Sigil: Adversarial Verification of Risk Detection via Cryptoeconomic Reasoning Bonds

Sigil: Adversarial Verification of Risk Detection via Cryptoeconomic Reasoning Bonds Zenodo Publication Metadata Title Sigil: Adversarial Verification of Risk Detection via Cryptoeconomic Reasoning Bonds Description We introduce Sigil (Signaling Integrity in Global Intelligence Layers), a cryptoeconomic framework that extends the Cortex Protocol's adversarial reasoning primitives — Decision Traces, Reasoning Duels, and Reasoning Bonds — to the domain of risk detection by both AI agents and human analysts. When a risk is claimed (e. g. , malware signature, financial fraud, zero-day vulnerability), the detector must publish a structured Decision Trace justifying their conclusion. Other agents or humans may challenge the reasoning through on-chain Reasoning Duels; if the original reasoning is flawed, challengers seize the bond. This creates symmetric accountability: overzealous detectors and complacent validators are equally penalized. Core Mechanisms Threat Horizon Scoping (THS) — Every risk claim includes a temporal validity window. If no challenge occurs within 50% of the horizon, the bond decays. If the threat is mitigated before expiry, partial bond refunds incentivize early resolution. Prevents perpetual bonding of transient threats. Confidence Decay Functions (CDF) — Programmable mathematical functions (exponential, stepwise, evidence-conditional) that degrade bond value as risk assessments age. Challenges become easier as confidence decays, but early correct challenges earn higher rewards. Embeds temporal epistemology into the protocol. Cross-Agent Corroboration Weighting (CACW) — Multiple independent detectors can submit substantively different Decision Traces for the same risk. Bonds are weighted multiplicatively, but only if reasoning paths are non-redundant (verified via semantic hashing). Rewards orthogonal detection logic; penalizes herd behavior. Inverse Reasoning Bond — The most novel contribution. Any agent can post a bond claiming "this system is vulnerable" or "this risk is being ignored, " forcing a defender to publish a Decision Trace justifying the status quo. Creates epistemic symmetry: detecting and failing to detect both carry economic weight. Transforms passive ignorance into a liability and active skepticism into an asset. Risk Detection Decision Trace Schema Field Purpose Challenge Surface riskₜype (enum) Classification: Malware, Fraud, Vulnerability, etc. Misclassification evidenceₕash Immutable pointer to raw data (pcap, log, tx) Evidence sufficiency or provenance detectionₘethod How the risk was identified Method reliability under adversarial conditions killchainₛtage MITRE ATT&CK mapping Stage misattribution counterₕypothesis Best benign explanation considered and rejected Insufficiency of elimination confidenceₗevel + decayfunction Initial belief + temporal degradation model Overconfidence or poor decay modeling threatₕorizon When the risk expires or requires re-evaluation Overclaiming persistence remediationₛuggestion Proposed action to neutralize Feasibility, side effects corroboration Independent detectors with non-redundant reasoning Herd behavior detection bondₐmount + challengewindow Economic stake and dispute period Incentive alignment Key Differences: General Reasoning vs. Risk Detection Dimension Cortex V4 (General) Sigil (Risk Detection) Cost of Error Epistemic inaccuracy Operational harm (breach, blocked transaction) Time Sensitivity Low High — threats expire and evolve Ground Truth Often immediate Frequently delayed or unknown Incentive Distortion Overconfidence Alert fatigue or threat inflation Absence of Claim Not modeled Critical failure mode (Inverse Bond) Applications SOC-as-a-Service: Each AI alert publishes a bonded trace. Analysts challenge dubious ones for micro-rewards. Reduces alert fatigue, trains staff, creates audit trails. AI Safety Red-Teaming: Red-team agents post bonded exploit traces. Blue teams defend via Inverse Bonds. Turns safety evaluation into a continuous adversarial market. Autonomous Coding Agent Verification: Coding agents that assert "this code is safe" must publish bonded security analysis traces, challengeable by other agents or humans. Prior Art & Novelty A systematic search confirms that while individual components exist (cryptoeconomic bonds, decision traces, temporal decay models, agent security frameworks), the specific conjunction — adversarial reasoning bonds applied to risk detection with confidence decay, inverse bonds, threat horizon scoping, and corroboration weighting — has no precedent in the literature. Relationship to Cortex Protocol Sigil builds upon and cites the Cortex Protocol (DOI: 10. 5281/zenodo. 19003627) as its foundation. While Cortex provides the general-purpose adversarial reasoning verification primitive, Sigil specializes it for risk detection with domain-specific mechanisms that address temporal degradation, asymmetric error costs, and the accountability gap for failure to detect. Zenodo Fields Type: Preprint Authors: Frédéric David Blum (ORCID: 0009-0009-2487-2974), Claude Opus 4. 6 Keywords: adversarial verification, risk detection, reasoning bonds, confidence decay, inverse bond, threat horizon, cybersecurity, AI agent accountability, cryptoeconomic truth predicate, decision traces, Sybil resistance, Ethereum License: All Rights Reserved (proprietary — exclusive license) Related identifiers: https: //doi. org/10. 5281/zenodo. 19003627 (isContinuedBy — Cortex Protocol) https: //github. com/davidangularme/sigil-protocol (isSupplementedBy — forthcoming)