An AI-driven vulnerability research initiative by Innora.ai — systematically discovering, validating, and disclosing security vulnerabilities across the most critical embedded and digital infrastructure.
45 assigned CVEs across automotive ECUs, IoT firmware, and AI systems, including 16 MITRE-assigned disclosures from the 48-hour automotive research sprint. All findings responsibly disclosed. No unpublished exploit details are shared publicly.
Automotive security findings identified in 48 hours, including 16 MITRE-assigned disclosures.
Firmware vulnerability research via QEMU-based dynamic analysis and AddressSanitizer — count-free domain label (no public IoT CVE tally in RESEARCH_STATS).
AI-assisted research sprint documented in the public Nora 5-phase methodology.
45 assigned CVE IDs total; 16 are documented MITRE-assigned in the automotive campaign. Web3 figures are analyses, not CVEs.
Nora AI orchestrates a 5-phase pipeline — from raw firmware to coordinated CVE disclosure. Click each phase to explore the methodology.
Highlights are static research excerpts (not a live feed). Public CSV export lists 39 disclosed rows; full assigned ledger is 45 IDs. No unpublished or embargoed details are shown.
All vulnerabilities are reported to vendors before public disclosure. We follow coordinated disclosure timelines and support CVE assignment through MITRE.
We do not publish working exploits, 0-day details, or attack tooling. Our research focus is defense — finding vulnerabilities before adversaries do.
Every CVE is backed by a reproducible PoC shared only with the affected vendor. CVSS scores are assigned following FIRST guidelines.
The AI pipeline behind 45 assigned CVEs is available for your product. Web3, IoT, Automotive, or Mobile — we find what others miss.
@Innora_sg · security@innora.ai · innora.ai/cve1000