FLARE-AI: Flaw Reporting for AI

2026-06-30

AI Summary

This work addresses the fragmented ecosystem of AI system defect reporting, which hinders effective submission, standardization, and collaboration. To overcome these challenges, the authors propose the first unified reporting framework specifically designed for AI-related defects. Drawing on an analysis of 12 existing systems and feedback from 49 domain experts, they design and open-source an interoperable reporting system that integrates conditional logic forms, early-stage defect categorization, machine-readable formats, and multi-platform distribution interfaces. The system enables one-click generation and cross-platform sharing of standardized reports. Empirical evaluation demonstrates that this approach significantly lowers the barrier to reporting, enhances the consistency and actionability of submitted information, and effectively facilitates collaborative defect resolution among diverse stakeholders.

Abstract

Flaw reporting for deployed AI systems is fundamental to identifying system failures and improving AI safety. Yet the AI reporting ecosystem is fragmented: researchers who identify flaws often do not know what or where to report, and groups who receive reports rarely share them with other relevant stakeholders. As a result, good-faith reporters duplicate effort by submitting many different forms, and recipients lack standardized, triage-ready information. We audit 12 reporting systems published by AI developers, cybersecurity groups, and AI flaw aggregators, identifying five recurring design challenges spanning discoverability, scope, information collection, coordination, and guidance for strict-liability cases. Building on this analysis and feedback from 49 experts across 32 organizations representing developers, security researchers, and ecosystem coordinators, we introduce FLARE-AI, an open-source AI flaw reporting system designed for interoperability with existing systems. FLARE-AI streamlines flaw report creation by collecting triage-relevant information through conditional logic and early classification, then enables optional dissemination of standardized, machine-readable reports to multiple developers, coordinators, and incident registries from a single submission. By lowering barriers to reporting AI flaws and improving interoperability across stakeholders, FLARE-AI helps break down silos and accelerate remediation across the AI ecosystem.

Problem

• AI flaw reporting
• reporting ecosystem fragmentation
• standardized reporting
• interoperability
• triage-ready information

Innovation

• AI flaw reporting
• interoperability
• standardized reporting
• conditional logic
• machine-readable reports