Statement
Confabulation, invented citations, prompt or tool injection, poisoned procedures or telemetry, stale signed material, automation bias, privacy leakage, evaluator contamination, sensor spoofing, and correlated model/runtime failure threaten epistemic resilience.
Evidence dimensions
- Basis
- observed
- Readiness
- early research
- Confidence
- strong
Assessment rationale
NIST's GenAI Profile and adversarial-ML taxonomy directly document confabulation, information-integrity, privacy, poisoning, evasion, and misuse risks and their mitigation limits. Digital-twin and secure-development guidance adds sensor, data, component, toolchain, lifecycle, and correlated dependency concerns. Application to multigenerational epistemic resilience remains a systems inference.
Citations and locators
- Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (opens external site in a new tab)
Risk sections on confabulation, data privacy, information integrity, human-AI configuration, value chains, and related governance, measurement, and incident actions. · direct observation - Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (opens external site in a new tab)
Taxonomy chapters for predictive- and generative-AI poisoning, evasion, privacy, misuse, lifecycle stages, attacker capabilities, and mitigation limitations. · direct observation - Secure Software Development Practices for Generative AI and Dual-Use Foundation Models: An SSDF Community Profile (opens external site in a new tab)
AI-specific secure-development additions addressing model, data, code, dependency, evaluation, release, provenance, and vulnerability-response practices. · direct observation - Security and Trust Considerations for Digital Twin Technology (opens external site in a new tab)
Sections 7 and 8 on digital-twin cybersecurity, trust, sensors, connections, data, components, operations, and accepted-quality concerns. · direct observation
Assumptions and limits
The assessment applies to this bounded statement and the cited source scopes. A source can support one relationship without validating a generation ship, and an editorial grade does not substitute for independent review or representative demonstration.
What would change this conclusion?
Representative end-to-end evaluations should separately measure each risk, severe outcomes, correlated failures, operator calibration, privacy loss, abstention, and recovery under offline conditions. Strong replicated evidence that a named risk is inapplicable to the specified architecture would narrow the enumeration; a safety prompt or average benchmark would not.
Editorial record
- Prepared by: GShips Project
- Last reviewed: 2026-07-25
- Review status: substantive editorial review
- Reviewer: GShips Project editorial synthesis
- Independent review: pending two person required
- Conflicts: Publisher intends to explore a commercial venture based on some GShips work; no entity, funding, customer, sponsor, or partner relationship with cited organizations is reported.
- High-consequence domains: ai-autonomy, cybersecurity, privacy, dual-use, epistemic-resilience