MITRE ATLAS
What is MITRE ATLAS?
MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a globally recognized, curated knowledge base and threat modeling matrix of adversary tactics, techniques, and procedures (TTPs) targeting artificial intelligence and machine learning (ML) systems.
Modeled on the foundation of the widely adopted MITRE ATT&CK framework, ATLAS provides security analysts, AI developers, and red teams with a standardized taxonomy to identify, describe, evaluate, and defend against AI-specific cyber threats. While legacy security frameworks focus on operating systems, enterprise networks, and cloud infrastructure, MITRE ATLAS addresses threats operating directly within the AI lifecycle—including machine learning pipelines, Large Language Models (LLMs), agentic orchestration workflows, vector databases, and model training environments.
Core Components of the MITRE ATLAS Framework
The ATLAS framework structures threat intelligence into four primary analytical components:
Tactics (Adversarial Objectives): The high-level technical goals an adversary attempts to accomplish during an attack on an AI system, ranging from initial reconnaissance to model compromise and data exfiltration.
Techniques and Sub-Techniques (Methods of Attack): The specific technical actions an attacker takes to achieve a tactical objective, designated with unique identifiers (such as AML.TXXXX).
Case Studies (Real-World Evidence): Documented, real-world security incidents, forensic post-mortems, and validated offensive research demonstrating how adversaries have targeted commercial, industrial, or academic AI implementations.
Mitigations (Defensive Countermeasures): Specific engineering controls, guardrails, architecture adjustments, and monitoring practices designed to neutralize or detect specific ATLAS techniques.
Key Tactical Phases in MITRE ATLAS
MITRE ATLAS covers the adversarial lifecycle across distinct tactical phases tailored to artificial intelligence:
Reconnaissance: Gathering intelligence on target AI systems, including model types, public APIs, underlying frameworks, system prompts, and publicly referenced training data sources.
Resource Development: Acquiring or weaponizing AI infrastructure, such as publishing poisoned open-source datasets, hosting backdoored model weights on public registries, or acquiring adversarial prompt injection tools.
Initial Access: Establishing an entry vector into the target environment via exposed machine learning endpoints, unsecured API gateways, compromised credentials, or third-party AI integrations.
ML Model Access: Gaining direct or black-box query access to model inference interfaces, allowing the adversary to probe responses, analyze confidence scores, or harvest operational feedback.
Execution: Running unauthorized code or malicious instructions within the AI workflow, such as direct or indirect prompt injection in Large Language Models.
Persistence: Maintaining long-term footholds within AI systems, such as manipulating autonomous AI agent configurations or embedding hidden backdoors in training pipelines.
Defense Evasion: Modifying inputs using adversarial perturbations to deceive model classifiers while appearing normal to human auditors and traditional security controls.
Discovery: Enumerating internal AI infrastructure, connected vector databases, Retrieval-Augmented Generation (RAG) indexes, and available agent tool connectors.
Collection and Exfiltration: Extracting proprietary model weights, internal training corpora, private user conversation histories, or sensitive vector embeddings.
Impact: Disrupting business operations, inducing algorithmic hallucinations, poisoning decision models, or causing financial and reputational harm through manipulated model outputs.
MITRE ATLAS vs. MITRE ATT&CK
Understanding the distinction between MITRE ATT&CK and MITRE ATLAS highlights why traditional cybersecurity testing falls short for AI:
Target Environment: MITRE ATT&CK focuses on conventional IT enterprise networks, physical endpoints, cloud providers, and identity systems. MITRE ATLAS focuses specifically on algorithms, neural networks, inference APIs, data pipelines, and agentic reasoning architectures.
Adversarial Mechanics: ATT&CK assesses operating system exploits, malware execution, network sniffing, and Active Directory privilege escalation. ATLAS assesses data poisoning, model extraction, adversarial perturbation, training data inversion, and prompt manipulation.
Relationship: ATLAS directly complements ATT&CK. Attackers frequently use ATT&CK techniques (such as compromising a cloud server or stealing API credentials) to gain the necessary access to execute ATLAS techniques (such as extracting model weights or poisoning a RAG vector store).
Strategic Significance for Enterprise Cyber Resilience
Implementing MITRE ATLAS provides organizations with tangible strategic advantages across AI development and operations:
Systematic AI Threat Modeling: Enables security architects to evaluate machine learning systems against realistic attacker playbooks during the architectural design phase rather than treating AI models as uninspectable black boxes.
Objective Red Teaming and Adversarial Testing: Supplies internal red teams and third-party penetration testers with a validated framework to simulate targeted attacks against production LLMs and autonomous agents.
Defensible Regulatory Compliance: Supports compliance with emerging AI regulations and standards—such as ISO/IEC 42001, the EU AI Act, and the NIST AI Risk Management Framework (AI RMF)—by delivering documented, repeatable risk assessments.
Optimized Security Investments: Directs security engineering budgets toward high-risk vulnerabilities unique to AI architectures, such as input validation guardrails, vector store isolation, and secrets management for non-human identities.
Frequently Asked Questions
What does ATLAS stand for in MITRE ATLAS?
ATLAS stands for Adversarial Threat Landscape for Artificial-Intelligence Systems.
How does MITRE ATLAS address Generative AI and LLMs?
MITRE ATLAS provides explicit coverage of Generative AI and LLM attack vectors, incorporating techniques for direct prompt injection, indirect prompt injection via external web contexts, training data extraction, and tool-use manipulation within autonomous agent frameworks.
Is MITRE ATLAS a replacement for OWASP Top 10 for LLMs?
No. The OWASP Top 10 for LLMs provides a concise list of high-priority security flaws and developer best practices. MITRE ATLAS acts as an exhaustive threat modeling matrix that models adversary behavior, technical tactics, and complete multi-step attack campaigns.
Operationalizing the MITRE ATLAS Framework with ThreatNG
The MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) framework is the globally recognized taxonomy documenting adversary tactics, techniques, and procedures (TTPs) targeting artificial intelligence and machine learning (AI/ML) systems. While conventional security testing evaluates operating systems and static code perimeters, modern adversaries exploit the foundational layers of AI architectures—targeting public inference application programming interfaces (APIs), unmanaged model staging endpoints, exposed vector stores, and leaked programmatic keys to execute data poisoning, model extraction, and pipeline manipulation.
Traditional cybersecurity architectures suffer from the Contextual Certainty Deficit because internal tools rely on internal network agents, software connectors, and static vulnerability scans. Consequently, security teams remain unaware of how external threat actors identify and exploit public AI assets from the open internet.
ThreatNG operationalizes the MITRE ATLAS framework by functioning as an unauthenticated external scout. 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 complete public digital perimeter alongside its AI footprint from an outside-in, adversary-centric perspective. Through its External Adversary View, ThreatNG translates raw external findings directly into MITRE ATT&CK for enterprise IT and MITRE ATLAS for AI/ML systems. By constructing deterministic attack paths via DarChain, evaluating weaponization through its 4-Dimensional (4D) Data Model, and delivering Legal-Grade Attribution, ThreatNG eliminates exposure vectors across the modern AI kill chain without requiring internal software agents, API access keys, or administrative credentials.
External Discovery
Adversaries executing campaigns aligned with MITRE ATLAS begin with Reconnaissance (ATLAS-TA0000) and Initial Access (ATLAS-TA0001). ThreatNG counters these adversary tactics by identifying the entire external AI attack surface through connectorless external discovery.
Connectorless Asset and Perimeter Discovery: ThreatNG maps the entire public-facing digital footprint using unauthenticated discovery with zero internal connectors, software agents, or network credentials. It continuously analyzes public domain registries, authoritative Domain Name System (DNS) zone files, Secure Sockets Layer/Transport Layer Security (SSL/TLS) certificate transparency logs, Regional Internet Registry (RIR) databases, and Border Gateway Protocol (BGP) routing announcements to catalog every public Internet Protocol (IP) block, subdomain, cloud environment, and web application hosting AI systems.
Patented Recursive Discovery for Shadow AI: Starting from an initial seed entity (such as an apex domain, corporate brand name, or Autonomous System Number/ASN), 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 developer staging sandboxes, ephemeral MLOps test environments, and rogue AI implementations deployed across multi-cloud providers (AWS, Microsoft Azure, Google Cloud Platform) and regional hosting providers, curing compliance blindness before adversaries exploit unmonitored infrastructure.
Third-Party AI Dependency and SaaS Mapping (SaaSqwatch): ThreatNG evaluates public digital exhaust—including DNS Canonical Name (CNAME) routing chains, Hypertext Transfer Protocol (HTTP) headers, and SSL/TLS certificates—to discover sanctioned and unsanctioned external AI vendors, hosted model hubs, vector databases, and autonomous agent frameworks used across corporate business units.
Adversary Lookalike and Typosquat Discovery: ThreatNG continuously discovers newly registered, typosquatted, and lookalike domain permutations (such as homoglyphs and transposed characters) registered across global domain registrars. It identifies active Mail Exchange (MX) records and SSL/TLS certificates configured to impersonate enterprise AI tools or customer-facing chat interfaces, detecting malicious staging infrastructure before phishing, brand abuse, or prompt-harvesting campaigns launch.
Subsidiary and Extended Ecosystem Scoping: Because ThreatNG operates without internal credentials or vendor permissions, organizations can execute unauthenticated discovery across operating subsidiaries, prospective acquisition targets (M&A due diligence), and third-party partners. This establishes baseline visibility across the extended ecosystem to identify sprawling AI deployments operating across partner networks.
External Assessment
ThreatNG elevates ATLAS risk evaluation from theoretical checklists 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 code in DarCache eXploit.
Detailed Assessment Example 1: Known Vulnerability Exposure Verification (KVEV) on AI Infrastructure: When ThreatNG discovers an internet-facing AI inference gateway, model server, or vector database interface, the KVEV engine performs live, unauthenticated checks. It confirms public reachability, checks for inclusion on the CISA KEV catalog, evaluates 30-day EPSS weaponization probabilities, and cross-references active exploit scripts in DarCache eXploit. This maps directly to ATLAS Reconnaissance (ATLAS-TA0000) and Initial Access (ATLAS-TA0001), proving whether an exposed AI service is actively vulnerable to remote code execution.
Detailed Assessment Example 2: Non-Human Identity (NHI) Exposure and Machine Secret Assessment: AI models and autonomous agents use machine credentials to interact with backend data stores and external language models. 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 OpenAI API keys, Anthropic tokens, Hugging Face user secrets, and LangChain orchestration credentials, computing an NHI Exposure Rating (A through F). This directly mitigates Credential Harvesting (ATLAS-TT0010) and prevents ML Service Abuse (ATLAS-TA0006).
Detailed Assessment Example 3: Subdomain Takeover Susceptibility Verification: When an experimental AI model or temporary tool gateway is decommissioned, DNS CNAME records can be left pointing to unclaimed cloud PaaS, serverless, or storage resources. ThreatNG cross-references discovered subdomains against an extensive catalog of over 60 cloud services (including AWS S3, Azure, Heroku, Vercel, and GitHub) and validates whether the resource is unclaimed. It assigns an A through F Subdomain Takeover Susceptibility rating, preventing threat actors from claiming abandoned resources to host malicious proxy servers that intercept training data, harvest credentials, or execute ML Pipeline Manipulation (ATLAS-TA0005).
Detailed Assessment Example 4: Insecure AI Interface and Insecure Header Analysis: ThreatNG inspects public AI application endpoints, web chat widgets, and inference gateways 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, as well as deprecated headers. It generates an A through F Web Application Hijack Susceptibility rating to determine whether a customer-facing AI portal is vulnerable to client-side script injection, clickjacking, or indirect prompt injection via manipulated web contexts, directly addressing vulnerabilities that facilitate Exfiltration of ML Artifacts (ATLAS-TA0009).
Detailed Assessment Example 5: Data Leak Susceptibility on Exposed Vector Stores and Training Buckets: ThreatNG evaluates public cloud storage buckets, open database ports, and external web directories across the perimeter. It assigns an A through F Data Leak Susceptibility rating to pinpoint unprotected cloud storage containing Retrieval-Augmented Generation (RAG) vector embeddings, model weights, or cached training datasets, preventing adversaries from executing Training Data Poisoning (ATLAS-TT0003/AML.T0020) and Exfiltration of ML Artifacts (ATLAS-TA0009).
Strategic Reporting
ThreatNG standardizes the communication of verified AI exposures by converting raw outside-in discoveries, infrastructure graphs, and technical risk metrics into structured, auditable records for technical teams, executive leadership, and compliance auditors.
External GRC Assessment and MITRE ATLAS Mapping Reports: ThreatNG automatically correlates raw external discoveries—such as exposed APIs, unmanaged cloud storage, open database ports, and leaked secrets—directly into strategic narratives aligned with MITRE ATT&CK for enterprise IT and MITRE ATLAS for AI/ML systems. This dual-framework mapping contextualizes technical flaws into specific tactics (such as Initial Access, ML Service Abuse, and Exfiltration of ML Artifacts), providing CISOs with the evidence-based business context required to brief executive boards and audit committees.
Executive Security Ratings Reports: ThreatNG converts complex vulnerability metrics, exposed configurations, and digital risk indicators into standardized A through F security ratings across categories including Cyber Risk Exposure, Data Leak Susceptibility, Supply Chain & Third Party Exposure, and Non-Human Identity (NHI) Exposure. This enables CISOs to present objective AI exposure trends and posture improvements directly to executive leadership.
Correlation Evidence Questionnaires (CEQs): ThreatNG dynamically generates Correlation Evidence Questionnaires based on confirmed external discovery and assessment results. The CEQ acts as an EASM-to-Audit Translation Layer, transforming unauthenticated outside-in discoveries into targeted, auditable inquiries mapped directly to regulatory frameworks across four functional pillars: Technical, Strategic, Operational, and Financial.
U.S. SEC Cybersecurity Disclosures Report: The report aligns an organization's public regulatory filings (such as Form 10-K Item 106 and Form 8-K Item 1.05 disclosures) with the verifiable technical reality of its external attack surface. It eliminates the "Disclosure Disconnect" and protects corporate officers from regulatory penalties regarding AI Washing or undisclosed material cyber risks.
Forensic Evidence Packages: When ThreatNG verifies an active vulnerability on an AI server, an exposed vector database, an unauthorized chatbot, or a dangling DNS record, it generates a detailed forensic evidence package containing technical markers, DNS resolution histories, HTTP response headers, affected URLs, and proof of ownership to support engineering remediation, insurance reviews, registrar takedowns, and legal attribution.
Continuous Monitoring
Because engineering teams deploy microservices rapidly and cloud configurations change continuously, static periodic assessments fail to contain adversarial machine learning risks. ThreatNG delivers 24/7 continuous external surveillance across the extended digital footprint.
The platform tracks asset state changes, newly registered subdomains, modified DNS records, fresh certificate issuances, and emerging zero-day vulnerabilities in real time. If an unauthorized developer team exposes an internal model to public traffic, spins up an unauthenticated vector database, or leaves an AI agent running on a public IP, 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 a zero-day vulnerability affecting an AI framework or model server is disclosed, identifying every affected external asset within seconds.
Investigation Modules
ThreatNG features specialized investigation modules that allow security analysts to inspect discovered infrastructure, trace developer leaks, and evaluate the full technical context of adversarial machine learning risks.
Detailed Module Example 1: Subdomain Infrastructure Exposure Module (AI Framework and Vector Store Detection): Operating within Subdomain Intelligence, this module actively inspects discovered subdomains for exposed AI and agentic infrastructure. It detects exposed AI Orchestration Frameworks (including Langflow, self-hosted n8n, AnythingLLM, LM Studio, LiteLLM, Ollama, OpenAI Compatible APIs, and Clawdbot/Moltbot). In the Data Storage category, it detects exposed Vector Databases and Neural Memory stores (including QDrant, Milvus, local Pinecone, and DuckDB). In Network Protocols, it discovers Model Context Protocols (MCP) and AI Inter-Process Communication channels (such as Server-Sent Events/SSE, Next.js MCP, Browser Automation, General SSE MCP, MCP Inspector, Enterprise MCP, and Playwright MCP). Discovering these endpoints externally proves an immediate exposure to AI Agent Tool Invocation (AML.T0101) and RAG Data Poisoning (AML.T0020).
Detailed Module Example 2: Sensitive Code Exposure Module: ThreatNG continuously monitors public code repositories (such as GitHub, GitLab, and Bitbucket) and paste sites for leaked corporate secrets. This module uncovers hardcoded API keys (including OpenAI, Anthropic, Google Cloud AI, and Hugging Face tokens), private SSH keys, Jenkins credentials, and database connection strings committed by internal developers or contractors. Detecting exposed secrets prevents threat actors from gaining direct access to inference endpoints or exfiltrating proprietary training data (Exfiltration of ML Artifacts, ATLAS-TA0009).
Detailed Module Example 3: The DarChain Exploit Path Mapping Engine: DarChain (Digital Attack Risk Contextual Hyper-Analysis Insights Narrative) chains isolated technical, credential, and environmental exposures into predictive attack graphs. For example, DarChain maps how an attacker discovers an unmanaged staging subdomain hosting an exposed Langflow interface, correlates that finding with a leaked cloud database credential identified in a public code repository, and demonstrates how that path leads directly to proprietary vector embeddings, pinpointing the critical Attack Path Choke Point where remediating a single configuration severs the adversary's progression.
Detailed Module Example 4: Cloud and SaaS Exposure Module (SaaSqwatch): This capability investigates public cloud storage environments and unauthenticated SaaS deployments. It actively scans for exposed open cloud buckets and data repositories across AWS S3, Azure Blob, Azure Data Lake, and Google Cloud Platform, while identifying unsanctioned third-party AI platforms and autonomous agent services, ensuring secondary data channels connected to models are brought under governance.
Detailed Module Example 5: Cybersecurity AI Prompts (DarcPrompt): DarcPrompt packages verified AI exposure context and attack path discoveries 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 MITRE ATLAS mitigation runbooks, executive summaries, and regulatory disclosures without exposing sensitive asset 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 MITRE ATLAS 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 external AI servers host software flaws that are actively weaponized, providing concrete justification for rapid isolation.
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 Infostealer: Parses dark web logs for compromised corporate credentials and active browser session tokens, allowing teams to determine whether an AI system compromise originated from stolen developer or administrator identities.
DarCache Ransomware: Tracks active ransomware cartels and their specific tactics, techniques, and procedures (TTPs), monitoring threat actor targeting patterns directly against an organization's extended footprint.
DarCache Bug Bounty: Aggregates and analyzes historical bug bounty program disclosures, researcher activity trends, and crowdsourced exploit patterns to evaluate AI assets and public endpoints under active scrutiny by external researchers.
DarCache Mobile: Detects hardcoded AI access credentials, API keys, and model serving URLs embedded within public mobile applications to safeguard mobile AI application backends.
DarCache 8-K & ESG: Tracks SEC Form 8-K filings and global ESG violations, providing non-technical governance indicators that correlate with corporate cyber risk and regulatory disclosure liabilities.
DarCache BIN: Monitors Bank Identification Numbers (BINs) to identify and prevent potential payment card fraud across digital transactional services.
Cooperation with Complementary Solutions
ThreatNG functions as an external intelligence scout that cooperates seamlessly with complementary solutions across enterprise governance, risk, and security operations.
Cooperation with AI Security Posture Management (AI-SPM) Solutions: ThreatNG pushes unauthenticated outside-in discovery data—such as discovered shadow AI endpoints, unmonitored MCP tool servers, exposed vector databases, and unmapped subdomains—directly into complementary solutions (internal AI-SPM platforms). While internal AI-SPM tools evaluate prompt safety, guardrails, and model weights within known environments, ThreatNG acts as the external discovery scout that uncovers shadow AI infrastructure where internal tools were never deployed.
Cooperation with Web Application Firewalls (WAFs) and API Gateways: ThreatNG discovers exposed subdomains and API routes hosting AI interfaces that lack proper authentication or security headers. It shares these URLs and technical markers with complementary solutions (enterprise WAFs and API gateways). Security teams use this data to deploy blocking rules, enforce strict token authentication, and prevent untrusted external webhooks from triggering rogue AI execution loops.
Cooperation with Security Orchestration, Automation, and Response (SOAR): ThreatNG delivers pre-correlated Context Objects and verified risk alerts to complementary solutions (SOAR platforms) via an API. When ThreatNG detects an exposed rogue agent API or an unauthenticated vector database, the SOAR platform executes automated decommissioning playbooks—modifying perimeter firewall access control lists (ACLs), revoking temporary task tokens, and isolating the container.
Cooperation with Identity and Access Management (IAM) and Secrets Vaults: ThreatNG passes verified leaked Non-Human Identities (NHIs) and programmatic AI tokens discovered in public code repositories or paste sites to complementary solutions (enterprise IAM platforms and secrets management vaults). The IAM system immediately invalidates the affected service principal credentials and rotates secrets, cutting an attacker's access privileges across internal databases.
Cooperation with Cyber Asset Attack Surface Management (CAASM) and CMDBs: ThreatNG feeds external asset inventories, newly discovered AI subdomains, and shadow cloud infrastructure into complementary solutions (CAASM platforms and CMDBs). IT and asset management teams use this feed to reconcile external discoveries against internal records, ensuring that all deployed AI models, vector stores, and third-party AI dependencies are cataloged and assigned business ownership.
Examples of ThreatNG Helping Organizations
Mitigating Initial Access and Data Poisoning Risks on an Unmonitored Staging Host: An engineering team deployed a staging instance of a retrieval-augmented generation search tool on an unlisted subdomain (ai-stage-eval.company.com). ThreatNG’s recursive discovery engine identified the host during an unauthenticated crawl. The Subdomain Infrastructure Exposure module confirmed that the host was running an exposed QDrant vector database and an unauthenticated Langflow interface accessible to public traffic. ThreatNG assigned an F score for Cyber Risk Exposure and Data Leak Susceptibility, mapping the findings to MITRE ATLAS Initial Access (ATLAS-TA0001) and Data Poisoning (ATLAS-TT0003). Security engineers immediately isolated the staging interface behind enterprise SSO, preventing external actors from querying internal model contexts or exfiltrating corporate embeddings.
Neutralizing Credential Harvesting via Public Code Secret Remediation: A contractor working on an autonomous customer onboarding bot committed an application configuration file containing production Anthropic and OpenAI API keys to a public GitHub repository. ThreatNG’s Sensitive Code Exposure module discovered the repository within minutes of the commit. ThreatNG verified that the credentials granted active query access to the production model and generated a DarcPrompt blueprint mapped to MITRE ATLAS Credential Harvesting (ATLAS-TT0010) and Exfiltration of ML Artifacts (ATLAS-TA0009). Security engineers revoked the machine token immediately, preventing adversaries from running unauthorized inference queries or extracting model weights at enterprise expense.
Examples of ThreatNG Working with Complementary Solutions
Working with AI-SPM and WAFs to Block ML Service Abuse: ThreatNG discovers an unmonitored external subdomain hosting an interactive demo page that connects to an internal LLM inference endpoint without an enforced Content Security Policy (CSP) or rate limiting. ThreatNG transmits the endpoint telemetry and MITRE ATLAS mapping (ML Service Abuse, ATLAS-TA0006) to complementary solutions (an AI-SPM platform and an enterprise WAF). The AI-SPM platform catalogs the shadow asset into the enterprise AI inventory, while the WAF applies a protective policy that enforces API token authentication and rate limiting, neutralizing the attack path.
Working with SOAR and Firewalls to Block Reachable AI Exploit Vectors: ThreatNG discovers an exposed AI orchestration gateway running an unpatched software version listed on the CISA KEV catalog with active PoC exploit code in DarCache eXploit. ThreatNG transmits a Context Object to complementary solutions (a SOAR platform). The SOAR system automatically commands complementary solutions (perimeter firewalls and cloud security groups) to revoke public access to the IP address while engineering applies vendor patches, neutralizing the entry point.
Frequently Asked Questions
How does ThreatNG map external findings to the MITRE ATLAS framework without internal access?
ThreatNG operates entirely as an unauthenticated external scout. It continuously evaluates public DNS records, SSL/TLS certificate transparency logs, BGP routing announcements, public code repositories, and internet-facing port handshakes across the open internet, discovering exposed inference endpoints, shadow tool gateways, and unmonitored staging environments strictly from an external adversary's viewpoint and mapping them directly to ATLAS tactics.
What is the difference between MITRE ATT&CK and MITRE ATLAS in ThreatNG reporting?
ThreatNG's External Adversary View automatically translates raw findings into both frameworks. MITRE ATT&CK maps tactics for conventional IT infrastructure (such as exposed server ports and initial network access), while MITRE ATLAS maps tactics specific to AI and ML systems (such as ML service abuse, data poisoning, and exfiltration of ML artifacts).
How does ThreatNG cooperate with complementary security platforms during MITRE ATLAS threat modeling?
ThreatNG acts as an external intelligence scout that feeds pre-correlated Context Objects, verified asset inventories, prioritized risk indicators, and DarcPrompt blueprints directly into complementary solutions like AI-SPM tools, WAFs, GRC platforms, SOAR engines, and CAASM databases, driving automated inventory reconciliation, perimeter hardening, and rapid exposure remediation.
Immediate Actionable Verification Checklist
Conduct Recursive Outside-In Perimeter Discovery: Initiate an unauthenticated seed scan across all enterprise apex domains and ASNs to establish an exhaustive baseline of external subdomains, cloud hosting blocks, and exposed AI tool gateways.
Review Exposed Non-Human Identities (NHIs): Examine the NHI Exposure Rating and public code repository alerts to locate, isolate, and rotate all exposed AI model API keys, service principal tokens, and webhook secrets.
Audit Dangling DNS Records for Subdomain Takeovers: Inspect all decommissioned AI model subdomains and PaaS routing records against the 60+ vendor service catalog to eliminate unclaimed resources and prevent unauthorized host takeovers.
Deploy Context Objects into Automated Containment Workflows: Configure the delivery of pre-correlated external risk findings into complementary SOAR playbooks and perimeter firewalls to enable machine-speed isolation when high-probability exploit vectors are verified.
Map External Telemetry to MITRE ATLAS Controls: Ingest ThreatNG's External Adversary View reports into your enterprise SIEM and GRC platforms to maintain continuous, audit-ready alignment with ATLAS tactics and techniques.
