FLARE-AI: Flaw Reporting for AI

• URL: http://arxiv.org/abs/2606.31567v1
• Date: Tue, 30 Jun 2026 12:27:09 GMT
• Title: FLARE-AI: Flaw Reporting for AI
• Abstract summary: Flaw reporting for deployed AI systems is fundamental to identifying system failures and improving AI safety.<n>Yet the AI reporting ecosystem is fragmented: researchers who identify flaws often do not know what to report, and groups who receive reports rarely share them with other relevant stakeholders.<n>We audit 12 reporting systems published by AI developers, cybersecurity groups, and AI flaw aggregators.<n>We introduce FLARE-AI, an open-source AI flaw reporting system designed for interoperability with existing systems.
• Score: 51.64618567247884
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• Federated Agentic AI for Wireless Networks: Fundamentals, Approaches, and Applications [60.721304295812445]
  Federated learning (FL) has the potential to improve the overall loop of agentic AI.<n>We first summarize fundamentals of agentic AI and mainstream FL types. Then, we illustrate how each FL type can strengthen a specific component of agentic AI's loop.<n>We conduct a case study on using FRL to improve the performance of agentic AI's action decision in low-altitude wireless networks.
  arXiv  Detail & Related papers  (2026-03-02T11:26:56Z)
• Data Annotation Quality Problems in AI-Enabled Perception System Development [3.716862357836751]
  Data annotation is essential but highly error-prone in the development of AI-enabled perception systems.<n>We develop a taxonomy of 18 recurring annotation error types across three data-quality dimensions.<n>This study contributes to SE4AI by offering a shared vocabulary, diagnostic toolset, and actionable guidance for building trustworthy AI-enabled perception systems.
  arXiv  Detail & Related papers  (2025-11-20T14:30:51Z)
• Automating AI Failure Tracking: Semantic Association of Reports in AI Incident Database [7.946359845249688]
  We propose a retrieval-based framework that automates the association of new reports with existing AI Incidents.<n>Our analysis shows that combining titles and descriptions yields substantial improvements in ranking accuracy.<n>Our approach provides a scalable and efficient solution for supporting the maintenance of the AIID.
  arXiv  Detail & Related papers  (2025-07-31T15:48:12Z)
• In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI [93.33036653316591]
  We call for three interventions to advance system safety.<n>First, we propose using standardized AI flaw reports and rules of engagement for researchers.<n>Second, we propose GPAI system providers adopt broadly-scoped flaw disclosure programs.<n>Third, we advocate for the development of improved infrastructure to coordinate distribution of flaw reports.
  arXiv  Detail & Related papers  (2025-03-21T05:09:46Z)
• AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons [62.374792825813394]
  This paper introduces AILuminate v1.0, the first comprehensive industry-standard benchmark for assessing AI-product risk and reliability.<n>The benchmark evaluates an AI system's resistance to prompts designed to elicit dangerous, illegal, or undesirable behavior in 12 hazard categories.
  arXiv  Detail & Related papers  (2025-02-19T05:58:52Z)
• Position: Mind the Gap-the Growing Disconnect Between Established Vulnerability Disclosure and AI Security [56.219994752894294]
  We argue that adapting existing processes for AI security reporting is doomed to fail due to fundamental shortcomings for the distinctive characteristics of AI systems.<n>Based on our proposal to address these shortcomings, we discuss an approach to AI security reporting and how the new AI paradigm, AI agents, will further reinforce the need for specialized AI security incident reporting advancements.
  arXiv  Detail & Related papers  (2024-12-19T13:50:26Z)
• HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs) [0.09208007322096533]
  We present HuntGPT, a specialized intrusion detection dashboard applying a Random Forest classifier. The paper delves into the system's architecture, components, and technical accuracy, assessed through Certified Information Security Manager (CISM) Practice Exams. The results demonstrate that conversational agents, supported by LLM and integrated with XAI, provide robust, explainable, and actionable AI solutions in intrusion detection.
  arXiv  Detail & Related papers  (2023-09-27T20:58:13Z)
• Discovering and Validating AI Errors With Crowdsourced Failure Reports [10.4818618376202]
  We introduce crowdsourced failure reports, end-user descriptions of how or why a model failed, and show how developers can use them to detect AI errors. We also design and implement Deblinder, a visual analytics system for synthesizing failure reports. In semi-structured interviews and think-aloud studies with 10 AI practitioners, we explore the affordances of the Deblinder system and the applicability of failure reports in real-world settings.
  arXiv  Detail & Related papers  (2021-09-23T23:26:59Z)
• Bias in Multimodal AI: Testbed for Fair Automatic Recruitment [73.85525896663371]
  We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data. We train automatic recruitment algorithms using a set of multimodal synthetic profiles consciously scored with gender and racial biases. Our methodology and results show how to generate fairer AI-based tools in general, and in particular fairer automated recruitment systems.
  arXiv  Detail & Related papers  (2020-04-15T15:58:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.