AI Attack Surface Management
AI Attack Surface Management (AI ASM) is a proactive, continuous, and systematic cybersecurity discipline focused on identifying, evaluating, prioritizing, and mitigating all potential points of vulnerability (the attack surface) that an attacker could use to compromise an organization's Artificial Intelligence (AI) and Machine Learning (ML) systems.
It is an extension of traditional Attack Surface Management (ASM), specifically tailored to address the unique and complex risks introduced by the AI/ML lifecycle, which spans data, models, infrastructure, and applications.
Components of the AI Attack Surface
The AI attack surface is significantly different from traditional IT due to the inclusion of model-specific components and the data pipeline. Key areas of exposure include:
1. Data and Model Assets
This is the most unique part of the AI attack surface, where vulnerabilities are introduced via the information powering the AI system.
Training Data: The datasets used to teach the model.
Vulnerability: Susceptible to data poisoning (introducing corrupted data to influence model behavior) or data leakage/privacy attacks (if the model memorizes and leaks sensitive information from the training set).
Model Artifacts (The Model Itself): The trained mathematical model, including its architecture and weights.
Vulnerability: Vulnerable to model extraction/stealing (an attacker queries the model to reverse-engineer a functional copy, stealing intellectual property) or model inversion (reconstructing sensitive training data from the model's outputs).
Prompts and Inputs (for Generative AI/LLMs): The text or data provided by users to interact with models like Large Language Models (LLMs).
Vulnerability: Susceptible to prompt injection (crafting malicious inputs to override safety mechanisms or make the model perform unintended actions, like leaking internal instructions).
2. Infrastructure and Platform
This covers the underlying technology stack used to develop, train, and deploy AI.
ML Pipelines: The automated workflows for data preprocessing, model training, deployment, and monitoring (e.g., using platforms like MLflow or cloud AI services).
Vulnerability: Prone to misconfigurations, overly permissive access controls, and supply chain attacks targeting third-party components or libraries used in the pipeline.
APIs and Endpoints: The interfaces that expose the AI model to end-users or other applications.
Vulnerability: Standard web application and API vulnerabilities (like broken authentication or injection flaws), which can also become a vector for model-specific attacks like prompt injection.
Shadow AI: Untracked or unsanctioned use of AI services or tools by various internal teams without security oversight.
Vulnerability: Creates a blind spot for the security team, leading to unmanaged exposure and potential data leaks.
Core Functions of AI Attack Surface Management
AI ASM adopts a continuous lifecycle approach, often automated with AI-driven tools, to reduce overall risk:
Continuous Asset Discovery & Inventory: Automatically mapping and maintaining an up-to-date inventory of all assets related to AI systems—from training data storage buckets and cloud infrastructure to model APIs and shadow AI instances.
AI-Specific Risk Evaluation & Prioritization: Assessing each component for its potential vulnerabilities, with a focus on adversarial machine learning threats (like data poisoning, evasion, and prompt injection). Risks are prioritized based on their exploitability and the potential business impact (e.g., reputational harm, data leakage, safety risks).
Adversarial Testing and Monitoring: Proactively testing the AI models themselves against known and emerging attack vectors (adversarial AI). This involves creating malicious inputs to see how the model reacts and implementing real-time monitoring of model inputs/outputs for anomalous or abusive behavior.
Remediation and Reduction: Taking action to minimize the attack surface. This includes hardening the ML infrastructure, implementing robust input sanitization and validation, fixing misconfigurations in cloud access, retiring unused data sets or model versions, and enforcing secure model deployment practices.
Importance in Cybersecurity
AI ASM is critical because a successful AI attack can lead to outcomes far beyond a typical data breach:
Financial and IP Theft: Stealing proprietary AI models or algorithms (model extraction).
Safety and Integrity Failure: Manipulating an AI model (e.g., in an autonomous vehicle or a medical diagnostic system) to cause physical harm or incorrect decisions (evasion attacks).
Data Breach and Compliance: Leaking sensitive training data (PII, trade secrets) or violating ethical/regulatory frameworks like the NIST AI RMF.
Reputation Damage: Causing a generative AI system to produce biased, hateful, or harmful content due to successful prompt injection attacks.
ThreatNG, an all-in-one external attack surface management (EASM), digital risk protection (DRP), and security ratings solution, is uniquely positioned to help with AI Attack Surface Management by providing continuous, outside-in visibility into the digital risks of an organization's AI/ML components and the broader infrastructure supporting them.
It approaches AI ASM not by looking inside the protected network, but by adopting the unauthenticated External Adversary View to identify and map vulnerabilities an attacker would target.
External Discovery and the AI Attack Surface
ThreatNG begins by performing purely external unauthenticated discovery, meaning it finds assets from the internet without requiring credentials. This is vital for discovering the often-overlooked external footprint of AI systems:
Shadow IT Discovery: ThreatNG identifies unauthorized or unknown external assets that could be hosting AI services or data pipelines, such as an exposed Cloud and SaaS Exposure instance or an undocumented API endpoint a developer spun up to test a machine learning model.
Code Repository Exposure: A critical component of AI development is code. ThreatNG’s Code Repository Exposure module discovers public code repositories (like on GitHub) and investigates their contents for sensitive data. This is essential in AI/ML, as an exposed repository might leak model code, training data pointers, or Access Credentials (such as an AWS Access Key ID or Google Cloud Platform OAuth token) that an attacker could use to pivot into the AI infrastructure.
Mobile App Exposure: It evaluates an organization's mobile apps in marketplaces, scanning them for Access Credentials (like Stripe API Key or Discord BOT Token) and Platform Specific Identifiers (like an Amazon AWS S3 Bucket), which could be connected to the back-end AI services.
External Assessment and AI Risks
ThreatNG performs various external assessments that directly assess AI-related attack vectors, quantifying the risk through security ratings (A through F):
Cyber Risk Exposure: This score considers factors like exposed sensitive ports, certificates, and known vulnerabilities. Critically, it incorporates Code Secret Exposure, which finds code repositories and their exposure level, investigating their contents for sensitive data. For an AI system, this could highlight a data processing script or model configuration file that was accidentally committed publicly, revealing a path to compromise.
Data Leak Susceptibility: This is derived from external intelligence, including Cloud and SaaS Exposure and Dark Web Presence. If the AI training data is stored in an open AWS, Microsoft Azure, or Google Cloud Platform bucket, ThreatNG flags these Open Exposed Cloud Buckets as a high-risk data leak susceptibility.
Mobile App Exposure (Assessment): Beyond discovery, it assesses the content of mobile apps for exposed credentials, which, if found, could grant an adversary unauthorized access to AI model APIs or data pipelines.
Breach & Ransomware Susceptibility: This score factors in known vulnerabilities (in the infrastructure supporting the AI) and Dark Web Presence of compromised credentials and ransomware events. A high score here indicates that the underlying technology is weak, making the AI system it runs on an easy target for a full-scale compromise.
Investigation Modules and AI Technology Identification
ThreatNG’s Investigation Modules provide the granular detail necessary to track and remediate AI-specific risks, particularly through Domain Intelligence.
Detailed Investigation Examples
DNS Intelligence and AI/ML Vendors: The DNS Intelligence capabilities include Vendor and Technology Identification. ThreatNG can identify external assets running services from specific Artificial Intelligence & Machine Learning (AI/ML) providers and platforms. For example, it can determine if an organization is using AI Model & Platform Providers like OpenAI or Anthropic, or AI Development & MLOps tools such as LangChain or Pinecone. Discovering a public-facing API linked to a Hugging Face model that the security team was unaware of is a direct, actionable AI attack surface discovery.
Subdomain Intelligence: This module identifies sensitive content like APIs and Development Environments. For an AI system, this might flag a subdomain like dev-model.company.com that hosts an unsecured staging environment for a high-value proprietary model, exposing it to attack.
Search Engine Exploitation: The Search Engine Attack Surface facility discovers an organization’s susceptibility to exposing information like Errors, Potentially Sensitive Information, and Susceptible Files via search engines. An example is identifying an exposed error log containing stack traces or internal IP addresses of an AI service, which an attacker could use to map the internal network.
Intelligence Repositories and Prioritization
ThreatNG's DarCache (Data Reconnaissance Cache) intelligence repositories provide the context needed to prioritize remediation of AI-related vulnerabilities:
DarCache Vulnerability: This provides a proactive approach to managing external risks. For a vulnerability found in a web server hosting an AI application, security teams don't just see the CVSS severity (from DarCache NVD). They also see the EPSS score, which estimates the likelihood of exploitation in the near future, and whether it’s in the KEV (actively exploited in the wild). This allows the security team to focus on patching the one vulnerability that is both a part of their AI infrastructure and is currently being exploited by adversaries.
DarCache Ransomware: By tracking over 70 ransomware gangs, this intelligence informs the Breach & Ransomware Susceptibility score. If a ransomware gang is known to target the specific cloud platform hosting the organization’s AI infrastructure, the risk is immediately reprioritized.
DarCache Dark Web and Rupture: These track Associated Compromised Credentials and organizational mentions on the Dark Web. If a credential used for accessing the AI data pipeline is discovered here, it becomes an immediate and critical risk to the AI system's integrity.
Reporting and Continuous Monitoring
ThreatNG provides Continuous Monitoring of the external attack surface, digital risk, and security ratings of all organizations, ensuring that new AI components or misconfigurations are identified immediately. The reporting capabilities facilitate action:
Prioritized Reporting: Threats are prioritized as High, Medium, Low, and Informational, ensuring security teams focus on the most critical AI exposures first, such as an exposed cloud bucket containing PII-laden training data.
MITRE ATT&CK Mapping: Raw findings—like exposed credentials or open ports—are automatically translated into an adversary behavior narrative by correlating them with specific MITRE ATT&CK techniques. For example, a finding of Leaked Credentials is mapped to an Initial Access technique, enabling security leaders to understand how an attacker would first compromise their AI system.
Knowledgebase: Reports include the Reasoning and Recommendations, which provide context and practical advice on reducing the risk, enabling teams to take proactive measures to improve the AI security posture.
Complementary Solutions
ThreatNG's EASM capabilities provide the outside-in view, which complements inside-out and model-specific solutions:
Vulnerability Management (VM) and Security Operations (SOC) Tools: ThreatNG’s ability to discover an exposed API with an associated critical vulnerability (via DarCache Vulnerability) is only the first step. By providing an external view, it can feed prioritized EASM data to an internal VM platform (like a solution from Qualys or Tenable ) or a Security Monitoring (SIEM/XDR) tool (like Splunk or Cortex XDR ). This synergy allows the internal team to use the external threat intelligence to prioritize patching efforts on systems that are both vulnerable internally and exposed externally via an identified attack path.
AI/ML Security Platforms (Model Firewalls): When ThreatNG identifies a public-facing API exposed on a subdomain that is running an AI model, it flags the risk. This external risk data can be integrated with a complementary AI security platform that acts as a "model firewall." The EASM data gives context to the firewall, enabling it to better look for and block the specific Adversarial AI Threats (like prompt injection) that ThreatNG’s external reconnaissance suggests an attacker is likely to attempt. For example, if ThreatNG identifies the use of an OpenAI or Cohere platform, the model firewall can be tuned to protect that specific vendor's interface better.
Digital Risk Protection (DRP) and Threat Intelligence Platforms: The Domain Name Permutations feature identifies typosquatted domains (e.g., company-ai-app.com versus companyai.com). This intelligence is critical for DRP. While ThreatNG discovers the squatting domain, it can work with a complementary DRP vendor to execute takedown procedures, reducing the risk of a successful phishing or BEC attack that targets AI users or developers.
