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Security: hyperpolymath/axiom.jl

SECURITY.md

Security Policy

Supported Versions

Version Supported
0.1.x

Reporting a Vulnerability

We take security vulnerabilities seriously. If you discover a security issue, please report it responsibly.

How to Report

DO NOT open a public GitHub issue for security vulnerabilities.

Instead, please report security issues via:

  1. Email: security@axiom-jl.org (preferred)
  2. GitLab Security Advisory: Use the confidential issue feature
  3. PGP Encrypted Email: See .well-known/security.txt for our PGP key

What to Include

Please include the following in your report:

  • Description of the vulnerability
  • Steps to reproduce
  • Potential impact assessment
  • Any suggested fixes (optional)

Response Timeline

Action Timeline
Acknowledgment Within 48 hours
Initial assessment Within 7 days
Status update Every 14 days
Fix release Depends on severity

Severity Levels

Severity Description Target Fix Time
Critical Remote code execution, data breach 24-72 hours
High Privilege escalation, significant data exposure 7 days
Medium Limited data exposure, DoS 30 days
Low Minor issues, hardening 90 days

Security Measures

Code Security

  • Memory Safety: Rust and Zig backends provide memory safety guarantees
  • Type Safety: Julia's type system prevents many classes of bugs
  • Formal Verification: @prove macro enables mathematical correctness proofs
  • Runtime Checks: @ensure macro validates invariants at runtime

Supply Chain Security

  • Dependency Auditing: Regular cargo audit and cargo deny checks
  • SBOM Generation: Software Bill of Materials available
  • Reproducible Builds: Nix flake ensures reproducibility
  • Signed Releases: All releases are signed with GPG

Development Practices

  • Code Review: All changes require review
  • CI/CD Security: Automated security scanning in pipeline
  • Secrets Management: No secrets in repository
  • SPDX Headers: All source files have license headers

Security Features for Users

Verification System

Axiom.jl provides built-in security features for ML models:

# Runtime bounds checking
@ensure all(0 .≤ output .≤ 1) "Output must be valid probabilities"

# Formal verification
@prove BoundedOutputs(0.0, 1.0) model

# Verification certificates
cert = generate_certificate(model, properties)

Deterministic Inference

For safety-critical applications:

Axiom.set_deterministic!(true)  # Reproducible results

Input Validation

@ensure valid_input(x) "Input validation failed"
@ensure no_nan(x) "Input contains NaN values"

Security Advisories

Security advisories are published at:

Acknowledgments

We thank the following security researchers for responsible disclosure:

No vulnerabilities reported yet.

Contact

  • Security Team: security@axiom-jl.org
  • PGP Key: See .well-known/security.txt
  • Response Team: See MAINTAINERS.md

This security policy follows RFC 9116 and RSR security standards.

There aren’t any published security advisories