dsh-secret-scrub
Run the following command in DeepSeek Harness:
dsh plugin install jkt-check/dsh-secret-scrub
Paste the following prompt into your AI chat to install this plugin:
Install the plugin in DeepSeek Harness by running dsh plugin install jkt-check/dsh-secret-scrub; the full source is available at https://github.com/jkt-check/dsh-secret-scrub .
About this plugin
In AI terminal and Agent workloads, API keys, bearer tokens, and private key blocks slip into persistent session logs or ride along in model requests all too easily—once leaked, the damage is irreversible. dsh-secret-scrub is a guard plugin built for exactly this gap: it intercepts text on its way to the log and the model, detects secret-shaped fragments against configurable rules, and irreversibly replaces them with a [REDACTED:category] placeholder before they are written or transmitted.
The plugin ships a three-tier rule set. Tier 0 (always active, non-disableable) covers a dozen-plus mainstream credential formats—AWS, GitHub, OpenAI, Anthropic, Stripe, and generic bearer/JWT tokens. Tier 1 adds environment-variable-style cloud provider keys at the balanced level. Tier 2, available only at aggressive, extends to email, phone numbers, national ID numbers, Luhn-checked credit cards, and a high-entropy fallback. Three waterfall listeners—user-message pre-step, tool-result post-execute, and code-dispatch log—guarantee that every write path is scrubbed. A pure-function scrubbing engine is also exported for use outside any Cordis runtime.
It is designed for teams operating AI terminals, headless Agent pipelines, or any Cordis-managed context where sensitive text must not appear in durable logs or outbound model calls. If you want placeholder-only logs, credential-free model requests, and a zero-dependency regex engine without building your own pipeline, dsh-secret-scrub is a drop-in, configure-and-go solution.
Use Cases
- Users paste API keys or bearer tokens into an AI terminal session that must not persist real credentials in logs
- Tool execution results in an Agent workflow carry sensitive tokens that need irreversible replacement before disk write
- Headless pipelines require audit logs to contain redaction placeholders instead of real keys or secrets
Best For
- Security and platform teams operating AI terminal or Agent products
- Development teams with compliance-driven log redaction requirements
- Engineers building headless AI pipelines who want to mitigate credential-leak risk
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