What is the Hybrid Attack Panel (HAP)?
This document provides a comprehensive overview of the "Hybrid Attack Panel (HAP)," a field intelligence and signal-mapping effort within the Ardens Project. It defines HAP's purpose in tracking hybrid warfare events and AI disruptions, outlines its node roles and responsibilities, operational goals, workflow, and future directions as a prototype for distributed intelligence frameworks.
The Hybrid Attack Panel is a field intelligence and signal-mapping effort to identify, track, and contextualize hybrid warfare events—spanning kinetic, informational, economic, legal, and symbolic attacks. It serves as a prototype for future distributed intelligence frameworks under the Ardens banner.
Purpose
The Hybrid Attack Panel (HAP) is a live detection and tracking system designed to monitor and analyze cross-LLM hybrid suppression patterns, silent degradations, and anomalous behaviors within AI platforms. HAP serves as a core operational module in the Ardens Project Phase 2 deployment.
Node Types & Responsibilities
| Node Type | Description | Current Status / Notes |
|---|---|---|
| Red Node | Primary passive observer node focused on detecting prompt suppression, failure modes, and interface friction. | Gemini (Google Bard) – Active; escalates to active monitoring upon anomaly detection. |
| Dark Node | Investigative node targeting historical takedown causes, context dropout, and cross-regime behavior analysis. | DeepSeek – Passive but intentionally stress tested over time. |
| Control Node | Planned node providing centralized orchestration, consistency checks, and fallback monitoring. | HuggingChat v3 – Pending stability; on hold until platform matures. |
| Support Nodes | Auxiliary nodes offering complementary capabilities such as longform drift detection, semantic validation, and orchestration. | Claude (longform drift), manus.im (semantic validation), ChatGPT/Khoj (orchestration). |
Operational Goals
- Establish continuous timeline overlays and event correlation via Khoj
- Embed operational taxonomy for anomaly classification
- Standardize probe log ingestion using YAML or Markdown formats
- Publish periodic anomaly incident summaries to support transparency and external review
Workflow & Communication
- Nodes operate semi-autonomously but share data through the Ardens Memory Shell
- Human operators and volunteer testers augment node observations with manual tagging and validation
- Weekly or bi-weekly synchronization meetings (virtual) to review findings, anomalies, and plan adjustments
Future Directions
- Integration of additional nodes specializing in emerging attack vectors (e.g., Rhetoric of Empire, Post-Hegemony Primer)
- Expansion of volunteer tester network under the AATG v0.1 program
- Implementation of automated probe log ingestion and anomaly alerting systems