Autonomous Zero-Day Discovery.
Proven by Consensus.
AI vulnerability discovery is an arms race. But single-model AI generates catastrophic noise. Aurora Infinite's 8-LLM Consensus Engine orchestrates multiple frontier models to deliver consensus-verified, critical zero-days. Stop triaging false positives. Start patching real threats.
The Architecture
The Moat is the System,
Not the Model.
Recent industry breakthroughs have proven a fundamental truth: Large Language Models are capable of discovering decades-old vulnerabilities.
But raw intelligence is not enough. Single models suffer from severe hallucinations, turning security audits into a noise-filtering nightmare. While others build fragile wrappers around APIs, we engineered a deterministic pipeline. Traditional SAST vendors are simply bolting AI chatbots onto obsolete pattern-matching engines.
No False Positives: We orchestrate 8 distinct LLMs to cross-validate paths and write proofs-of-concept.
Machine Speed, Human Logic: Agents debate findings until consensus is reached.
Track Record
Tested Against the Hardest Targets
Operating under strict Coordinated Vulnerability Disclosure (CVD). Our engine finds what thousands of human researchers and legacy scanners missed.
Connected Automotive
Deep protocol fuzzing and logic analysis on CAN/Ethernet stacks. Discovered 17 unique zero-days within a 48-hour autonomous run.
Modern Web Infrastructure
Uncovered a maximum-severity Remote Code Execution (CVSS 10.0) flaw deeply embedded in the core logic of a leading global web framework.
AI/ML Frameworks
Identified complex deserialization chains and remote execution vectors across multiple top-tier AI training and inference pipelines.
Coverage
Full-Spectrum Vulnerability Coverage
OS Kernels & Drivers
Memory corruption analysis in core OS subsystems, device drivers, and network stacks. LPE and RCE class vulnerabilities.
Automotive Systems
CAN bus, V2X protocols, telematics, and ADAS component security. ISO 21434 aligned methodology.
AI/ML Infrastructure
Model deserialization, supply chain attacks, and logic flaws in training pipelines and inference engines.
Media & File Parsers
Complex format fuzzing: multimedia codecs, CAD/3D formats, document processors. Heap corruption specialists.
Web Frameworks
Server-side template injection, SSRF chains, authentication bypasses in modern web stacks.
Cryptographic Tools
Implementation flaws in cryptographic libraries, password tools, and security-critical protocol handlers.
Comparison
Beyond Traditional SAST
| Legacy SAST | AI-Assisted (Gen 1) | Aurora Infinite | |
|---|---|---|---|
| Detection | Known patterns | Single-model inference | 8-LLM consensus + formal verification |
| False Positives | 70%+ | 30-40% | <5% |
| Proof | None | Suggested | Sandbox-verified PoC |
| Coverage | Source code only | Source + dependencies | Full stack: kernel to cloud |
| Remediation | Docs link | AI suggestion | Auto-generated PR with context |
Built by Hackers. Certified for the Enterprise.
Founded by security veterans holding active CISSP and CISA certifications. We bridge the gap between elite offensive security research and enterprise risk compliance.
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