Denial of Wallet (DoW) Exposure Assessment
The Denial of Wallet (DoW) Exposure Assessment is a specialized cybersecurity process that evaluates an organization's susceptibility to a financially destructive cyberattack that targets the Large Language Model (LLM) or Artificial Intelligence (AI) API to drive up billing costs intentionally.
It is an exposure, not a vulnerability itself, that quantifies the risk that a financially motivated attack could lead to exorbitant, unauthorized consumption of cloud computing and third-party service resources.
Detailed Breakdown of the Assessment
The assessment focuses on identifying the external factors that enable an attacker to execute a massive, uncontrolled consumption attack.
The Attack Vector: The DoW attack relies on the same entry points as the Unauthenticated Model Theft Vectoror a generic Denial of Service (DoS) attack. The attacker repeatedly submits queries (prompts) to an LLM endpoint, but with the intent of forcing the provider to bill the victim organization for the resource-intensive computation.
Core Exposure Indicators (Focus of the Assessment): The assessment determines risk by checking for the absence of specific external security controls:
Lack of Rate Limiting: This is the most critical indicator. The assessment checks whether the public-facing API endpoint for the LLM application enforces strict limits on the number of queries per second, per minute, or per unique IP address. The absence of throttling allows an attacker to run a high-volume script until it is stopped, draining the budget.
Weak or Absent Authentication: The assessment checks for unsecured endpoints that do not require valid API keys or credentials. If access is unauthenticated, the attacker can use easily obtained credentials or none at all, making the attack trivial to execute at scale.
Exposed High-Cost Functions: The assessment identifies whether the public API endpoint exposes functions that are disproportionately expensive to run (e.g., tasks that require massive vector database lookups, multi-turn conversations, or complex code generation), as these are the preferred targets for a DoW attacker.
Financial Impact Quantification: Unlike most cybersecurity assessments that focus on data or system integrity, a DoW assessment quantifies explicitly the economic consequences. It may involve stress-testing an unthrottled endpoint to estimate the theoretical cost ceiling—for example, calculating the charge for one million unauthenticated API calls in a short period.
Cybersecurity Context
The Denial of Wallet Exposure Assessment is crucial in the AI/ML space because many LLM services are billed on a Pay-Per-Token or Pay-Per-Query model. A successful DoW attack can cause bills to soar from hundreds of dollars to hundreds of thousands of dollars in just hours, posing a direct, immediate financial threat to the organization's bottom line. ThreatNG is highly effective at neutralizing the Denial of Wallet (DoW) Exposure Assessment risk because it specifically addresses the external misconfigurations—lack of authentication and rate limiting—that enable attackers to launch massive, uncontrolled query attacks that drive up costs.
The solution focuses on detecting and providing actionable intelligence on the exploitable, high-volume access vector.
External Discovery
ThreatNG’s External Discovery is the essential first step in assessing DoW risk, as it identifies the exposed LLM endpoint vulnerable to the cost-escalation attack. This discovery is performed using purely external unauthenticated methods.
How it helps: The DoW attack targets the public-facing LLM API. ThreatNG uses Subdomain Intelligence to discover all associated subdomains and the Technology Stack Identification module to confirm that a service is running an AI component (e.g., AI Model & Platform Provider). This confirms the existence of the asset that could incur excessive charges.
Example of ThreatNG helping: ThreatNG discovers an unmanaged subdomain, query-ai.company.com, running a technology identified as an AI Model & Platform Provider service. This is the endpoint that an attacker would target for high-volume, cost-inflating requests, immediately justifying a Denial of Wallet Exposure Assessment on that asset.
External Assessment
ThreatNG’s assessment modules are crucial for identifying the specific configuration flaws that allow a DoW attack to escalate costs exponentially.
Highlight and Examples:
Bypassing Authentication (The Credential Leak): The Non-Human Identity (NHI) Exposure Security Rating directly addresses the leakage of credentials that could be used to sustain a DoW attack.
Example: The Sensitive Code Discovery and Exposure capability finds a leaked API Key or Bearer Token in a public GitHub repository. An attacker would use this key to bypass weak authentication, initiating millions of queries that appear to be legitimate, authenticated usage, thereby inflating cloud billing dramatically. ThreatNG's findings provide Legal-Grade Attribution that a key cost-control mechanism has been defeated.
Lack of Control (The Missing Throttle): ThreatNG's Cyber Risk Exposure rating assesses the endpoint infrastructure's security posture.
Example: Subdomain Intelligence or the Technology Stack assessment reveals that the publicly exposed API endpoint is not protected by an API Gateway or a Web Application Firewall (WAF)that typically provides rate limiting. This lack of external throttling is the primary indicator of exposure to a DoW attack, demonstrating that the model can be queried indefinitely until the budget is depleted.
Continuous Monitoring and Reporting
ThreatNG ensures sustained protection by continuously monitoring for the reappearance of exposed DoW vectors and providing clear financial risk communication.
Continuous Monitoring: Because configurations can drift, ThreatNG continuously monitors exposed endpoints and repositories. If rate limiting is temporarily disabled for testing or if a developer pushes a configuration file that removes usage caps (Configuration Drift), continuous monitoring detects the change in the external security posture immediately, minimizing the window of DoW exposure.
Reporting: ThreatNG provides Executive, Technical, and Prioritized reports. While DoW does not have a dedicated Security Rating, it is captured under Cyber Risk Exposure and Financial Exposure. A report will prioritize the finding of an unthrottled, unauthenticated API endpoint as a High risk with a Reasoning that explicitly states the potential for Denial of Wallet and extreme financial liability.
Investigation Modules
These modules provide the granular, unauthenticated evidence necessary to prove the financial threat.
Highlight and Examples:
Subdomain Intelligence (Header Analysis): This module analyzes HTTP responses to detect the presence of a WAF or API Gateway.
Example: An analyst uses this module to confirm that the public AI endpoint's responses lack security headers or a WAF fingerprint, indicating the absence of the critical throttling layer needed to prevent a DoW attack.
Online Sharing Exposure: This module tracks file-sharing and public code sites.
Example: An analyst discovers a code snippet on Pastebin that includes the exact unthrottled API call structure for the organization’s AI service. This information provides the attacker with the direct payload needed to launch the high-volume attack, validating the severity of the DoW vector.
Cooperation with Complementary Solutions
ThreatNG's external validation and prioritization of the DoW risk can trigger automated financial and security controls internally.
Cooperation with Financial Management Systems: The detection of an unthrottled public endpoint (a DoW vector) can be integrated with complementary Billing Alert and Spend Cap systems.
Example: ThreatNG flags an unthrottled API. This finding is used to automatically lower the cloud billing limit for the associated project or service, creating an emergency hard stop that would cap the financial damage if a DoW attack were launched.
Cooperation with API Security Gateways/WAFs: ThreatNG identifies exposed API endpoints that lack rate limiting.
Example: This external intelligence is routed to a complementary API Security Gateway or WAF, forcing the system to immediately implement granular, token-aware rate limiting for the specific exposed endpoint, thereby eliminating the DoW exposure.
