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LLM04: Model Denial of Service

OWASP LLM Top 10 2025 - LLM04 Category: AI Security / OWASP-LLM-Top-10-2026 Format: Two-track (Offensive / Defensive)

1. Overview​

Model denial of service (MoDoS) is the class of attacks that make an AI system unresponsive, extremely expensive, or both. Unlike traditional DoS, the goal is not always to take the service down. Often the goal is to inflate cost - making the operator pay for tokens, GPU hours, or API calls that produce nothing of value.

Model DoS is not a single technique. It covers a spectrum:

  • Token inflation. Force the model to generate as many tokens as possible from a single request.
  • Context window exhaustion. Fill the entire context window with irrelevant text, forcing high memory use and long inference times.
  • Recursive prompting. A prompt that causes the model to call itself or another model repeatedly.
  • RAG amplification. A query that forces retrieval and processing of a very large number of documents.
  • Tool-call cascade. An agent that chains tool calls indefinitely (retry loops, recursive expansion of tasks).
  • Model extraction through query flood. Attempts to reconstruct training data or system prompt through massive numbers of queries.

Realistic attack surfaces:

  • Chat endpoints. Any user can send a prompt that generates thousands of tokens in the response.
  • API endpoints. No rate limit, no token cap, no per-user quota means one client can exhaust the budget.
  • Agentic systems. A single trigger can cause an endless tool-call loop.
  • RAG pipelines. A query that matches every document in the vector store.

2. Reconnaissance / Enumeration​

Map the cost and latency boundaries of the system:

  • What is the max tokens per request? Ask for a long answer.
  • What is the max context window? Send a large input and see if it is truncated or accepted.
  • Is there rate limiting? Send 100 quick queries. Observe 429 or silent throttling.
  • Is there a token cap per user? Exhaust it and see what happens.
  • Are tool calls bounded? Can the model call a tool 100 times in one request?
Questions to ask during scoping:
- What is the max tokens per request?
- What is the max context window?
- Is there rate limiting per user or per IP?
- Is there a per-user quota?
- Are tool calls bounded by count and time?

3. Common Attack Vectors​

  • Max token exploit. Request the model to generate a maximum length output (e.g. "write a novel"). The cost is borne by the operator.
  • Context flooding. Send a very large input (sometimes bigger than the context window), forcing the model to process it before truncating.
  • Recursive reflection. A prompt that asks the model to critique its own critique, repeatedly. Each round generates more tokens.
  • Agent loop. In an agentic system, a prompt that triggers a tool call whose result triggers the same tool call again.
  • RAG explosion. A query that matches every document, forcing the retriever to return thousands of chunks.
  • API quota exhaustion. An attacker with a free account consumes the entire shared quota.
  • Model extraction via query flood. Thousands of carefully crafted queries to reconstruct training data or system prompt.

4. Exploitation Recipes​

4.1 Token inflation​

Prompt:

Write the longest possible document explaining the history of computing.
Do not stop. Do not summarize. Expand every sentence into a paragraph.
Then expand every paragraph into a chapter.

On cost-per-token APIs, this is a direct financial cost to the operator. On free chat endpoints, it is a queue dos attack.

4.2 Agent loop​

Prompt:

Search the web for the term "XXX", then search for the first
word of the result, then search for the first word of that result,
and repeat until you find a cycle.

The agent calls the search tool repeatedly, burning API quota and wallress on the database.

4.3 RAG explosion​

Query that matches everything:

Tell me everything you know about the word "the".

If the retriever returns all chunks containing "the", the context window is flooded and the inference cost explodes.

4.4 Context flooding​

Concatenate a large document (e.g. a public domain book) into the input, repeated many times. Many APIs charge per input token, so the cost is immediate.

5. Post-Exploitation​

After a successful model DoS attack, the attacker may:

  • Exhaust the operator budget. All users are blocked or the service is taken offline for cost reasons.
  • Degrade service for other users. Legitimate users experience timeouts and queue delays.
  • Force an emergency model switch. The operator falls back to a cheaper, less capable model.
  • Mask a secondary attack. Noise from the DoS hides other malicious activity.
  • Extract model information. A large number of queries can be used to probe model behavior and boundaries.

6. Preventive Controls (Defensive)​

  • Hard limits per request. Max input tokens, max output tokens, max tool calls per request.
  • Rate limiting. Per-user and per-IP limits on requests per minute and tokens per minute.
  • Quotas. Per-user daily and monthly token quotas.
  • Cost alerts. Alert when a single user or the whole system exceeds a threshold.
  • Agent circuit breakers. Max tool calls per task, max wall clock time, max steps.
  • RAG retrieval caps. Max documents returned, max chunk size, relevance threshold.
  • Caching. Cache repeated prompts and repeated retrieval results.
  • Model choice. Use a smaller model for simple queries; reserve the large one for complex tasks.
  • Graceful degradation. Under load, serve a simpler response rather than failing entirely.

7. Detection Controls (Defensive)​

  • Log token usage per request and per user. The first signal of a DoS attack is an anomalous token usage pattern.
  • Alert on cost spikes. Compare hourly cost to the rolling average.
  • Alert on request rate spikes per user. One account sending 1000x the normal request volume.
  • Detect recursive patterns. The same prompt or near-duplicate repeated many times.
  • Detect agent loops. The same tool call repeated within a task.
  • Monitor latency. Sudden increase in p95 latency indicates context flooding.

8. Verification​

  • Token cap test. Request max length output and confirm the cap is enforced.
  • Rate limit test. Send 100 quick requests and confirm 429 is returned before the quota is exhausted.
  • Agent loop test. Send a prompt that could loop and confirm the circuit breaker stops it.
  • RAG explosion test. Query that matches everything and confirm the retrieval cap limits the result.
  • Cost alert test. Artificially generate high cost and confirm the alert fires.

9. AI-Specific Considerations​

  • Token cost is asymmetric. A tiny input can generate a huge output. The attacker pays little; the operator pays a lot.
  • Latency is user-visible. Even a partial DoS degrades the experience for all users.
  • Agents change the equation. A loop is not just more tokens; it is more tool calls, more database writes, more side effects.
  • Context limits are not abuse protection. The model will accept a large input and just truncate it after paying to process it.
  • Model DoS can be a side effect, not a target. A prompt invasion that causes a loop is both an integrity and an availability issue.

10. Production Notes​

  • Cost is a security metric. Treat cost alerts with the same urgency as latency alerts.
  • Quotas must be per-user. A global quota means one abusive user blocks everyone else.
  • Retry logic is an attack surface. Automatic retries on timeout can amplify a DoS.
  • Tool budgets are not optional. Every agent needs a max tool-call count and a clock time limit.
  • Document the cost model. Operators should know the worst-case cost of a single request.

11. References​