Versatile AI Risk Assessment

AI threat catalogue Attacks on the Running Model and Service Production

Model Denial of Service

Attackers overload the AI model with especially expensive requests until the service slows down or fails. They exploit the fact that AI computation ties up large amounts of costly processing power.

As of: October 2026 · Catalogue version 2026.10.24.2 · 5 mitigations · 10 verified sources

Description

AI computations require a lot of specialised and expensive computing power, and that capacity is often the system's bottleneck. Attackers deliberately construct inputs that force as much useless computation as possible: so-called sponge examples that drive up processing time, very long inputs that exhaust the processing budget, or requests that make the model run in loops. A flood of individually harmless requests can likewise saturate the graphics processors. For paying users the service then becomes slow or unreachable.

Possible impact

The AI service degrades to the point of outage, while computing costs and response times spike. Legitimate users and connected business processes are blocked. For high-risk systems, availability is part of the required robustness, and an outage can halt dependent workflows.

Example

A customer-service chatbot receives thousands of deliberately nested, maximum-length requests in an automated stream. The graphics processors behind it are fully loaded, and genuine customer enquiries go unanswered for minutes.

Recommended mitigations (5)

Compare effects and implementation levels. Expand each entry for the additional classification and rationale.

  • Request rate limiting

    Technical
    Effect
    Preventive, Impact-limiting
    Implementation level
    Application, API & agents, Infrastructure
    Classification and rationale

    Complementary control typeOrganizational & process-based

    Primarily technical: Automated resource controls, budget limits, or runtime boundaries constrain overload, abuse, cost, and cascading failures; complemented by binding workflows.

  • Input size constraints

    Technical
    Effect
    Preventive, Impact-limiting
    Implementation level
    Application, API & agents, Infrastructure
    Classification and rationale

    Primarily technical: Automated resource controls, budget limits, or runtime boundaries constrain overload, abuse, cost, and cascading failures.

  • Timeout enforcement

    Technical
    Effect
    Preventive, Impact-limiting
    Implementation level
    Application, API & agents, Infrastructure
    Classification and rationale

    Primarily technical: Automated resource controls, budget limits, or runtime boundaries constrain overload, abuse, cost, and cascading failures.

  • Resource quotas per user/session

    Technical
    Effect
    Preventive, Impact-limiting
    Implementation level
    Application, API & agents, Infrastructure
    Classification and rationale

    Complementary control typeOrganizational & process-based

    Primarily technical: Automated resource controls, budget limits, or runtime boundaries constrain overload, abuse, cost, and cascading failures; complemented by binding workflows.

  • DDoS protection

    Technical
    Effect
    Preventive, Impact-limiting
    Implementation level
    Application, API & agents, Infrastructure
    Classification and rationale

    Primarily technical: Automated resource controls, budget limits, or runtime boundaries constrain overload, abuse, cost, and cascading failures.

Framework mappings

Verified locations in OWASP, NIST AI RMF, MITRE ATLAS, the EU AI Act and further frameworks. The mappings are taxonomic, not evidence of compliance.

OWASP LLM Top 10 LLM06:2026NIST AI RMF Section 2.9 · NISTAML.014MITRE ATLAS AML.T0029EU AI Act Article 26(5) · Article 55(1)(d) · Article 9(1), 9(2)(a), 9(2)(d)BSI R28BIML BIML-LLM input:5 · BIML78 system:10

Verified references (10)

Every reference states the framework, the exact location and the publishing organisation.

EU AI Act obligations related to this threat

The obligations from the obligations compass whose articles the catalogue maps to this threat. Each leads to its entry with core, action step and evidence; whether it applies to you depends on role and risk class.

  • Establish and operate a risk management system Art. 9 · applies from Annex III: 2 December 2027 · Annex I: 2 August 2028

    Statutory roles: Provider, Product manufacturer. Classes: High-risk under Annex III, High-risk as a product or safety component.

    This reference is a review prompt. A threat match alone does not establish applicability.

  • Monitor operation and suspend on risk Art. 26 Abs. 5 · applies from Annex III: 2 December 2027 · Annex I: 2 August 2028

    Statutory roles: Deployer. Classes: High-risk under Annex III, High-risk as a product or safety component.

    This reference is a review prompt. A threat match alone does not establish applicability.

  • Evaluate and mitigate systemic risks Art. 55 · applies from 2 August 2025

    Statutory roles: Provider of a general-purpose AI model. Classes: Model with systemic risk.

    This reference is a review prompt. A threat match alone does not establish applicability.

All 62 obligations by role, risk class and deadline in the obligations compass

More entries from the topic group Attacks on the Running Model and Service.

Model Theft Attackers create a working copy of a proprietary AI model, either by querying it at scale or by breaking into the infrastructure. Adversarial Inputs Attackers alter an input in a way that is minimal and imperceptible to humans, so that the AI model classifies it incorrectly. Cost Harvesting / Repurposing Attackers use a company's paid AI services without authorisation and at its expense, for example with stolen access keys.

Matching use cases

This list shows where to review this threat, rather than how high the risk is.

Typical for:

For 27 further use cases, relevance depends on the system design, for example whether it accepts queries directly through an interface or is accessible over a network. Show these use cases

Frequently asked questions

What is Model Denial of Service?
Attackers overload the AI model with especially expensive requests until the service slows down or fails. They exploit the fact that AI computation ties up large amounts of costly processing power.
Which mitigations help against Model Denial of Service?
The catalogue lists 5 mitigations, each with control type, effect and implementation level. First among them are Request rate limiting, Input size constraints and Timeout enforcement.
Which EU AI Act obligations does Model Denial of Service touch?
The catalogue maps this threat to Art. 9, Art. 26 Abs. 5 and Art. 55 of the EU AI Act. In the obligations compass this leads to: Establish and operate a risk management system, Monitor operation and suspend on risk and Evaluate and mitigate systemic risks. Whether an obligation applies depends on role, risk class and deadline.

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Cite this entry

For reports, policies or internal documents; the link leads directly to this entry.

“Model Denial of Service”. Versatile AI Risk Assessment, AI threat catalogue, as of October 2026.
https://www.versatile-ai-risk-assessment.com/en/wissensbasis/threats/model-denial-of-service/

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