure Mapping (AI & LLM Runtimes): Within Subdomain Intelligence, ThreatNG inspects discovered subdomains and open ports for running AI frameworks. It detects exposed orchestration interfaces, automated workflow engines, and local model servers—including Langflow, self-hosted n8n, AnythingLLM, LM Studio, LiteLLM, Ollama, OpenAI-compatible APIs, Clawdbot, and Moltbot.

  • Vector Database and Embedding Store Discovery: Discovers unauthenticated, public-facing vector databases, embedding endpoints, and semantic caches. It maps exposed instances of Qdrant, Milvus, ChromaDB, local Pinecone proxies, DuckDB, and Weaviate listening on standard and non-standard network ports.

  • Model Context Protocol (MCP) and Agentic Tool Discovery: Identifies exposed Model Context Protocol (MCP) servers, General SSE MCP endpoints, Next.js MCP instances, Enterprise MCP gateways, and tool-use endpoints (such as Playwright MCP and HighByte connectors) that expose internal systems to agentic manipulation.

  • Non-Human Identity (NHI) and Leaked AI Key Discovery: ThreatNG continuously scans public version control platforms (such as GitHub, GitLab, and Bitbucket), paste repositories, and developer forums for hardcoded AI service tokens, OpenAI/Anthropic/HuggingFace API keys, database connection strings, and cloud access credentials accidentally committed by internal developers or third-party AI contractors.

  • Third-Party Dependency and SaaS Mapping (SaaSqwatch): Evaluates public digital exhaust—such as DNS Canonical Name (CNAME) routing chains, HTTP response headers, and certificates—to map third-party AI SaaS tools, managed vector services, and cloud dependencies operating across the enterprise supply chain.

External Assessment

ThreatNG elevates AI security from passive policy audits to deterministic, evidence-backed assessment using its Known Vulnerability Exposure Verification (KVEV) engine, proprietary Security Ratings, and 4-Dimensional (4D) Data Model. The 4D model cross-references National Vulnerability Database (NVD) baselines, 30-day Exploit Prediction Scoring System (EPSS) probabilities, Cybersecurity and Infrastructure Security Agency (CISA) Known Exploited Vulnerabilities (KEV) listings, and verified Proof-of-Concept (PoC) exploit scripts in DarCache eXploit.


  • Detailed Assessment Example 1: Unauthenticated Vector Database Data Leak Susceptibility: When ThreatNG discovers a public-facing instance of Qdrant, Milvus, or ChromaDB, it performs non-intrusive, unauthenticated HTTP/gRPC handshake verifications. It checks whether the endpoint responds to schema queries or returns unauthenticated collection lists without requiring API authentication tokens. ThreatNG calculates a dedicated A-F Data Leak Susceptibility rating. If an external vector database exposes public read/write capabilities on port 6333 or 19530 containing production embeddings and raw metadata, ThreatNG assigns an immediate F rating, providing empirical proof of active data exposure.

  • Detailed Assessment Example 2: KVEV Engine Validation on Exposed AI Orchestrators: When ThreatNG identifies an internet-facing Langflow, Ollama, or n8n instance, the KVEV engine performs live, unauthenticated checks against known vulnerabilities (such as remote code execution or arbitrary file read flaws). It confirms public reachability, checks against the CISA KEV catalog, calculates 30-day EPSS weaponization probabilities, and cross-references active exploit scripts in DarCache eXploit. If an unmanaged Ollama server runs a version susceptible to arbitrary model file pulling or remote execution with an EPSS score of 0.91, ThreatNG delivers empirical proof of exploitability and establishes immediate material risk for executive committees.

  • Detailed Assessment Example 3: Non-Human Identity (NHI) Exposure and AI Agent Tool Permissions: ThreatNG evaluates external exposure variables—including open non-standard ports, accessible environment variables, public cloud configurations, and unvetted webhook endpoints—to locate exposed programmatic machine identities. It identifies exposed API keys and service tokens used by autonomous AI agents and computes an NHI Exposure Rating (A through F). If a developer hardcodes an unmanaged cloud service key into a public GitHub repository that grants an AI agent write permissions to production cloud buckets, ThreatNG calculates the blast radius, proving identity lifecycle controls failed.

  • Detailed Assessment Example 4: Subdomain Takeover Susceptibility on Decommissioned AI Prototypes: AI development teams frequently point corporate subdomains (ai-test.company.com) to ephemeral cloud instances or third-party hosting platforms and abandon them once the experiment concludes. ThreatNG cross-references discovered subdomains across multi-cloud environments against a catalog of over 60 cloud services (such as AWS S3, Azure, Heroku, and GitHub) to verify whether the underlying host is unclaimed. When an authoritative CNAME points to an unclaimed resource that returns an HTTP 404 status, ThreatNG assigns an A through F Subdomain Takeover Susceptibility rating, showing that adversaries can hijack an authentic company subdomain to host malicious AI models or harvest employee prompt inputs.

  • Detailed Assessment Example 5: Web Application Hijack Susceptibility on AI Chat Consoles: ThreatNG inspects public-facing AI chat interfaces and internal LLM web consoles across all discovered subdomains for missing or weak HTTP security headers—specifically evaluating subdomains missing Content-Security-Policy (CSP), HTTP Strict Transport Security (HSTS), X-Content-Type-Options, and X-Frame-Options. It assigns an A through F Web Application Hijack Susceptibility rating, establishing empirical evidence for compliance auditors of whether user-facing AI chat interfaces enforce protections against clickjacking and prompt-injection-driven cross-site scripting (XSS).

Strategic Reporting

ThreatNG standardizes the communication of AI exposure risks by converting raw outside-in telemetry, attack graphs, and digital risk indicators into structured, auditable records designed for compliance directors, Chief Data Officers (CDOs), CISOs, and corporate boards.

  • Executive Security Ratings Reports: ThreatNG converts complex AI vulnerabilities, exposed runtimes, and digital risk indicators into standardized A through F security ratings across categories including Cyber Risk Exposure, Data Leak Susceptibility, Non-Human Identity (NHI) Exposure, and Supply Chain & Third-Party Exposure. Using the transparent, penalty-based DarcRadar formula, ThreatNG provides objective ratings backed by deterministic evidence rather than theoretical math, giving board audit committees an unvarnished benchmark of AI governance.

  • Correlation Evidence Questionnaires (CEQs): Dynamically generate targeted questionnaires based on confirmed external discoveries. The CEQ acts as an EASM-to-Audit Translation Layer, transforming unauthenticated outside-in discoveries—such as an open Milvus database, an unpatched Langflow instance, or an exposed OpenAI API key—into targeted, auditable inquiries mapped directly to regulatory frameworks across four functional pillars: Technical, Strategic, Operational, and Financial.

  • EU AI Act & Global AI Governance Reporting: Maps discovered shadow AI infrastructure, unmonitored model endpoints, and exposed vector databases directly to specific articles of the EU AI Act, the NIST AI RMF, and ISO/IEC 42001. This report gives compliance officers the documentation needed to avoid statutory non-compliance penalties and demonstrate governance oversight of generative AI systems.

  • U.S. SEC Cybersecurity Disclosures Report: Connects material AI data leaks, unauthorized cloud workloads, and third-party AI dependencies directly to corporate filings (such as Form 10-K Item 106 risk management governance and Form 8-K incident materiality determinations), documenting continuous, auditable technical oversight over external AI conduits.

  • Forensic Evidence Packages for Rapid Remediation: When ThreatNG verifies an exposed vector database or an unauthenticated AI orchestrator, it compiles a comprehensive forensic evidence package containing technical markers, DNS resolution histories, HTTP response headers, affected URLs, and proof of ownership to support immediate firewall blocking, instance shutdown, and credential revocation.

Continuous Monitoring

Because engineering teams deploy new model endpoints, spin up ephemeral cloud GPU instances, and alter API routes daily, periodic quarterly audits create dangerous exposure windows. ThreatNG delivers 24/7 continuous external surveillance across global cloud environments and perimeters.

The platform tracks asset state changes, newly registered subdomains, modified DNS records, fresh certificate issuances, and emerging zero-day vulnerabilities in real time. If a developer exposes a new Qdrant vector database to public traffic or commits an administrative Anthropic key to GitHub, ThreatNG detects the configuration drift immediately. Furthermore, ThreatNG incorporates its Overwatch capability—a cross-entity vulnerability intelligence system that instantly evaluates exposure across an entire portfolio of subsidiaries, business units, and supply chain partners whenever an emerging zero-day vulnerability in an AI framework (such as LangChain, Ollama, or vLLM) is announced, pinpointing every exposed model server within seconds.

Investigation Modules

ThreatNG features specialized investigation modules that allow security analysts and compliance officers to trace technical exposures, investigate developer leaks, and evaluate the full intelligence context of Shadow AI deployments.

  • Detailed Module Example 1: The DarChain Exploit Path Mapping Engine: DarChain (Digital Attack Risk Contextual Hyper-Analysis Insights Narrative) chains isolated technical, credential, and environmental discoveries into predictive attack graphs. For example, DarChain maps how an attacker discovers an abandoned staging subdomain hosting an unauthenticated Langflow orchestrator, exploits that interface to query an internal vector database containing raw customer support transcripts, extracts hardcoded cloud service credentials stored in the vector metadata, and pivots into core corporate AWS S3 data lakes. Instead of alerting on disconnected signals, DarChain identifies the exact Attack Path Choke Point—such as applying network-level access controls to the Langflow portal—proving that severing that specific node collapses the entire AI data exfiltration chain before execution begins.

  • Detailed Module Example 2: Sensitive Code Exposure Module: Continuously monitors public version control platforms (such as GitHub, GitLab, and Bitbucket) and paste sites for leaked corporate secrets. This module uncovers hardcoded API keys for commercial AI providers (OpenAI, Anthropic, Cohere), private Hugging Face tokens, vector database connection strings, and cloud infrastructure keys committed by internal developers or third-party AI contractors. The module captures exact repository URLs, commit timestamps, and file paths in real time, delivering undeniable proof of whether software development practices comply with data protection policies.

  • Detailed Module Example 3: Subdomain Infrastructure Exposure Module (Shadow AI Investigation): Within Subdomain Intelligence, this module actively inspects discovered subdomains for exposed administrative interfaces, development pipelines, and automated tools. It detects exposed orchestration frameworks (including Langflow, self-hosted n8n, AnythingLLM, LM Studio, LiteLLM, Ollama, OpenAI-compatible APIs, and Clawdbot/Moltbot), vector databases (Qdrant, Milvus, local Pinecone, and DuckDB), and Model Context Protocols (MCP), uncovering the exact technology stacks driving shadow AI initiatives.

  • Detailed Module Example 4: Dark Web Presence and Infostealer Intelligence: Continuously monitors underground marketplaces, paste sites, and infostealer malware logs for compromised corporate credentials, session cookies, and corporate mentions. This module parses log clouds to identify when developer credentials, enterprise AI platform logins, or active Single Sign-On (SSO) session tokens appear in botnet archives (such as RedLine, Lumma, or Vidar), alerting defenders to identity compromise before threat actors use those credentials to access internal AI pipelines.

  • Detailed Module Example 5: Cybersecurity AI Prompts (DarcPrompt): DarcPrompt packages verified attack-surface context and attack-path findings into structured prompt blueprints. Featuring specialized personas—such as Shadow IT and AI, External Attack Paths, and External GRC Assessment—DarcPrompt applies strict architectural constraints that bind the prompt to ThreatNG's proprietary ground truth. Through an Air-Gapped Handoff, security analysts safely copy these blueprints into their internal private enterprise AI systems to draft AI compliance memos, firewall change requests, and executive threat briefings without exposing sensitive model infrastructure data to public AI services.

Intelligence Repositories

ThreatNG centralizes and structures threat intelligence through the DarCache intelligence engine, providing an interconnected dynamic ecosystem that grounds AI defense in empirical adversary reality:

  • DarCache Vulnerability & eXploit: Integrates NVD baselines, CISA KEV listings, 30-day EPSS probabilities, and verified PoC exploit pointers to evaluate whether perimeter software flaws in AI runtimes and web gateways are actively weaponized in the wild.

  • DarCache Infostealer: Parses dark web logs, Telegram channels, and illicit cloud archives for compromised corporate credentials, session cookies, and Primary Refresh Tokens (PRTs), allowing teams to detect identity theft before adversaries use valid developer logins to access proprietary AI models.

  • DarCache Dark Web & Rupture: Scans underground forums, paste sites, and dark web sources for threats to brand assets and personnel, while tracking compromised corporate credentials, session cookies, and data leaks across all domain permutations.

  • DarCache 8-K & ESG: Tracks SEC Form 8-K filings, global ESG violations, and corporate regulatory disclosures, providing non-technical governance indicators that connect digital attack surface risks directly to financial materiality, board oversight, and legal exposure.

  • DarCache Ransomware: Tracks active ransomware cartels and their specific tactics, techniques, and procedures (TTPs), monitoring whether threat actors target proprietary corporate training datasets or use false-flag brand assets for extortion.

  • DarCache Bug Bounty: Aggregates and analyzes historical bug bounty program disclosures, researcher activity trends, and crowdsourced exploit patterns to evaluate which public perimeter assets and AI applications are under active scrutiny by external researchers.

  • DarCache Mobile: Detects hardcoded access credentials, security keys, and platform-specific identifiers within public mobile applications, discovering mobile AI backends and embedded API keys that expose external entry points.

  • DarCache BIN: Monitors Bank Identification Numbers (BINs) to identify and prevent potential payment card fraud across digital transactional and e-commerce assets.

Cooperation with Complementary Solutions

ThreatNG functions as an external intelligence scout that cooperates seamlessly with complementary solutions across enterprise governance, risk, and security operations to eliminate Shadow AI and vector database exposures.

  • Cooperation with Cloud Security Posture Management (CSPM) and CAASM Platforms: ThreatNG passes newly discovered unmanaged cloud assets, public vector database endpoints, and shadow AI subdomains into complementary solutions (enterprise CSPM platforms, CAASM tools, and CMDBs). IT and cloud security teams use this outside-in feed to reconcile discovered assets against internal cloud accounts, ensuring that rogue AWS, Azure, and GCP instances hosting AI runtimes are immediately brought under centralized governance, tagged, and monitored.

  • Cooperation with Security Orchestration, Automation, and Response (SOAR): ThreatNG delivers pre-correlated Context Objects, forensic evidence packages, and DarChain attack paths to complementary solutions (enterprise SOAR platforms) via an API. When ThreatNG discovers an unauthenticated vector database or an unpatched Langflow instance, the SOAR platform executes automated response workflows—updating perimeter firewall rules and Cloud Security Groups to restrict public access to authorized IP ranges, terminating unapproved cloud instances, and opening urgent containment tickets in Jira.

  • Cooperation with Identity and Access Management (IAM) and Secrets Vaults: ThreatNG passes verified leaked Non-Human Identities (NHIs), AI service tokens, and database credentials discovered in public repositories or dark web logs to complementary solutions (enterprise IAM platforms and secrets management vaults). The IAM platform immediately invalidates the compromised credentials, revokes active session tokens, and initiates automated key rotation, shutting down unauthorized access pathways into production AI pipelines.

  • Cooperation with Governance, Risk, and Compliance (GRC) Platforms: ThreatNG feeds empirical, outside-in technical evidence, continuous Security Ratings, and Correlation Evidence Questionnaires (CEQs) into complementary solutions (enterprise GRC platforms). While traditional GRC tools manage qualitative AI policies and vendor surveys, ThreatNG adds a continuous technical validation layer—automatically proving that public perimeters comply with the EU AI Act, NIST AI RMF, and ISO/IEC 42001, and populating risk registers with verifiable proof.

  • Cooperation with Web Application Firewalls (WAFs) and API Gateways: ThreatNG identifies public-facing AI endpoints, shadow LLM consoles, and origin server IP addresses that bypass corporate reverse proxies. It feeds these findings directly into complementary solutions (enterprise WAFs and API management gateways) to enforce mutual TLS (mTLS), apply virtual patches for known framework flaws, and enforce strict rate-limiting on generative model APIs.

Examples of ThreatNG Helping Organizations

  • Discovering and Securing an Unauthenticated RAG Vector Database: A multinational financial services enterprise initiated an internal pilot to build an AI-powered financial research assistant. The external engineering contractor deployed a Qdrant vector database on a standalone AWS EC2 instance to store document embeddings. The contractor left port 6333 exposed to the public internet with authentication disabled. ThreatNG’s recursive discovery engine identified the unmanaged subdomain (research-ai-test.firm.com) and its open port. ThreatNG’s Data Leak Susceptibility module conducted an unauthenticated probe, confirming that the instance returned collection schemas containing full-text extracts of proprietary merger-and-acquisition research memos stored in metadata. ThreatNG assigned an immediate F Data Leak Susceptibility rating and generated a forensic evidence package. Armed with this verifiable proof, the enterprise security team immediately closed the public security group rule and enforced private VPC peering, neutralizing a catastrophic insider-trading data exposure before malicious scrapers indexed the endpoint.

  • Neutralizing an Exposed Langflow Orchestrator and Leaked AI Keys: A healthcare organization contracted a third-party software agency to build an automated patient inquiry routing bot. ThreatNG’s Subdomain Infrastructure Exposure module discovered an exposed, unauthenticated Langflow orchestration console hosted on an unmonitored Google Cloud instance. At the same time, ThreatNG’s Sensitive Code Exposure module found that the contractor had committed production OpenAI API keys and internal database connection strings to a public GitHub repository. DarChain modeled the full exploit path, demonstrating that an attacker could access the Langflow interface, modify agentic tool configurations using the leaked database strings, and execute unauthorized queries against patient records. ThreatNG pinpointed the Langflow console and the GitHub key as critical Attack Path Choke Points. The organization revoked the API keys, decommissioned the public Langflow instance, and enforced corporate AI development standards, preventing severe HIPAA violations and regulatory penalties under global AI privacy standards.

Examples of ThreatNG Working with Complementary Solutions

  • Working with SOAR and Firewalls to Contain Exposed Vector Databases: ThreatNG discovers an unauthenticated Milvus vector database listening on port 19530 on an unmanaged cloud host, exposing internal customer service transcripts. ThreatNG transmits a pre-correlated Context Object to complementary solutions (an enterprise SOAR platform). The SOAR system automatically triggers API commands to complementary solutions (cloud provider security groups and perimeter firewalls) to modify ingress rules, restricting port 19530 access to corporate VPN ranges exclusively, while opening an urgent remediation ticket in Jira to enforce API key authentication.

  • Working with CSPM and IAM to Automate Shadow AI Governance: ThreatNG’s recursive discovery discovers an unmonitored Ollama LLM runtime deployed on an unmanaged developer instance in AWS, running software with a known remote execution vulnerability. In parallel, ThreatNG detects an exposed AWS IAM service key in a public code repository. ThreatNG transmits a pre-correlated Context Object to complementary solutions (an enterprise CSPM platform and an enterprise IAM platform). The IAM platform immediately revokes the exposed service key, while the CSPM imports the discovered Ollama host into the central cloud inventory, applies baseline security policies, and alerts the cloud security team to enforce centralized logging.

Frequently Asked Questions

Why are vector databases considered a high-risk data leakage vector?

Vector databases are a high-risk data leakage vector because modern RAG pipelines do not store mathematical vectors in isolation; they store original, unencrypted source text, customer PII, internal proprietary code, and sensitive documents in accompanying metadata fields. If an unauthenticated vector database is exposed to the internet, attackers can query the endpoint to dump the underlying source text directly.

How does ThreatNG discover Shadow AI without installing internal software agents?

ThreatNG operates entirely as an unauthenticated external scout. It continuously monitors public DNS zone files, SSL/TLS certificate transparency logs, BGP routing announcements, public code repositories, and network ports across the open web, identifying exposed model servers (Ollama, LM Studio), orchestrators (Langflow, n8n), and vector stores (Qdrant, Milvus) from an external adversary's viewpoint.

How does ThreatNG support compliance with the EU AI Act and global AI governance frameworks?

ThreatNG maps confirmed external AI discoveries—including unmonitored shadow AI runtimes, exposed vector databases, and leaked model keys—directly to specific requirements of the EU AI Act, the NIST AI RMF, and ISO/IEC 42001. It dynamically generates Correlation Evidence Questionnaires (CEQs) that turn raw technical findings into auditable governance evidence, helping enterprises demonstrate continuous oversight and avoid severe non-compliance penalties.

Immediate Actionable Verification Checklist

  1. Map Your Extended Digital Perimeter for AI Runtimes: Run ThreatNG across all corporate apex domains, netblocks, and brand names to establish an unauthenticated baseline of public-facing assets and shadow cloud environments.

  2. Execute a Subdomain Infrastructure Audit for AI Frameworks: Inspect ThreatNG’s Subdomain Infrastructure Exposure module to identify exposed instances of Langflow, n8n, AnythingLLM, LM Studio, LiteLLM, Ollama, and agentic tools.

  3. Audit the External Perimeter for Unauthenticated Vector Databases: Verify whether public-facing endpoints expose Qdrant, Milvus, ChromaDB, local Pinecone proxies, or DuckDB instances listening on standard or non-standard ports.

  4. Scan Public Repositories for Leaked AI Tokens and Machine Secrets: Use ThreatNG’s Sensitive Code Exposure module to identify hardcoded OpenAI, Anthropic, Hugging Face, or cloud infrastructure credentials committed to public GitHub and GitLab repositories.

  5. Feed External Context Objects into Complementary CSPM and SOAR Platforms: Configure automated feeds of ThreatNG’s pre-correlated Context Objects into complementary CSPM, SOAR, and IAM solutions to automate perimeter firewall blocking, asset onboarding, and credential rotation.