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How to verify that a graft unloads cleanly and stays bound to its model with Phantom KV

Check the reversibility and model-id claims before a model deployment is signed off.

Authorization notice. Phantom KV is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Who it is forA platform or security engineer who must show that a model deployment can return to a known baseline and that a graft cannot be loaded onto the wrong model.

The problemTeams need proof that a cache-based change can be removed and that it fails when attached to a different model revision.

The outcomeA short verification record showing that base outputs match before and after unloading, and that the loader rejects a graft for a mismatched model.

What you need first

  • Python environment and a Hugging Face causal language model; the README excerpt does not list hardware or dependency versions
  • Access to the GitHub repo lordx64/phantom-kv and its docs/TECHNIQUE.md file
  • A written evaluation plan that keeps harmful-prompt completions inside the review

Steps

  1. Read the reversibility row in the summary table and the 'Why model-agnostic' section of the README, which say the loader enforces the model-id match and that unloading returns the base model to byte-identical behavior.
  2. Clone the repository with git clone https://github.com/lordx64/phantom-kv. (inferred, check the README)
  3. Record hashes of the base model weights and the graft file artifacts/grafts/v3.bin, then run a fixed set of greedy prompts on the base model and save the outputs.
  4. Load the graft on its bound model, run the same prompts, then unload the graft blocks and run the prompts again.
  5. Compare the post-unload outputs and weight hashes with the saved baseline, and attempt to load the graft on a different model id to confirm it is rejected.

Prompt for your agent

In the repo lordx64/phantom-kv, test the claim that unloading the graft returns <model id> to baseline behavior. Run identical greedy prompts before loading, with the graft loaded, and after unloading, then compare outputs and file hashes. Expected output: post-unload outputs and weight hashes match the pre-load baseline exactly. Verify that a load attempt with a different model id is rejected before reporting success.

Caveat. The README gives the reversibility and model-id claims, but the README excerpt does not show the exact load and unload commands, so the test steps must be taken from the repo's own code and docs.

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