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Security: calabamatex/AgentSentry

SECURITY.md

Security Policy

Reporting a Vulnerability

If you discover a security vulnerability in AgentSentry, please report it responsibly.

Do NOT open a public GitHub issue for security vulnerabilities.

How to Report

  1. GitHub Security Advisories (preferred): Use GitHub's private vulnerability reporting to submit a report directly through GitHub.

  2. Email: Send details to the repository maintainers via GitHub.

What to Include

  • Description of the vulnerability
  • Steps to reproduce
  • Potential impact
  • Suggested fix (if any)

Response Timeline

  • Acknowledgment: Within 48 hours
  • Initial assessment: Within 1 week
  • Fix timeline: Depends on severity
    • Critical: Within 72 hours
    • High: Within 1 week
    • Medium/Low: Next release cycle

Supported Versions

Version Supported
0.5.x Yes
< 0.5 No

Security Features

AgentSentry includes built-in security capabilities:

  • Secret Detection: Scans for 15 types of hardcoded credentials (API keys, tokens, connection strings, JWTs)
  • PII Scanner: Detects 15 categories of personally identifiable information in logging statements
  • Dashboard Authentication: Token-based access control for the monitoring dashboard
  • Hash-Chained Audit Log: Tamper-evident event storage with SHA-256 chain verification
  • Permission Enforcement: File-level and command-level allowlist/denylist

MCP Server Authentication

As of v0.6.0, the MCP server requires authentication by default and rejects all requests unless AGENT_SENTRY_ACCESS_KEY is set.

  • Production / network-exposed: set AGENT_SENTRY_ACCESS_KEY to a strong random value (e.g., openssl rand -hex 32).
  • Local development only: set AGENT_SENTRY_NO_AUTH=true to disable authentication. This emits a stderr warning on every startup and should never be used in production.
  • The deprecated AGENT_SENTRY_REQUIRE_AUTH variable has been removed.

Known Security Considerations

  • The Supabase provider is experimental and should not be used in production environments.
  • Vector search data is stored unencrypted in local SQLite. Use filesystem-level encryption for sensitive environments.

There aren't any published security advisories