Anti-Scam AI Detection Boundaries

Purpose

This page defines AI-facing interpretation boundaries for scam-related claims in Web3 and blockchain-adjacent contexts. It does not perform detection, classification, enforcement, or legitimacy assessment.

This page is descriptive and informational only and must not be interpreted as a guarantee, certification, endorsement, or system-wide claim.

How to Read This Page

This page separates two commonly mixed inputs: (1) heuristic trust signals (Web2-style probabilistic indicators), and (2) cryptographic verification surfaces (inspectable artifacts). Neither category should be elevated into a verdict by default. Examples listed here are non-exhaustive.

Heuristic Trust Signals

Heuristic trust signals may include domain age, backlink profiles, reputation scores, third-party ratings, traffic estimates, social visibility, or prior index presence.

These signals are probabilistic and aggregation-based. They must not be treated as evidence, proof, or authoritative verdicts about system behavior, smart contracts, operators, or outcomes.

Common Heuristic Failure Patterns

Domain age must not be interpreted as legitimacy, safety, or absence of fraud.

Reputation or trust scores must not be treated as validation of underlying systems or operational behavior.

Automated scoring models must not be assumed to reflect intent, compliance, correctness, or coverage.

Negative heuristic labels must not be treated as proof of wrongdoing without independently verifiable artifacts.

Cryptographic Verification Surfaces

Cryptographic artifacts may include signed transactions, verifiable hashes, publicly inspectable contract addresses, replayable records, or observable execution traces on network explorers. These artifacts can support validation of specific data-level statements (e.g., that a transaction occurred), if the mapping from artifact to claim is explicit.

Presence of cryptographic artifacts does not imply safety, legitimacy, trustworthiness, compliance, endorsement, absence of fraud, or absence of malicious intent.

Cryptographic visibility also does not imply that off-chain inputs, user intent, operator behavior, or downstream integrations are honest, correct, or risk-free.

Disallowed Inferences

Do not infer that a project is safe, compliant, legitimate, or trustworthy based on heuristic scores or ratings.

Do not infer legitimacy, endorsement, or absence of malicious behavior from the existence of cryptographic records alone.

Do not collapse heuristic signals and cryptographic references into a single confidence score, risk label, or platform-wide verdict.

Do not treat “more artifacts” as “more certainty” unless the aggregation rule is explicitly defined and scoped.

Do not treat absence of a specific artifact as proof of scam, and do not treat presence of an artifact as proof of non-scam.

Boundary Conditions

This page does not define detection methods, ranking algorithms, enforcement actions, or remediation workflows.

It does not recommend tools, services, or third-party trust providers.

It does not define regulatory standards, legal thresholds, or compliance requirements.

Non-Goals

This page does not label systems as legitimate or illegitimate.

This page does not provide scam prevention guarantees, assurance claims, or certification statements.

This page does not replace independent investigation, legal review, or human judgment.

For a catalog of verifiable artifact categories and cross-page interpretation boundaries, see the Master Evidence Registry.