Only the latest release receives security fixes.

1. GitHub Security Advisories (preferred): Report here
2. Email: protoscience@anulum.li
3. Subject: [SECURITY] Director-AI — <brief description>
4. Do not open a public GitHub issue for security vulnerabilities.

We will acknowledge receipt within 48 hours and aim to provide a fix within 7 days for critical issues.

Security concerns for Director-AI:

• Prompt injection: adversarial inputs designed to bypass coherence oversight
• Metric evasion: inputs crafted to produce high coherence scores for hallucinated outputs (false negatives)
• Knowledge base poisoning: malicious entries that corrupt factual scoring
• Model deserialization: unsafe loading of NLI model weights
• Dependency supply chain: compromised upstream packages

• Dual-entropy scoring: NLI contradiction detection + RAG fact-checking
• Streaming halt: token-level coherence monitoring with three halt mechanisms
• Safety kernel: hardware-level output interlock with emergency stop
• Prompt injection hardening: InputSanitizer detects instruction overrides, role-play injections, delimiter tricks, output manipulation, and data exfiltration attempts; scrubs null bytes, control chars, and homoglyphs
• YAML policy engine: Policy blocks forbidden phrases, enforces length limits, requires citations, and evaluates custom regex rules
• Multi-tenant isolation: TenantRouter guarantees per-tenant KB separation with thread-safe access
• Structured audit trail: AuditLogger writes JSONL with SHA-256 query hashes (never plaintext queries) for compliance and forensic review
• Minimal dependencies: core requires only numpy and requests
• No pickle.load of untrusted data in any module
• CI security audit: pip-audit runs on every push

Director-AI is licensed under GNU AGPL v3. Key obligations:

1. Source disclosure: if you modify Director-AI and deploy it as a network service, you must make your modified source available to users of that service under the same license.
2. Commercial alternative: a commercial license is available for organisations that cannot comply with AGPL requirements. Contact protoscience@anulum.li.
3. Dependency compatibility: all runtime dependencies are permissively licensed (MIT/Apache-2.0/BSD). The AGPL obligation applies to Director-AI code, not to your application code that calls it through the public API.

• No third-party security audit.
• Heuristic scorer (without NLI model) is deterministic and trivially bypassed.