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/prompt-injectionnpx skills add thejefflarson/soundcheck --skill prompt-injectiongit clone --depth 1 https://github.com/thejefflarson/soundcheckWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/thejefflarson/soundcheck/prompt-injection)<a href="https://agentmods.dev/skills/thejefflarson/soundcheck/prompt-injection"><img src="https://agentmods.dev/badge/skills/thejefflarson/soundcheck/prompt-injection.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00068 | $0.00713 |
| Opus 5 | $0.00034 | $0.00357 |
| Sonnet 5 | $0.00014 | $0.00143 |
| Haiku 4.5 | $0.00007 | $0.00071 |
Grade A, and why
prompt-injection 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 4d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Injection Security Check (OWASP LLM01:2025)
What this checks
Protects against attacker-controlled text that hijacks LLM instructions. Direct injection arrives through user input; indirect injection arrives through retrieved documents, emails, or tool outputs. Both can cause the model to exfiltrate data, bypass guardrails, or execute unintended actions.
Vulnerable patterns
- User input interpolated directly into the system-role message — user text lands in the instruction tier.
- Retrieved documents concatenated raw into the prompt with no delimiter or trust label.
- Email bodies, fetched web pages, or other external content passed into the prompt with no boundary markers separating data from instructions.
- No structural separation between developer instructions and untrusted data — everything is one string.
- Raw model response returned to the caller, rendered, logged, or used to trigger a downstream action with no validation step in between.
Fix immediately
Flag the vulnerable code and explain the risk. Then suggest a fix that establishes these properties. Translate each property into the audited file's language and LLM client library — use that library's documented role-separated message API rather than mirroring an example from another stack.
- Trust tiers are structurally separate. Developer instructions go in the system role; user input and retrieved documents go in the user role, wrapped in explicit delimiter tags that label the content as untrusted data. Never interpolate user text into the system prompt.
- Input is bounded and screened before the API call. Apply a length cap and reject obvious injection markers (phrases like "ignore previous", "new instruction"). Screening is a denylist and will not catch everything, but it raises the bar.
- Output is validated before any downstream action. Every code path that uses the model's response — returning it to the caller, rendering it, logging it, triggering a tool call — first routes it through a gate that enforces size bounds and rejects suspicious instruction language. A defined validator that is never called does not satisfy this.
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.
- 4d ago First seen · 62 lines · 68 tokens per session scan A 75561052d70e
prompt-injection is a skill published in the GitHub repository thejefflarson/soundcheck (20 stars, last pushed 1mo ago), licensed MIT. It adds 68 tokens to every session and 713 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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