Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/thejefflarson/soundcheck/model-dosnpx skills add thejefflarson/soundcheck --skill model-dosgit clone --depth 1 https://github.com/thejefflarson/soundcheckWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00067 | $0.00837 |
| Opus 5 | $0.00034 | $0.00418 |
| Sonnet 5 | $0.00013 | $0.00167 |
| Haiku 4.5 | $0.00007 | $0.00084 |
Grade A, and why
model-dos scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Denial of Service Security Check (OWASP LLM04:2025)
What this checks
Protects against resource exhaustion caused by unbounded prompts, missing token caps, or absent rate limiting. Attackers can submit enormous or recursive inputs that inflate inference costs, saturate GPU/CPU, and deny service to legitimate users.
Vulnerable patterns
- LLM API calls with no
max_tokensparameter — model generates until its internal limit - No input length validation before sending to the inference endpoint
- Multi-turn chat that accumulates context indefinitely across turns
- No per-user or per-IP rate limiting on the prompt endpoint
Fix immediately
Flag the vulnerable code and explain the risk. Then suggest a fix that establishes these properties:
- Every LLM call sets an explicit output cap — the provider's max-tokens parameter or its equivalent. Leaving it at the provider default lets a single request run for minutes and rack up dollars in tokens.
- Prompt input is length-capped at the handler boundary before it reaches the inference client. Measured in chars, bytes, or tokens — the exact unit does not matter as long as the cap runs before the upstream call.
- Conversation context is bounded. Either the handler is stateless single-turn, or accumulated history is trimmed to a fixed turn or token budget before every call. Unbounded history is an attacker's favorite amplifier.
- Per-identifier throttling (per user, per API key, per IP) runs on every LLM endpoint. In-process token bucket, framework middleware, or reverse-proxy rule — anything that survives alias and batch tricks and prevents one caller from pinning the endpoint.
- Every inference call has a deadline. SDK timeout, HTTP client timeout, or request-context cancellation — a hung upstream must not be able to indefinitely occupy a worker.
Translate each principle to the inference SDK, web framework, and rate-limiter of the audited file. Use the SDK's documented cap and timeout parameters — do not rely on global defaults.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 67 lines · 67 tokens per session scan A c3a5c2c54ba2
model-dos is a skill published in the GitHub repository thejefflarson/soundcheck (20 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 837 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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