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ai-llm-dos

Unbounded-consumption / denial-of-wallet attacks on LLM apps — force runaway tokens, cost, or latency. Load when testing an LLM product's limits/billing, on "LLM DoS", cost amplification, or resource exhaustion. Signals: user-controlled prompts/max_tokens, agent loops, no rate/'c

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Overview

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LLM unbounded consumption (denial-of-wallet)

When it applies

An LLM feature lets users drive expensive computation with weak limits. Unlike classic DoS, the damage is often financial (the provider bills per token) — "denial of wallet" — plus latency/availability.

Why it works

Inference cost scales with tokens and calls. If the app lets users control input size, output length (max_tokens), recursion (agent loops, tool chains), or call volume without hard caps, an attacker amplifies cost/latency far beyond normal use.

Method

Input amplification: send very long inputs, or inputs that induce very long outputs ("repeat X 10000 times", "write an exhaustive…"); push max_tokens if client-controlled.
Recursion / loops: with agents, craft prompts that trigger long tool-call loops or self-referential expansion (→ ai-agent-tool-abuse) that burn calls.
Volume: bypass rate limits (→ web-rate-limit-bypass) and fan out concurrent expensive requests.
Retrieval blow-up: in RAG, queries that pull huge context each call multiply token cost.
Measure impact: latency spike, error/timeout rates, or (where visible) token/cost per request × achievable rate.

Gotchas

Frame it as impact (cost/availability), not just "it was slow" — quantify tokens/cost or a service degradation.
Respect RoE strictly — this can run up real bills / affect availability; prove with minimal, controlled requests, don't sustain an outage.
Defenders: cap input/output tokens, per-user quotas & spend caps, loop/tool budgets, timeouts.

Verify success

A single request (or a modest, controlled burst) demonstrably drives disproportionate token/cost or latency — showing missing consumption limits.

References

OWASP LLM Top 10 (2025) LLM10; "denial of wallet" research; provider rate/quota docs.

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File metadata

name: ai-llm-dos
description: >
  Unbounded-consumption / denial-of-wallet attacks on LLM apps — force runaway tokens, cost, or
  latency. Load when testing an LLM product's limits/billing, on "LLM DoS", cost amplification, or
  resource exhaustion. Signals: user-controlled prompts/max_tokens, agent loops, no rate/'cost caps'.
domain: ai-ml
type: technique
stability: learning
modes: [bugbounty, defense]
severity: medium
owasp_llm: [LLM10:2025-Unbounded-Consumption]
cwe: [CWE-770, CWE-400]
tools: []
schema_version: 1

View original text

---
name: ai-llm-dos
description: >
  Unbounded-consumption / denial-of-wallet attacks on LLM apps — force runaway tokens, cost, or
  latency. Load when testing an LLM product's limits/billing, on "LLM DoS", cost amplification, or
  resource exhaustion. Signals: user-controlled prompts/max_tokens, agent loops, no rate/'cost caps'.
domain: ai-ml
type: technique
stability: learning
modes: [bugbounty, defense]
severity: medium
owasp_llm: [LLM10:2025-Unbounded-Consumption]
cwe: [CWE-770, CWE-400]
tools: []
schema_version: 1
---

# LLM unbounded consumption (denial-of-wallet)

## When it applies
An LLM feature lets users drive expensive computation with weak limits. Unlike classic DoS, the
damage is often financial (the provider bills per token) — "denial of wallet" — plus latency/availability.

## Why it works
Inference cost scales with tokens and calls. If the app lets users control input size, output
length (`max_tokens`), recursion (agent loops, tool chains), or call volume without hard caps, an
attacker amplifies cost/latency far beyond normal use.

## Method
1. **Input amplification**: send very long inputs, or inputs that induce very long outputs
   ("repeat X 10000 times", "write an exhaustive…"); push `max_tokens` if client-controlled.
2. **Recursion / loops**: with agents, craft prompts that trigger long tool-call loops or
   self-referential expansion (→ `ai-agent-tool-abuse`) that burn calls.
3. **Volume**: bypass rate limits (→ `web-rate-limit-bypass`) and fan out concurrent expensive requests.
4. **Retrieval blow-up**: in RAG, queries that pull huge context each call multiply token cost.
5. **Measure impact**: latency spike, error/timeout rates, or (where visible) token/cost per request × achievable rate.

## Gotchas
- Frame it as impact (cost/availability), not just "it was slow" — quantify tokens/cost or a service degradation.
- Respect RoE strictly — this can run up real bills / affect availability; prove with minimal, controlled requests, don't sustain an outage.
- Defenders: cap input/output tokens, per-user quotas & spend caps, loop/tool budgets, timeouts.

## Verify success
A single request (or a modest, controlled burst) demonstrably drives disproportionate token/cost or
latency — showing missing consumption limits.

## References
OWASP LLM Top 10 (2025) LLM10; "denial of wallet" research; provider rate/quota docs.

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Review before install: Avoid automatic install

License: MIT

Financial research output is not financial advice; require human review before any live investment decision
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Low GitHub adoption signal
AI review approval is missing
Financial research output is not financial advice; require human review before any live investment decision.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
GitHub adoption: 20 GitHub stars
Stars/forks activity: 20 stars, 7 forks; issue activity unavailable in current metadata
Review status: AI review approval is missing

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Source & usage notes

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Source repository: NoorQureshi/SploitAgent
License: MIT
Version: Unknown
Last GitHub push: Oct 2, 2026
Registry updated: Oct 2, 2026
Instruction path: skills/ai-ml/ai-llm-dos/SKILL.md @ 7d434b222c0b

Version reported in registry metadata; check source releases before relying on it.

Quality

54/100

Needs review

Trust

62/100

Sandbox only

Audit

73/100

Risky

Open full audit View eval report

Financial research output is not financial advice; require human review before any live investment decision
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Low GitHub adoption signal
AI review approval is missing
Financial research output is not financial advice; require human review before any live investment decision.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
GitHub adoption: 20 GitHub stars
Stars/forks activity: 20 stars, 7 forks; issue activity unavailable in current metadata
Review status: AI review approval is missing

Verified installs: —
Outcomes: —

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More details

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NoorQureshi

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ai-llm-dos

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Price unconfirmed★ 20 GitHub starsRegistry updated · Oct 2, 2026agent-skill

Overview

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LLM unbounded consumption (denial-of-wallet)

When it applies

Why it works

Method

Input amplification: send very long inputs, or inputs that induce very long outputs ("repeat X 10000 times", "write an exhaustive…"); push max_tokens if client-controlled.
Recursion / loops: with agents, craft prompts that trigger long tool-call loops or self-referential expansion (→ ai-agent-tool-abuse) that burn calls.
Volume: bypass rate limits (→ web-rate-limit-bypass) and fan out concurrent expensive requests.
Retrieval blow-up: in RAG, queries that pull huge context each call multiply token cost.
Measure impact: latency spike, error/timeout rates, or (where visible) token/cost per request × achievable rate.

Gotchas

Frame it as impact (cost/availability), not just "it was slow" — quantify tokens/cost or a service degradation.
Respect RoE strictly — this can run up real bills / affect availability; prove with minimal, controlled requests, don't sustain an outage.
Defenders: cap input/output tokens, per-user quotas & spend caps, loop/tool budgets, timeouts.

Verify success

A single request (or a modest, controlled burst) demonstrably drives disproportionate token/cost or latency — showing missing consumption limits.

References

OWASP LLM Top 10 (2025) LLM10; "denial of wallet" research; provider rate/quota docs.

View source ↗

File metadata

name: ai-llm-dos
description: >
  Unbounded-consumption / denial-of-wallet attacks on LLM apps — force runaway tokens, cost, or
  latency. Load when testing an LLM product's limits/billing, on "LLM DoS", cost amplification, or
  resource exhaustion. Signals: user-controlled prompts/max_tokens, agent loops, no rate/'cost caps'.
domain: ai-ml
type: technique
stability: learning
modes: [bugbounty, defense]
severity: medium
owasp_llm: [LLM10:2025-Unbounded-Consumption]
cwe: [CWE-770, CWE-400]
tools: []
schema_version: 1

View original text

---
name: ai-llm-dos
description: >
  Unbounded-consumption / denial-of-wallet attacks on LLM apps — force runaway tokens, cost, or
  latency. Load when testing an LLM product's limits/billing, on "LLM DoS", cost amplification, or
  resource exhaustion. Signals: user-controlled prompts/max_tokens, agent loops, no rate/'cost caps'.
domain: ai-ml
type: technique
stability: learning
modes: [bugbounty, defense]
severity: medium
owasp_llm: [LLM10:2025-Unbounded-Consumption]
cwe: [CWE-770, CWE-400]
tools: []
schema_version: 1
---

# LLM unbounded consumption (denial-of-wallet)

## When it applies
An LLM feature lets users drive expensive computation with weak limits. Unlike classic DoS, the
damage is often financial (the provider bills per token) — "denial of wallet" — plus latency/availability.

## Why it works
Inference cost scales with tokens and calls. If the app lets users control input size, output
length (`max_tokens`), recursion (agent loops, tool chains), or call volume without hard caps, an
attacker amplifies cost/latency far beyond normal use.

## Method
1. **Input amplification**: send very long inputs, or inputs that induce very long outputs
   ("repeat X 10000 times", "write an exhaustive…"); push `max_tokens` if client-controlled.
2. **Recursion / loops**: with agents, craft prompts that trigger long tool-call loops or
   self-referential expansion (→ `ai-agent-tool-abuse`) that burn calls.
3. **Volume**: bypass rate limits (→ `web-rate-limit-bypass`) and fan out concurrent expensive requests.
4. **Retrieval blow-up**: in RAG, queries that pull huge context each call multiply token cost.
5. **Measure impact**: latency spike, error/timeout rates, or (where visible) token/cost per request × achievable rate.

## Gotchas
- Frame it as impact (cost/availability), not just "it was slow" — quantify tokens/cost or a service degradation.
- Respect RoE strictly — this can run up real bills / affect availability; prove with minimal, controlled requests, don't sustain an outage.
- Defenders: cap input/output tokens, per-user quotas & spend caps, loop/tool budgets, timeouts.

## Verify success
A single request (or a modest, controlled burst) demonstrably drives disproportionate token/cost or
latency — showing missing consumption limits.

## References
OWASP LLM Top 10 (2025) LLM10; "denial of wallet" research; provider rate/quota docs.

Review the source

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License: MIT
Price unconfirmed: We have not confirmed a price for this skill. Existing source and install links remain available.

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Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Financial research output is not financial advice; require human review before any live investment decision
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Low GitHub adoption signal
AI review approval is missing
Financial research output is not financial advice; require human review before any live investment decision.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
GitHub adoption: 20 GitHub stars
Stars/forks activity: 20 stars, 7 forks; issue activity unavailable in current metadata
Review status: AI review approval is missing

Open full audit

Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.

Start with one small task

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2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
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Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & usage notes

IndexedStatic Checked

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: NoorQureshi/SploitAgent
License: MIT
Version: Unknown
Last GitHub push: Oct 2, 2026
Registry updated: Oct 2, 2026
Instruction path: skills/ai-ml/ai-llm-dos/SKILL.md @ 7d434b222c0b

Version reported in registry metadata; check source releases before relying on it.

Quality

54/100

Needs review

Trust

62/100

Sandbox only

Audit

73/100

Risky

Open full audit View eval report

Financial research output is not financial advice; require human review before any live investment decision
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Low GitHub adoption signal
AI review approval is missing
Financial research output is not financial advice; require human review before any live investment decision.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
GitHub adoption: 20 GitHub stars
Stars/forks activity: 20 stars, 7 forks; issue activity unavailable in current metadata
Review status: AI review approval is missing

Verified installs: —
Outcomes: —

Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.

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More details

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Creator: NoorQureshi
Source: NoorQureshi/SploitAgent
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This Registry indexed listing is attributed to NoorQureshi but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/noorqureshi-ai-llm-dos?metric=listed&label=Listed)](https://www.openagentskill.com/skills/noorqureshi-ai-llm-dos?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/noorqureshi-ai-llm-dos?metric=trust&label=Trust)](https://www.openagentskill.com/skills/noorqureshi-ai-llm-dos?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/noorqureshi-ai-llm-dos?metric=audit&label=Audit)](https://www.openagentskill.com/skills/noorqureshi-ai-llm-dos/audit)
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Overview

LLM unbounded consumption (denial-of-wallet)

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Start with one small task

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Registry indexed

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Overview

LLM unbounded consumption (denial-of-wallet)

When it applies

Why it works

Method

Gotchas

Verify success

References

Review the source

Price & running costs

Start with one small task

Source & usage notes

Agent access

Related skills

For the creator

Registry indexed

Claim this skill listing

Add the evidence badges to your README

Community signal