token-smuggling
Detects user input passed to LLMs without Unicode normalization, allowing RTL overrides, zero-width joiners, and homoglyph attacks. Use when writing code that passes user-supplied strings to an LLM, constructs prompts from external data, or processes text that may contain Unicode control characters, homoglyphs, or invisible formatting characters.
Install
With your agent
Install OMGSkills skill "thejefflarson/soundcheck:.claude/skills/token-smuggling" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/thejefflarson/soundcheck /tmp/soundcheck && ln -s /tmp/soundcheck/.claude/skills/token-smuggling ~/.claude/skills/token-smuggling
From README
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.
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