Skip to content

Security: hypneum-lab/nerve-wml

Security

SECURITY.md

Security policy — nerve-wml

nerve-wml is a research code base providing a substrate-agnostic Nerve Protocol reference implementation for the dreamOfkiki research program. Correctness and reproducibility of the Gate W / Gate M measurements cited in Paper 1 are our top priorities ; security is handled on a best-effort basis.

Scope

This policy applies to :

  • The code in this repository (hypneum-lab/nerve-wml)
  • The MlpWML and LifWML substrate implementations and their shared Nerve Protocol
  • The measurement scripts (scripts/run_w2_hard, scripts/merge_pilot) whose outputs are cited in the parent paper
  • The documentation in docs/

It does not apply to dependencies (NumPy, PyTorch, surrogate-gradient libraries).

Reporting a vulnerability

If you discover a security issue that could compromise :

  • the determinism of the FlowProxyTask / HardFlowProxyTask runs under fixed seeds
  • the integrity of the polymorphism-gap measurements cited in dreamOfkiki Paper 1 v0.2 §7.4
  • silent numerical errors in LifWML spike-path integration
  • silent numerical errors in MlpWML gradient flow

please report it privately via one of these channels :

  1. Email : clement@saillant.cc — subject starting with [SECURITY]
  2. GitHub Private Vulnerability Reporting : https://github.com/hypneum-lab/nerve-wml/security/advisories/new

Please include :

  • a description of the issue
  • reproduction steps (seed, commit SHA, substrate variant)
  • the measured value of the affected metric if applicable
  • suggested mitigation if available

We aim to acknowledge reports within 5 business days and publish a fix within 30 days for critical issues. Reporters will be credited unless they prefer otherwise.

Out of scope

  • General questions about the polymorphism gap on harder tasks — open a regular GitHub issue.
  • Dependency vulnerabilities — report upstream.
  • Licensing or byline questions — see LICENSE and CONTRIBUTORS.md.

Threat model

Expected threats : inadvertent reproducibility regressions and silent numerical invalidation of the measurements cited in the parent paper. Write-access abuse is outside the model ; review happens via PR discipline.

There aren't any published security advisories