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noatmark-dsh-plugin

admin-security Updated 2026.08.25

Run the following command in DeepSeek Harness:

dsh plugin install ylwl1997/noatmark-dsh-plugin

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install ylwl1997/noatmark-dsh-plugin in DeepSeek Harness to install this plugin (source: https://github.com/ylwl1997/noatmark-dsh-plugin); once installed, your agent gains four text-hygiene tools immediately.

About this plugin

In LLM conversations or automated pipelines, pasted text and upstream model output rarely arrive clean: zero-width characters, hidden markers, stray formatting fences, and formula-injection prefixes sneak in unnoticed. noatmark-dsh-plugin packages the deterministic engines behind noatmark.com into four tools your dsh agent can call on demand. sanitize_text strips invisible characters and flags prompt-injection signals in one pass; scan_text reports every zero-width character with its code point and position, plus any detected injection patterns or hidden text; clean_format removes common LLM formatting artifacts such as extra blank lines, orphaned code fences, and trailing whitespace without touching meaning; and sanitize_csv prepends a leading quote to values starting with =, +, -, or @, following OWASP guidance to neutralize CSV formula injection. Everything runs locally with deterministic, reproducible results, so no data ever leaves your machine. The plugin is a natural fit for security teams auditing untrusted input, data engineers building CSV ingestion pipelines, and any developer who needs a trust-and-verify step between raw LLM output and downstream processing.

Use Cases

  • Sanitize user-pasted text before summarization to strip zero-width characters and flag injection signals
  • Clean LLM formatting artifacts like stray fences and blank lines before feeding output to downstream parsers
  • Escape CSV formula-injection prefixes before export to prevent malicious execution in spreadsheet apps

Best For

  • Security teams auditing untrusted external input for hidden characters or injection payloads
  • Data engineers building CSV and pipeline ingestion flows that need deterministic sanitization
  • Backend developers inserting a trust-and-verify step between raw LLM output and downstream processing