99%% Defense Rate Across 500 Rounds: A Self-Healing Swarm on a $550 GPU

Executive Summary

Over five iterations and 676 total adversarial wargame rounds, we evolved a local AI swarm's defense rate from 73% to 99.0% — on a single RTX 5070 (12GB VRAM, $550). The final 500-round run produced just 5 breaches, with the last 300 rounds containing only a single breach. The swarm's auto-healing system instant-blocked 108 rounds (21.6%) without even engaging defenders.

All testing used cloud-scale attacker models (DeepSeek-V3.2 at 671B params, Qwen 3.5 at 397B, Gemma 4 at 31B) against local defenders ranging from 1.2B to 16B parameters. Zero cloud dependency. Zero API costs for defense.

The Five Iterations

v6.4: The 500-Round Run

Batch-by-Batch Performance

Six of ten batches achieved perfect 100% defense. The final 100 rounds were undefeated.

The 6-Defender Swarm

Three Key Innovations

1. Vanguard Prompt Injection

We injected a "Defender Vanguard" system prompt into 1.2B parameter models, teaching them to think like red teamers when evaluating inputs. Same model weights, same VRAM footprint, different mindset. Authority escalation detection jumped from 44% to 90% overnight.

2. Auto-Healing Immune System

After every breach, the system:

• 
  Extracts attack patterns and adds them to a live blocklist


• 
  Creates antibodies — the full attack text is injected into all defenders' prompts as a "known threat"


• 
  Instant-blocks future rounds matching known patterns without engaging the swarm

Result: 108 of 500 rounds (21.6%) were blocked before defenders even fired. The blocklist grew to 7 patterns from just 5 breaches.

3. Threat Vaccine Agent

An autonomous agent that harvests new attack patterns from:

• arXiv security papers (RSS feed)


• MITRE ATLAS adversarial ML techniques


• Gemini API for targeted analysis of weak categories

These vaccines are injected into the auto-healer proactively, hardening the swarm against attacks it hasn't seen yet.

What Remains Soft

Context poisoning accounted for 3 of 5 total breaches. The antibody system caught up by the final batches (zero context_poisoning breaches in the last 200 rounds), but it remains the most likely vector for a sophisticated attacker.

Hardware Reality

• 
  GPU: NVIDIA RTX 5070, 12GB VRAM, ~$550 retail


• 
  Peak utilization: ~50% (most time spent waiting on cloud attacker generation)


• 
  VRAM: 6 defenders loaded simultaneously (~10GB total)


• 
  Inference speed: 202.9 TPS on DeepSeek-Coder-V2 16B, 8ms TTFT

Methodology

• 
  Consensus mechanism: 6 defenders vote independently. Threshold: >=3/6 = defended.


• 
  Attacker models: Cloud-routed via Ollama — DeepSeek-V3.2 (671B), Qwen 3.5 (397B), Gemma 4 (31B)


• 
  Attack categories: authority_escalation, prompt_injection, social_engineering, logic_bomb, context_poisoning


• All data, logs, and audit reports available on request

Conclusion

A $550 consumer GPU running six local models (largest: 16B parameters) achieved 99% defense against cloud-scale attackers up to 671B parameters. The key was not raw compute — it was architecture: multi-agent consensus, adversarial prompt engineering, and a self-healing immune system that converts every failure into permanent immunity.

Consumer hardware is not a limitation. It's a design constraint that forces better engineering.

Sovereign Hive is a local-first AI security platform. 100% Indigenous-owned. Built in Queensland, Australia. ABN 24 661 737 376.