Skip to content

Security: hyperpolymath/Axiom.jl

SECURITY.md

Security Policy

Supported Versions

VersionSupported
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

ActionTimeline
AcknowledgmentWithin 48 hours
Initial assessmentWithin 7 days
Status updateEvery 14 days
Fix releaseDepends on severity

Severity Levels

SeverityDescriptionTarget Fix Time
CriticalRemote code execution, data breach24-72 hours
HighPrivilege escalation, significant data exposure7 days
MediumLimited data exposure, DoS30 days
LowMinor issues, hardening90 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@ensureall(0.≤ output .≤1) "Output must be valid probabilities"# Formal verification@proveBoundedOutputs(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

@ensurevalid_input(x) "Input validation failed"@ensureno_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