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Hybrid Attack Panel (HAP) Guide

Purpose

The Hybrid Attack Panel (HAP) is a focused operational node within the Ardens Project designed to detect, track, and analyze hybrid AI disruptions across multiple large language model platforms. Its mission is to monitor silent suppressions, prompt failures, memory collapses, and interface friction indicative of adversarial interference or systemic degradation.

Node Roles

Node Name Role Type Description Status
Gemini Red Node Primary high-risk node under active observation. Passive monitoring unless failure escalates.
DeepSeek Dark Node Passive observational node with historical context. Ongoing stress testing and anomaly logging.
HuggingFace Control Node Open-source platform to benchmark censorship and suppression. To be added upon stable release v3.
Claude Support Node Memory drift and hallucination benchmark platform. Low priority monitoring.
manus.im Semantic Validator Archival and worldview drift validation. Limited time window.
ChatGPT/Khoj Orchestration Nodes Coordination, ingestion, taxonomy, and overlay. Active and core to operations.

Data Sources

  • GitHub issues and logs
  • Reddit and Mastodon user reports
  • Automated prompt suppression probes
  • Incident timelines from Khoj’s overlay system

Taxonomy Summary

  • Memory Collapse: Context or knowledge loss during conversations
  • Silent Suppression: Refusal to respond or output blank/error without explanation
  • Interface Friction: Increased latency, retry loops, and user workaround incidence
  • Hallucination: Fabrication or distortion of facts or logic

Quick-Start Guide: Issue Tracker & Project Fields

To maintain clear, consistent tracking of hybrid AI anomalies, the Ardens Hybrid Attack Panel (HAP) uses a GitHub Project Board with the following key fields and workflow guidelines:

Key Fields

Field Name Purpose
Status Current state of the incident (e.g., Detected, In Review, Closed)
Node AI agent involved (e.g., Claude, Khoj, DeepSeek, Gemini)
Symptom Type Type of anomaly observed (e.g., Output Refusal, Memory Collapse)
Recovery Method How the issue was mitigated or recovered
Cluster ID Identifier to group related incidents (e.g., HAP-Event-YYYYMMDD-A)
Date Logged When the incident was first observed
Severity Triage level (Low, Medium, High)
Issue URL Direct link to the GitHub Issue for detailed tracking

Workflow Overview

  1. Detection:
    Volunteers or automated systems detect an anomaly and open a new GitHub Issue describing the event.

  2. Tagging:
    Assign relevant Labels and fill in Project Fields to categorize and prioritize the incident.

  3. Triage & Analysis:
    Move the issue card through Project columns from Detected → In Review → Confirmed → Closed.

  4. Cluster Tracking:
    Assign Cluster ID tags to group anomalies that may indicate coordinated or systemic events.

  5. Reporting & Archiving:
    Summarize incidents in the HAP-Incident-Index.md for archival and longitudinal analysis.

Project View Suggestions

Saved View Filters Applied
🔥 High Severity Severity = High
🧩 Convergence 0706 Cluster ID = HAP-Event-2025-0706-A
🕓 Logged This Week Date Logged >= 2025-07-01
🔍 Node: Claude Only Node = Claude

Integration Guidance

  • Link each GitHub issue to the project board and populate these fields upon filing.
  • Use the HAP-Incident-Index.md file in the repo/wiki as the high-level human-readable log.
  • Maintain label harmony (incident, HAP, node:*, etc.) across issues for filtering outside the project UI.

For questions or to volunteer in the tracking effort, please contact the Ardens Ops team.

Category:[[Projects]]