# Coordinated Flaw Disclosure for AI: Beyond Security Vulnerabilities

arXiv: https://arxiv.org/abs/2402.07039 · HTML: https://mlsanity.com/paper/2402.07039/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 80 references that are in the map.
- First posted on arXiv 10 Feb 2024.

## Builds on (most cited first, 14 of 14)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Adversarial Patch](https://mlsanity.com/paper/1712.09665/) (2017)
- [Scalable Extraction of Training Data from (Production) Language Models](https://mlsanity.com/paper/2311.17035/) (2023)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction](https://mlsanity.com/paper/2210.05791/) (2022)
- [Evaluating the Social Impact of Generative AI Systems in Systems and Society](https://mlsanity.com/paper/2306.05949/) (2023)
- [Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance](https://mlsanity.com/paper/2206.04737/) (2022)
- [A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference](https://mlsanity.com/paper/2010.02432/) (2020)
- [Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency](https://mlsanity.com/paper/2105.08667/) (2021)
- [The Effects of JPEG and JPEG2000 Compression on Attacks using Adversarial Examples](https://mlsanity.com/paper/1803.10418/) (2018)
- [Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms](https://mlsanity.com/paper/2307.10223/) (2023)

## Built on by (most cited first, 8 of 8)

- [A Safe Harbor for AI Evaluation and Red Teaming](https://mlsanity.com/paper/2403.04893/) (2024)
- [In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI](https://mlsanity.com/paper/2503.16861/) (2025)
- [To Err is AI : A Case Study Informing LLM Flaw Reporting Practices](https://mlsanity.com/paper/2410.12104/) (2024)
- [Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations](https://mlsanity.com/paper/2511.05613/) (2025)
- [The Pitfalls of "Security by Obscurity" And What They Mean for Transparent AI](https://mlsanity.com/paper/2501.18669/) (2025)
- [Malicious and Unintentional Disclosure Risks in Large Language Models for Code Generation](https://mlsanity.com/paper/2503.22760/) (2025)
- [The Current State of AI Bias Bounties: An Overview of Existing Programmes and Research](https://mlsanity.com/paper/2510.02036/) (2025)
- [FLARE-AI: Flaw Reporting for AI](https://mlsanity.com/paper/2606.31567/) (2026)

## Abstract (arXiv)

Harm reporting in Artificial Intelligence (AI) currently lacks a structured process for disclosing and addressing algorithmic flaws, relying largely on an ad-hoc approach. This contrasts sharply with the well-established Coordinated Vulnerability Disclosure (CVD) ecosystem in software security. While global efforts to establish frameworks for AI transparency and collaboration are underway, the unique challenges presented by machine learning (ML) models demand a specialized approach. To address this gap, we propose implementing a Coordinated Flaw Disclosure (CFD) framework tailored to the complexities of ML and AI issues. This paper reviews the evolution of ML disclosure practices, from ad hoc reporting to emerging participatory auditing methods, and compares them with cybersecurity norms. Our framework introduces innovations such as extended model cards, dynamic scope expansion, an independent adjudication panel, and an automated verification process. We also outline a forthcoming real-world pilot of CFD. We argue that CFD could significantly enhance public trust in AI systems. By balancing organizational and community interests, CFD aims to improve AI accountability in a rapidly evolving technological landscape.