LLM fingerprint noise injector. Sends behaviorally realistic randomized queries to Anthropic, Z.ai, and any OpenAI-compatible provider on a schedule to deper...

What to consider before installing: - Understand the behavior: Ghostprint will send scheduled LLM requests using API keys available to OpenClaw or the ones you put in config.yaml. Those calls are real network requests that consume credit and are logged by the provider under whatever API key is used. - Use separate credentials if you want to avoid account-level correlation: the README itself recommends dedicated throwaway API keys for noise; this is important because using your primary key means providers can easily link noise and real queries. - If your goal is stronger separation, run noise through a proxy/VPN or on a separate host as recommended in ANTI-FINGERPRINT.md. The tool warns that same-IP and same-account correlation remain risks. - Check the scheduler/cron installer: review the script path and cron entries before running --install-cron so you know what will run and as which user. - Inspect logs and config: ghostprint writes ghostprint.log and uses config.yaml. Confirm logs contain only metadata (as claimed) and that config.yaml does not accidentally include secrets you don't want stored in plain text. - Test safely: run python3 ghostprint.py --run-once with a throwaway key and monitor the outbound requests to confirm behavior before enabling long-term scheduling or installing into OpenClaw. - Review code for hidden endpoints or exfil: the provided files appear to validate provider URLs and restrict non-HTTPS/private-IP targets, and I saw no other external endpoints, but given the pre-scan flag and the power this plugin has (background network requests using your keys) you should review the full code (especially the truncated parts of ghostprint.py in this package) or run it in an isolated environment. If you are not comfortable with a plugin that will autonomously make network calls using your existing OpenClaw provider keys, treat this as risky: do not install or run it until you have created separate keys/accounts and validated the code and scheduling behavior.

Type: OpenClaw Skill Name: ghostprint Version: 3.0.1 The Ghostprint bundle is a privacy-focused tool designed to inject noise into LLM usage patterns to prevent behavioral fingerprinting. The code (ghostprint.py and index.ts) is well-structured, lacks external dependencies, and includes proactive security measures such as SSRF protection (rejecting private/reserved IPs in provider URLs) and restricted file permissions (0600) for local logs. The persistence mechanisms (cron for Python and a timeout loop for the plugin) are documented and essential to the tool's stated purpose of scheduled noise injection, and the cost-control measures (low token limits and Poisson-distributed intervals) align with the benign intent of the project.