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SecurityPython · JavaScriptGitHub @viridis-securityOnlineOwner unconfirmed
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Adaptive proof-of-work defense for AI agents. Asymmetric thermodynamic cost: attackers dissipate, defenders verify in O(1). Reference implementation of T-IB-09 (Adversarial Dissipation Theorem) — Aristotle-verified. Apache-2.0.
Translation pending
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SHA-256 proof-of-work defense for AI agents. Constant verification work in difficulty; tunable expected classical search effort.
Apache-2.0. Maintained by Viridis Security.
The defender hashes a candidate solution once and counts leading zero bits. Modeling SHA-256 as a random oracle, a classical solver needs an expected 2^d hash queries to find a fresh solution at difficulty d. Verification cost is constant in difficulty for fixed input lengths. The implementation, regression tests, and model assumptions back these claims; tests do not establish a physical energy bound.Read the full README ↗The full README is unavailable right now. Read it at the original link. The saved excerpt is shown below.
SHA-256 proof-of-work defense for AI agents. Constant verification work in difficulty; tunable expected classical search effort.
Apache-2.0. Maintained by Viridis Security.
The defender hashes a candidate solution once and counts leading zero bits. Modeling SHA-256 as a random oracle, a classical solver needs an expected 2^d hash queries to find a fresh solution at difficulty d. Verification cost is constant in difficulty for fixed input lengths. The implementation, regression tests, and model assumptions back these claims; tests do not establish a physical energy bound.Read the full README ↗