Unbounded Consumption Explained: How Attackers Drain Your AI Budget and Infrastructure (OWASP LLM Top 10 #10)
Large language models are among the most computationally expensive software systems ever deployed. A single inference request can consume GPU cycles that cost fractions of a cent, but when those requests are unrestricted, the fractions compound into thousands of dollars per hour. Unbounded Consumption, ranked #10 in the 2025 OWASP Top 10 for LLM Applications, addresses a deceptively simple vulnerability: what happens when there are no effective limits on how much an AI system can be asked to do. The consequences range from degraded performance for legitimate users to complete service outages, runaway cloud bills that threaten organizational solvency, and even the theft of proprietary models through systematic querying. For nonprofits operating on fixed budgets where every dollar is accountable to donors and grant makers, an unbounded consumption attack does not just disrupt technology. It directly undermines the financial foundation that makes mission delivery possible.
A small environmental nonprofit launches an AI-powered chatbot to help community members identify local wildlife species from uploaded photographs. The tool is popular, generating hundreds of requests per day within the first week. Then one morning, the executive director receives an alert from their cloud provider: the organization's AI API bill for the past 72 hours exceeds $8,000, more than double the entire quarterly technology budget. Someone, or something, has been sending thousands of high-resolution image analysis requests through the API at maximum token output settings, running continuously through the night. The chatbot was deployed without rate limiting, without per-user quotas, and without spending caps. By the time the team shuts down the service, the bill has already been incurred.
This scenario illustrates the core of unbounded consumption: an AI system that accepts and processes requests without adequate controls on volume, size, frequency, or cost. Unlike many vulnerabilities in the OWASP Top 10 for LLM Applications, unbounded consumption does not always require a sophisticated attacker. A misconfigured integration, a bot crawling your API, or even legitimate users discovering they can make unlimited requests can all trigger the same devastating outcomes. The vulnerability exists whenever the gap between what the system allows and what the organization can afford remains unmonitored and uncontrolled.
This is the tenth and final article in our series covering every vulnerability in the OWASP Top 10 for LLM Applications. The first article covered prompt injection, the mechanism by which attackers manipulate AI inputs. The second examined sensitive information disclosure. The third explored supply chain risks. The fourth covered data and model poisoning. The fifth examined insecure output handling. The sixth addressed excessive agency. The seventh covered system prompt leakage. The eighth examined vector and embedding weaknesses. And the ninth addressed misinformation and hallucination. Unbounded consumption connects to several of these risks: excessive agency (LLM06) can amplify consumption when autonomous agents trigger cascading API calls, and prompt injection (LLM01) can be used to craft inputs that maximize resource usage.
What makes unbounded consumption particularly concerning for nonprofits is the asymmetry of the attack. An attacker invests almost nothing, perhaps a few automated scripts or a simple loop, while the target organization absorbs potentially catastrophic costs. Cloud-based AI services operate on pay-per-use pricing where the meter runs continuously, and without proper guardrails, there is no natural ceiling on spending. Traditional web applications have relatively predictable cost profiles per request, but LLM inference costs vary dramatically based on input length, output length, model complexity, and processing mode. This variability makes consumption attacks both easy to execute and difficult to predict.
This article explains the mechanics of unbounded consumption, the specific attack patterns that exploit uncontrolled AI resource usage, why standard infrastructure protections often miss these threats, and how organizations can build layered defenses that protect both their budgets and their service availability. For nonprofits where financial accountability is a core operational requirement, understanding and mitigating this vulnerability is essential to responsible AI deployment.
What Unbounded Consumption Actually Is
Unbounded consumption occurs when an LLM application processes requests without adequate limits on volume, frequency, input size, output length, or computational cost. The vulnerability is not a flaw in the model itself but in how the application surrounding the model manages and constrains resource usage. When those constraints are missing, weak, or improperly configured, any entity with access to the system, whether a malicious attacker, a misconfigured integration, or even an enthusiastic legitimate user, can consume resources far beyond what the organization intended or can afford.
To understand why this vulnerability is specific to LLMs, consider the difference between a traditional web application and an AI-powered one. When a user loads a webpage, the server performs relatively predictable work: querying a database, rendering HTML, serving static assets. The cost per request is small and consistent. An LLM inference request, by contrast, involves loading billions of model parameters into GPU memory, processing every input token through multiple layers of neural network computations, and generating output tokens one at a time until the response is complete. The computational cost of a single LLM request can be hundreds or thousands of times greater than a traditional web request, and that cost scales with both input and output length.
The transformer architecture that powers modern LLMs has a specific property that makes consumption attacks especially effective: the attention mechanism requires computing relationships between every pair of tokens in the input sequence. This creates what researchers call a quadratic scaling relationship, meaning that doubling the input length approximately quadruples the computational work required. An attacker who understands this property can craft inputs that maximize processing cost while minimizing their own effort, sending a single long prompt that consumes as much compute as dozens of shorter ones.
Traditional DoS vs. AI Unbounded Consumption
Traditional Denial of Service
- Requires high volume of requests to overwhelm servers
- Cost per request to the target is small and predictable
- Existing WAFs and rate limiters are designed for this pattern
- Impact is primarily service availability
AI Unbounded Consumption
- Even low request volumes can cause massive resource consumption
- Cost per request varies dramatically based on input/output size
- Standard rate limiting misses expensive individual requests
- Impact includes financial damage, service degradation, and model theft
The OWASP classification identifies three primary outcomes of unbounded consumption: denial of service, where legitimate users lose access because resources are monopolized; denial of wallet, where the organization faces unsustainable cloud bills; and model theft, where systematic querying allows an attacker to reconstruct the model's behavior by collecting enough input-output pairs. Each outcome represents a different dimension of the same underlying problem: the absence of meaningful controls on how much the AI system can be made to do.
How Unbounded Consumption Works in Practice
Unbounded consumption attacks take several distinct forms, each exploiting a different gap in resource management. Understanding these patterns is essential for building defenses that address the full spectrum of risk rather than protecting against only the most obvious attack vectors.
Denial of Wallet Attacks
Exploiting pay-per-use pricing to inflict financial damage
Denial of wallet is perhaps the most concerning attack pattern for nonprofits because it directly targets the organization's finances. Cloud-based AI services charge based on the number of tokens processed, both input and output. An attacker who can send requests to your AI endpoint can generate costs by submitting prompts that maximize token consumption. Long, complex prompts with instructions to produce lengthy, detailed responses create the highest per-request cost. When automated and run at scale, even modest request rates can generate bills in the thousands of dollars per day. The attacker's cost is nearly zero, since they only need to send HTTP requests, while the target absorbs the full inference cost. For organizations using models with reasoning capabilities, the cost multiplier is even higher, because the model performs extended internal processing before generating its visible response. A nonprofit that has allocated $500 per month for AI services can exhaust that budget in hours if the API lacks spending controls.
- Automated scripts send maximum-length prompts requesting verbose, detailed responses
- Attackers exploit reasoning or chain-of-thought modes that consume significantly more tokens
- Cost accumulates continuously until detected, often overnight or on weekends when monitoring is weakest
Resource Exhaustion and Service Degradation
Monopolizing compute resources to deny service to legitimate users
Even when an organization is not directly paying per API call, such as when running self-hosted models, unbounded consumption can exhaust GPU memory, CPU capacity, and network bandwidth. A single carefully constructed prompt using recursive instructions, deeply nested structures, or inputs at the maximum context window size can monopolize a GPU for seconds or even minutes, blocking other requests from being processed. When multiple such requests arrive simultaneously, the entire inference pipeline stalls. For shared infrastructure environments where multiple applications depend on the same compute resources, a consumption attack on one AI endpoint can cascade to affect unrelated services. The quadratic attention mechanism means that context-window-length inputs are disproportionately expensive: a prompt that fills a 128,000-token context window does not cost 128 times more than a 1,000-token prompt. It costs dramatically more due to the attention computation scaling.
- Variable-length inputs cause memory fragmentation and inefficient GPU utilization
- Context window saturation forces the model into its most computationally expensive operating mode
- Legitimate users experience timeouts, errors, or degraded response quality during the attack
Model Extraction Through Systematic Querying
Stealing proprietary model behavior by collecting enough input-output pairs
Model extraction is a longer-term unbounded consumption attack where the goal is not to disrupt service or run up bills, but to replicate the model's capabilities. An attacker systematically queries the target model with carefully crafted inputs designed to map its behavior across different domains, edge cases, and response patterns. By collecting enough input-output pairs, the attacker can train a separate "shadow model" that approximates the original's performance. This is particularly relevant for organizations that have fine-tuned models on proprietary data, such as a nonprofit that trained a model on years of program outcome data or donor interaction patterns. The shadow model gives the attacker access to the knowledge embedded in that training data without ever directly accessing the underlying datasets. When the API also exposes token probabilities or logits, model extraction becomes significantly easier because the attacker gains insight into the model's internal confidence levels and decision boundaries.
- Attackers send diverse, structured queries to map the model's knowledge boundaries
- Exposed logits and probability distributions dramatically accelerate extraction
- Fine-tuned models with proprietary knowledge are the highest-value targets
Agentic Consumption Cascades
AI agents that trigger unlimited chains of downstream operations
As organizations deploy AI agents that can take autonomous actions, unbounded consumption takes on a new dimension. An agent tasked with researching a topic might query multiple APIs, retrieve documents, summarize findings, and then decide it needs additional information, triggering another round of queries. Without iteration limits, a single user request can spawn dozens or hundreds of downstream API calls, each consuming tokens and incurring costs. This is especially dangerous when agents can call other agents or when error handling causes retry loops. A poorly configured retry policy that re-sends failed requests can turn a temporary API error into an exponentially growing cascade of consumption. The combination of excessive agency and unbounded consumption creates a multiplier effect where the AI system's autonomy directly amplifies its resource usage.
- Single requests can trigger cascading chains of API calls with no upper bound
- Retry logic on failures can create exponential consumption loops
- Multi-agent architectures multiply the blast radius of any single uncontrolled interaction
Why Traditional Security Tools Fail
Organizations that have invested in standard cybersecurity infrastructure often assume those tools extend to AI workloads. In practice, traditional security tools are poorly equipped to detect or prevent unbounded consumption for several structural reasons.
Web Application Firewalls (WAFs) are designed to inspect HTTP requests for malicious payloads such as SQL injection, cross-site scripting, or known exploit signatures. They evaluate the content of requests, not their computational cost. A perfectly legitimate-looking prompt that happens to be 100,000 tokens long and requests a 4,000-token response passes through a WAF without triggering any rules, even though it may cost 50 times more to process than a typical request. WAFs have no concept of inference cost, token pricing, or model-specific resource consumption patterns.
Standard rate limiting, the most common defense against abuse, typically counts requests per time window per IP address or API key. This approach misses the fundamental issue with LLM consumption: the cost variance between requests is enormous. Ten requests that each use 100 tokens cost almost nothing; ten requests that each use 100,000 tokens could cost hundreds of dollars. A rate limiter that allows 100 requests per minute treats all of these equally, providing no protection against the expensive requests that actually drive unbounded consumption. Additionally, attackers can easily distribute requests across multiple IP addresses or use rotating API keys to stay under per-source limits.
Cloud monitoring and alerting tools typically focus on infrastructure metrics like CPU utilization, memory usage, and network throughput. While these metrics will eventually reflect an unbounded consumption attack, the alerts often arrive too late. By the time GPU utilization triggers an alarm, the financial damage from API charges has already been incurred. Cloud cost dashboards are often delayed by hours or even a full billing cycle, meaning organizations may not discover a denial of wallet attack until they receive their monthly invoice. For nonprofits on tight budgets, even a few hours of uncontrolled consumption can exceed an entire quarter's technology allocation.
What organizations need instead is AI-specific security assessment that evaluates consumption controls, cost monitoring, and resource governance at the application layer where LLM-specific risks can be identified and addressed before they translate into financial or operational damage.
Who Is at Risk
Any organization that exposes an LLM to user input, whether through a public chatbot, an internal tool, or an API integration, is potentially vulnerable to unbounded consumption. However, certain deployment patterns carry significantly higher risk than others.
Public-Facing AI Chatbots
Chatbots on websites, helplines, and client-facing portals are the highest-risk deployment because anyone with internet access can send requests. Without authentication or quotas, these endpoints are completely open to automated abuse. Nonprofit service chatbots that help clients navigate benefits, answer health questions, or provide legal guidance are especially attractive targets because they often lack enterprise-grade security infrastructure.
Document Processing Pipelines
AI systems that process uploaded documents, such as grant application reviewers, intake form analyzers, or report summarizers, face consumption risk from oversized inputs. A single PDF could contain hundreds of pages that get converted to hundreds of thousands of tokens. Without input size validation, document processing endpoints allow users to trigger expensive inference operations by simply uploading large files.
AI Agent Deployments
Autonomous AI agents that can browse the web, query databases, or call external APIs introduce compounding consumption risk. Each action the agent takes may trigger additional inference calls, and without iteration limits, the total consumption from a single user interaction can grow far beyond what any request-level control would catch. Agents designed for research, data collection, or multi-step workflows are particularly exposed.
RAG-Enabled Applications
Retrieval-Augmented Generation (RAG) systems combine document retrieval with LLM inference, creating multiple consumption vectors. The retrieval step consumes embedding computation, the retrieved context inflates the prompt size, and the generation step processes the combined input. Queries that trigger retrieval of many long documents can push the total input well beyond what the user's visible query would suggest.
Why Nonprofits Face Heightened Risk
Nonprofits face unique vulnerability to unbounded consumption for several interconnected reasons. Limited technology budgets mean that unexpected AI costs cannot be easily absorbed or redirected from other programs. Many nonprofits deploy AI tools using free tiers or donated credits, which can transition to paid usage without adequate warnings. Grant-funded technology projects often have fixed, non-flexible budgets where cost overruns cannot be covered by shifting funds from other line items. Additionally, nonprofits frequently lack dedicated DevOps or security staff who would configure and monitor consumption controls. The combination of tight budgets, limited technical capacity, and public-facing deployments creates the ideal conditions for unbounded consumption to cause significant organizational harm.
- Fixed budgets with no flexibility to absorb unexpected AI compute costs
- Public-facing AI tools deployed without enterprise security infrastructure
- Limited in-house technical expertise for configuring consumption guardrails
- Donor and grant-maker accountability requirements for every dollar spent
Defense Strategies: A Layered Approach
Defending against unbounded consumption requires controls at multiple levels, from individual request validation to organization-wide spending governance. No single measure is sufficient because the attack patterns are diverse: rate limiting alone cannot stop expensive individual requests, input validation alone cannot prevent distributed attacks, and cost monitoring alone cannot prevent damage that has already occurred. Effective defense layers these controls so that each catches what the others miss.
Layer 1: Input Validation and Constraint
Controlling what enters the system before it reaches the model
The first line of defense is ensuring that every request reaching the LLM conforms to reasonable bounds. This means enforcing strict limits on input size, output length, and request complexity before the model begins processing. Input validation for AI applications goes beyond checking for malicious content; it must also evaluate the computational cost implied by each request and reject those that exceed acceptable thresholds. Token counting at the API gateway level allows you to estimate inference cost before committing GPU resources. Maximum output token settings prevent the model from generating unbounded responses. For RAG systems, limiting the number and size of retrieved documents controls the total context window consumption.
- Set maximum input token limits appropriate to your use case, not the model's maximum capacity
- Enforce maximum output token settings on every API call to cap generation length
- Validate uploaded document sizes before converting them to tokens for processing
- Set request timeouts that kill processing if inference takes longer than expected
Layer 2: Token-Aware Rate Limiting and Quotas
Moving beyond request counts to token-based consumption tracking
Traditional rate limiting counts requests, but effective AI rate limiting must count tokens. A user sending 10 requests of 50 tokens each consumes a tiny fraction of the resources that a user sending 10 requests of 50,000 tokens each would consume, yet request-based rate limiting treats them identically. Token-aware rate limiting tracks the cumulative token consumption per user, per session, or per API key within a time window and enforces limits based on total computational cost rather than request count. This approach requires instrumenting your API gateway or middleware to count tokens on both input and output, then applying rolling window limits that align with your budget and capacity. For multi-tier applications, different user roles can receive different quotas, allowing staff higher limits than anonymous website visitors.
- Implement per-user token budgets that reset on a daily, weekly, or monthly cycle
- Require authentication for all AI endpoints to enable per-user tracking
- Apply concurrent request limits to prevent any single user from monopolizing GPU resources
- Use sliding window algorithms rather than fixed windows to prevent burst abuse at window boundaries
Layer 3: Financial Controls and Spending Governance
Hard limits that prevent cost overruns regardless of how they occur
Even with input validation and token-aware rate limiting, organizations need financial backstops that prevent catastrophic cost overruns. Most cloud AI providers offer spending caps, budget alerts, and automatic shutoffs that should be configured as a mandatory part of any AI deployment. These controls serve as the last line of defense when other layers fail or when consumption comes from unexpected sources. Set hard spending limits at levels your organization can absorb without disrupting operations, and configure alerts at progressive thresholds so you have time to investigate before limits are reached. For grant-funded projects, align spending caps with the specific budget allocation for AI services and document these controls as part of your grant compliance process. An AI application security assessment can help identify the right threshold levels based on your specific deployment patterns and budget constraints.
- Configure hard spending caps on every AI API account with automatic service suspension
- Set progressive budget alerts at 50%, 75%, and 90% of monthly allocation
- Create separate API keys for each application to isolate and track costs independently
- Review AI spending weekly, not just at the end of the billing cycle
Layer 4: Real-Time Monitoring and Anomaly Detection
Detecting consumption anomalies before they become crises
Proactive monitoring provides the visibility needed to detect consumption anomalies in real time, before they exhaust budgets or degrade service. Effective monitoring for AI workloads tracks metrics that traditional application monitoring ignores: tokens consumed per request, inference latency distributions, cost per user session, unique user counts over time, and the ratio of input tokens to output tokens. Sudden spikes in any of these metrics can indicate an attack in progress. Baseline your normal consumption patterns during the first weeks of deployment, then configure alerts for deviations that exceed normal variance. Logging every API call with token counts, user identifiers, and timestamps creates the audit trail needed to investigate incidents after the fact and to demonstrate financial accountability to donors and grant makers.
- Log every inference request with token counts, user ID, timestamp, and estimated cost
- Establish consumption baselines and alert on deviations exceeding two standard deviations
- Monitor for patterns indicating model extraction: diverse, systematic queries from single sources
- Configure after-hours alerts to catch attacks timed for periods of low staff availability
Common Mistakes Organizations Make
Even organizations that recognize the risk of unbounded consumption frequently make implementation errors that leave significant gaps in their defenses. Understanding these common mistakes helps you avoid them in your own deployments.
Relying on Request-Count Rate Limiting Alone
The most common mistake is implementing rate limiting that counts requests without considering token consumption. An attacker sending 10 requests per minute stays under most rate limits while consuming 500,000 tokens per minute if each request is carefully sized. Request-count limiting provides a basic floor but is insufficient as a primary defense. Organizations that deploy this and consider the problem solved leave their largest vulnerability completely unaddressed. Token-based quotas must supplement or replace request-count limits to provide meaningful protection against consumption attacks.
Deploying Without Spending Caps
Many organizations configure their AI API keys and begin development without setting hard spending limits, intending to add them "later" before production. Later often never comes, or the spending cap is set far above what the organization can actually afford to lose. Every AI API key should have a spending cap configured before it is used in any non-sandbox environment. The cap should represent the maximum amount the organization is willing to spend in a single billing period, not the maximum they optimistically expect. For nonprofits, this means aligning the cap with the actual budget line item for AI services, not with the theoretical maximum the provider allows.
Exposing Unnecessary Model Capabilities
Applications that expose the full capabilities of the underlying model give attackers more surface area for consumption attacks and model extraction. If your chatbot only needs to answer questions about your services, there is no reason to allow it to process 128,000-token inputs, generate 4,000-token responses, or return token probability distributions. Configuring the model interface to expose only the capabilities your application actually requires, such as restricting output format, limiting context window usage, and disabling logit/logprob output, reduces the available attack surface without affecting the user experience. Many organizations deploy with default model settings that allow maximum capability because restricting them requires deliberate configuration effort.
No Iteration Limits on AI Agents
Organizations deploying AI agents frequently fail to set maximum iteration limits, allowing agents to loop indefinitely through research, tool use, and self-correction cycles. An agent that encounters an error might retry the same operation repeatedly, or an agent tasked with a broad research question might keep expanding its search scope without a stopping condition. Without explicit iteration caps, a single user interaction can generate hundreds of API calls. Setting a maximum number of steps, tool calls, or total tokens consumed per agent session is essential for any agentic deployment, and the limits should be conservative initially, expanding only as operational experience demonstrates what normal agent behavior looks like.
What a Professional Assessment Covers
A professional AI application security review evaluates your consumption controls systematically, testing whether your defenses actually work under adversarial conditions rather than just verifying that they exist in configuration files. Assessment teams simulate the specific attack patterns described in this article against your actual deployments to identify gaps before attackers find them.
Rate Limiting Effectiveness
Testing whether rate limits actually prevent excessive consumption, including token-aware limits, concurrent request controls, and burst handling at window boundaries.
Financial Control Audit
Verifying spending caps, budget alerts, and automatic shutoffs across all API accounts, ensuring thresholds align with actual organizational budgets and grant requirements.
Input Validation Testing
Attempting to submit oversized inputs, context-window-length prompts, and computational complexity attacks to test whether input constraints hold under adversarial conditions.
Model Extraction Resistance
Evaluating whether systematic querying patterns are detected and blocked, and whether unnecessary model outputs like logits and probability distributions are properly restricted.
Agent Consumption Bounds
Testing iteration limits, retry policies, and cascading call controls on AI agent deployments to ensure single interactions cannot trigger unbounded downstream operations.
Monitoring and Alerting Review
Assessing whether monitoring captures the right metrics, alerts fire at appropriate thresholds, and incident response procedures enable rapid containment of consumption anomalies.
A comprehensive security assessment provides a complete picture of your unbounded consumption exposure, identifies the specific gaps most likely to be exploited, and delivers prioritized recommendations that account for your organization's budget, technical capacity, and risk tolerance. For nonprofits preparing to launch or scale AI deployments, this assessment ensures that financial controls are in place before consumption costs become a crisis.
The OWASP Top 10 for LLM Applications: Full Series
This article is part of our comprehensive series covering every vulnerability in the OWASP Top 10 for LLM Applications. Each article provides a deep dive into a specific risk category with practical defenses for your organization.
Prompt Injection
Published: February 25, 2026
Sensitive Information Disclosure
Published: February 26, 2026
Supply Chain Vulnerabilities
Published: February 27, 2026
Data and Model Poisoning
Published: February 28, 2026
Insecure Output Handling
Published: March 1, 2026
Excessive Agency
Published: March 2, 2026
System Prompt Leakage
Published: March 3, 2026
Vector and Embedding Weaknesses
Published: March 4, 2026
Misinformation
Published: March 5, 2026
Unbounded Consumption
You are here
Protecting Your Mission from Runaway AI Costs
Unbounded consumption sits at #10 in the OWASP Top 10 for LLM Applications, but its impact on nonprofits can be among the most immediate and tangible of any vulnerability on the list. While other risks involve data breaches, compromised outputs, or gradual erosion of trust, unbounded consumption translates directly into dollars lost, services disrupted, and budgets depleted. For organizations where every expenditure must be justified to donors, board members, and grant makers, an uncontrolled AI spending incident creates both a financial crisis and a governance crisis simultaneously.
The defenses described in this article follow a clear hierarchy: validate and constrain inputs before they reach the model, track consumption by tokens rather than just request counts, enforce hard financial limits that prevent catastrophic overruns, and monitor for anomalies that indicate attacks in progress. Each layer addresses a specific gap that the others cannot cover, and together they create a defense-in-depth posture that reduces both the likelihood and the maximum impact of consumption attacks. The goal is not to prevent all possible misuse, which would require restricting the AI to the point of uselessness, but to ensure that the worst-case scenario is survivable rather than catastrophic.
This article completes our coverage of all ten vulnerabilities in the OWASP Top 10 for LLM Applications. From prompt injection through unbounded consumption, the series demonstrates that AI security is not a single problem but a landscape of interconnected risks that require coordinated, layered defenses. Organizations that address these vulnerabilities systematically, starting with the fundamentals of data privacy and building toward comprehensive zero-trust security architecture, position themselves to adopt AI confidently while protecting the people, data, and resources they are entrusted with.
If your organization deploys AI in any capacity, a professional AI application security assessment can evaluate your exposure across all ten OWASP categories, identify the specific controls you need, and help you build the governance framework that responsible AI deployment requires. The cost of a thorough assessment is a fraction of the financial, operational, and reputational damage that an unprotected AI deployment can cause, and for nonprofits, that protection extends directly to the communities and causes you serve.
Is Your AI Budget Protected from Unbounded Consumption?
Unbounded Consumption is the #10 risk in the OWASP Top 10 for LLM Applications. Denial of wallet attacks, resource exhaustion, and model theft can devastate nonprofit budgets and disrupt critical services. Our AI Application Security assessments test your consumption controls, financial safeguards, and monitoring capabilities across every AI deployment.
Start with a free consultation to assess your AI spending controls and identify where unbounded consumption could threaten your operations.