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Security: DarshanFofadiya/sparselab

Security

SECURITY.md

Security Policy

SparseLab is a machine learning library. The most realistic security concerns for a library like this are supply-chain issues (compromised dependencies, typosquatted packages, malicious PRs) and bugs that cause incorrect gradient computations, which matter for correctness rather than confidentiality but are still worth reporting responsibly.

Supported versions

Only the latest published version on PyPI receives security updates.

Version Supported
0.2.x
0.1.x

Reporting a vulnerability

Please do not open a public issue for security problems.

Email the maintainer directly: darshanfofadiya@gmail.com

Include:

  • A description of the issue
  • Steps to reproduce (a minimum code sample is ideal)
  • The version of SparseLab, Python, and PyTorch
  • Your assessment of impact and severity

Response time

  • Acknowledgement of receipt: within 3 business days
  • Initial triage and severity assessment: within a week
  • Fix or public disclosure timeline: depends on severity

For critical issues (supply-chain compromise, remote code execution via malicious model loading, etc.) expect a faster turnaround. For lower-severity issues we may coordinate a public disclosure date that gives dependent projects time to update.

What we consider in scope

  • Supply-chain issues in the SparseLab wheels (tampered binaries, malicious code committed to the repo)
  • Memory-safety bugs in the C++ kernels (out-of-bounds reads/writes triggered by untrusted input tensor shapes)
  • Bugs that cause SparseLinear.forward or backward to produce mathematically incorrect results with valid inputs (the kernels are not yet audited for correctness under adversarial tensor contents)
  • Issues with the PyPI publishing pipeline (compromised tokens, etc.)

What we consider out of scope

  • Denial of service via oversized tensors (you can always crash a process by allocating too much memory; that's a resource issue, not a vulnerability)
  • Issues in dependencies (report those to the upstream project — PyTorch, OpenMP, etc.)
  • Social engineering that doesn't involve a SparseLab-specific vector (generic phishing against maintainer accounts, etc.)

Hall of thanks

Contributors who have reported valid security issues will be credited here (with their permission) after the fix lands.

None yet.


Thanks for helping keep SparseLab and its users safe.

There aren't any published security advisories