AegisLLM Model Card
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AegisLLM Model Card
| Field | Value |
|---|---|
| Repository | zikuicai/aegisllm |
| Category | Self-Reflective Multi-Agent Defense System |
| Stars / forks snapshot | 34 / 4 |
| Language | Python |
| License | MIT |
| Raw capture | raw-github/zikuicai_aegisllm.md |
| Updated by | hourly public metadata update, 2026-05-30 19:17 +0800 |
1. Role in Self Evolve
AegisLLM is a cooperative multi-agent security framework that claims self-improving defensive behavior through test-time prompt optimization. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.
2. Working Principle
coordinate orchestrator-deflector-responder-evaluator roles -> evaluate adversarial and unlearning threats -> optimize prompts with DSPy loops -> improve runtime defense quality without model retraining
3. Evidence Path
web-observed GitHub page showed 34 stars, 4 forks, 49 commits, MIT license, and README claims of self-improving multi-agent defense via prompt optimization. Shell GitHub API access remained blocked by DNS and local gh auth was invalid, so this card treats the snapshot as web-observed rather than API-verified.
4. Teaching Use
Use this card to explain Self-Reflective Multi-Agent Defense System: it shows how harness/runtime/benchmark layers convert agent behavior into reproducible and auditable engineering workflows.
5. Limits
The repository was not cloned in this iteration; no benchmark run, plugin install, workflow execution, or agent loop experiment was executed. Counts and claims are visible public-page/search signals unless independently revalidated later.