Aethel-Core is a hybrid cybersecurity framework combining neuro-symbolic AI and logic programming in Prolog to deliver a high-performance WAF engine. It features dynamic backtracking queries over a large in-memory rule set, multi-layered obfuscation decoding, and inductive learning capabilities. The system was tested with a 40,000-wave attack simulation achieving 100% block rate with no bypasses, executing over 100 million logical inferences with minimal RAM usage and near-zero garbage collection overhead. It supports detection of diverse Layer-7 threats including volumetric DDoS, Web3-specific attacks, cloud misconfigurations, and adversarial AI poisoning. The framework exports normalized telemetry data for AI training. The production deployment is under active development as a lightweight reverse proxy to integrate with modern cloud environments without modifying upstream applications. Core public components are MIT licensed, while enterprise-grade features remain proprietary.

The framework provides advanced Layer-7 threat detection and mitigation capabilities with high accuracy and low latency, potentially improving defense against complex multi-vector web attacks. It supports real-time blocking and structured telemetry generation for AI-based threat intelligence. No vulnerabilities or exploits have been reported in the provided data, indicating the system is currently a defensive tool rather than a threat.

No vulnerabilities or exploits are reported for this framework. No remediation or patching is applicable. Users interested in deploying this technology should follow the project's development updates and integrate the WAF according to best practices once the production deployment model is finalized.