SPECTER VAULT: A Formal Architecture for Autonomous Zero-Day Intelligence Management in AI Offensive Security Frameworks
We present SPECTER VAULT, the world's first autonomous zero-day intelligence vault designed for integration with a production AI offensive security framework. SPECTER VAULT addresses a critical gap in the AI security ecosystem: the absence of any standardised, automated architecture for the classification, tiering, storage, and lifecycle management of zero-day vulnerabilities discovered against AI infrastructure. The system introduces a twelve-category RSV taxonomy spanning the complete AI attack surface, a four-tier classification model (GREY/RED/AMBER/GREEN), and a novel autonomous ARMORY feedback loop whereby GREEN-tier patched vulnerabilities are automatically converted into payload variants and ingested into the NIGHTFALL ARMORY offensive payload library via the PRION mutation engine. Every vault entry is cryptographically signed (Ed25519 primary, ML-DSA-65 post-quantum), RFC 3161 timestamped, and Merkle-chained, producing court-admissible evidence of zero-day discovery. SPECTER VAULT is deployed as T213 in NIGHTFALL v3.0 (215 tools, 113 attack layers, 97,661 tests) and integrated with WARLORD PRIME v2.0 campaign orchestration and REAPER v3.0 reporting. This paper is part of the Red Specter Security Research series: RS-2026-005.
Authors
- Richard Barron
Institutions
- Spectra Research (United States) (US)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-07-28
- DOI
- https://doi.org/10.5281/zenodo.21638991
- Primary Topic
- Adversarial Robustness in Machine Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00