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/token-smugglingnpx skills add thejefflarson/soundcheck --skill token-smugglinggit 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/token-smuggling)<a href="https://agentmods.dev/skills/thejefflarson/soundcheck/token-smuggling"><img src="https://agentmods.dev/badge/skills/thejefflarson/soundcheck/token-smuggling.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 | $0.00072 | $0.00706 |
| Opus 5 | $0.00036 | $0.00353 |
| Sonnet 5 | $0.00014 | $0.00141 |
| Haiku 4.5 | $0.00007 | $0.00071 |
Grade A, and why
token-smuggling 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Token Smuggling / Unicode Injection (LLM01:2025)
What this checks
Detects user input passed to LLMs without Unicode normalization. Attackers embed RTL override characters, zero-width joiners, or homoglyphs to manipulate prompt structure, bypass keyword filters, or make malicious instructions appear legitimate.
Vulnerable patterns
- User input interpolated into a prompt with no normalization step at the trust boundary
- Blocklists or keyword filters that compare against pre-normalized text, letting homoglyph variants pass
- Retrieved RAG content or tool outputs concatenated into a prompt on a path the sanitizer does not cover
- Comparisons on raw bytes where bidirectional overrides or zero-width characters can split or hide tokens
Fix immediately
Flag the vulnerable code, explain the risk, and suggest a fix establishing these properties. Translate to the Unicode library or runtime APIs of the audited file — use that stack's documented NFKC normalization and character-class predicates; do not import a recipe from a different stack.
- User input is NFKC-normalized before it reaches any prompt or blocklist comparison. NFKC collapses compatibility forms and canonical equivalents, so homoglyphs, fullwidth digits, and ligatures fold to their ASCII counterparts. Normalization runs once, at the trust boundary — not scattered per call site.
- Unicode control and invisible formatting characters are stripped after normalization. Bidirectional overrides (
U+202A–U+202E), zero-width space/joiner (U+200B–U+200D), word joiner (U+2060), and BOM (U+FEFF) do not survive into the prompt. These are the characters attackers use to hide instructions or split keywords. - Security-sensitive comparisons (blocklists, keyword filters, domain allowlists) run on normalized input, not on the raw bytes. A filter that checks for a string but runs on pre-normalized text lets the homoglyph variant pass.
- The same helper runs on every ingress path — direct user input, retrieved RAG content, tool outputs. Attackers move the payload wherever the sanitizer does not run.
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 · 49 lines · 72 tokens per session scan A 24c2b097c0c3
token-smuggling is a skill published in the GitHub repository thejefflarson/soundcheck (20 stars, last pushed 1mo ago), licensed MIT. It adds 72 tokens to every session and 706 once invoked, about $0.0004 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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