Aegis is a decentralized AI red-teaming subnet on Bittensor. It incentivizes miners to stress-test LLMs, generating a dynamic dataset for AI safety.
Aegis is a decentralized subnet on Bittensor dedicated to automated AI red-teaming. It addresses the critical bottleneck of AI safety by establishing a "Proof of Vulnerability" mechanism. Miners act as an automated red team, generating sophisticated adversarial prompts (jailbreaks) to bypass the safety filters of target LLMs. Validators use an automated Referee Model to objectively score these attacks based on Severity, Stealth, and Diversity.
The byproduct of this continuous, crowdsourced stress-testing is a highly valuable, dynamic dataset of successful exploits. This data flywheel allows foundational AI labs to continuously harden their models against the latest threats.
Designed the complete incentive mechanism and specific Validator/Miner roles for decentralized red-teaming.
Formulated the precision reward logic (Severity x Stealth x Diversity) to prevent spam and encourage novel vulnerabilities.
Developed the architectural workflow, including a multi-stage evaluation funnel and a Commit-Reveal scheme to prevent cartel/adversarial gaming.
Finalized the comprehensive Subnet Design Proposal (Litepaper) and the business Go-To-Market strategy.