"AI Slop is DDoSing Open Source": Understanding the Impact of AI-Generated Contributions on Open Source Sustainability

• URL: http://arxiv.org/abs/2607.04003v1
• Date: Sat, 04 Jul 2026 19:55:11 GMT
• Title: "AI Slop is DDoSing Open Source": Understanding the Impact of AI-Generated Contributions on Open Source Sustainability
• Abstract summary: We call it AI-DDoS: a denial-of-service effect in which plausible but low-quality AI-generated contributions overwhelm community capacity.<n>We analyze practitioner accounts from Reddit, OSS mentor mailing lists, and blogs to identify six recurring themes and derive hypotheses.<n>Our findings show that AI-DDoS is not only a contribution-volume problem but a sustainability trap.
• Score: 8.07130264725486
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open source software (OSS) communities are facing increasing pressure from Generative AI (GenAI) tools. We call it AI-DDoS: a denial-of-service effect in which plausible but low-quality AI-generated contributions overwhelm OSS community capacity. Using a phenomenon-based mixed-methods approach, we first analyze practitioner accounts from Reddit, OSS mentor mailing lists, and blogs to identify six recurring themes and derive hypotheses. We then evaluate these hypotheses using Bayesian Structural Time Series analysis across 294 repositories with over 2 million pull requests and issues. Our results show that while PR volume increased in 2025, merge rates declined, with one-time contributors experiencing an 18.18% drop in PR merge rates relative to the counterfactual. Finally, we identify 11 remediation strategies through practitioners' interviews and validate them with a survey of 229 OSS practitioners, grouping them into preservative, adaptive, and transformative orientations. Our findings show that AI-DDoS is not only a contribution-volume problem but a sustainability trap: communities often default to low-effort defensive strategies that protect short-term review capacity while making openness difficult to sustain.

Related papers

• Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion [52.722575491987904]
  We show that ICL and SFT dominate in different regimes, determined by an interplay between pretraining coverage and data signal-to-noise ratios.<n>We prove that offering both personalization methods never hurts the platform's maximal profits, despite potentially increasing computational load.
  arXiv  Detail & Related papers  (2026-07-15T21:17:42Z)
• Augmentation with Dilution: A Large-Scale Empirical Study of Human Contributor Ecosystems After AI Coding Agent Adoption [5.3319140556634315]
  We study how the number, composition, and behavior of human participants change following AI coding agent adoption in open-source projects.<n>Our results show that AI agent adoption does not significantly change the absolute number of human contributors.<n>We find that AI agents are not displacing human contributors, but are systematically reshaping the structure of open-source ecosystems.
  arXiv  Detail & Related papers  (2026-06-24T18:36:01Z)
• Making MLLMs Blind: Adversarial Smuggling Attacks in MLLM Content Moderation [54.38271718421492]
  Adversarial smuggling attacks exploit the Human-AI capability gap.<n>It encodes harmful content into human-readable visual formats that remain AI-unreadable.<n>We conduct a preliminary exploration of mitigation strategies.
  arXiv  Detail & Related papers  (2026-04-08T11:13:16Z)
• Agentic Test-Time Scaling for WebAgents [65.5178428849495]
  We present Confidence-Aware Test-Time Scaling (CATTS), which uses vote-derived uncertainty to allocate compute only when decisions are genuinely contentious.<n>CATTS improves performance on WebArena-Lite and GoBrowse by up to 9.1% over React while using up to 2.3x fewer tokens than uniform scaling.
  arXiv  Detail & Related papers  (2026-02-12T18:58:30Z)
• Why Are AI Agent Involved Pull Requests (Fix-Related) Remain Unmerged? An Empirical Study [5.127121704630949]
  We analyze 8,106 fix related PRs authored by five widely used AI coding agents from the AIDEV POP dataset.<n>Our results indicate that test case failures and prior resolution of the same issues by other PRs are the most common causes of non integration.
  arXiv  Detail & Related papers  (2026-01-29T22:06:58Z)
• Emergence: Overcoming Privileged Information Bias in Asymmetric Embodied Agents via Active Querying [0.0]
  Large Language Models (LLMs) act as powerful reasoning engines but struggle with "symbol grounding" in embodied environments.<n>We investigate the Privileged Information Bias (or "Curse of Knowledge"), where a knowledgeable "Leader" agent fails to guide a sensor-limited "Follower" due to a lack of Theory of Mind.<n>Our experiments reveal a significant "Success Gap": while the Leader successfully perceives the target in 35.0% of episodes, the collaborative team succeeds only 17.0% of the time, implying that nearly 50% of feasible plans fail solely due to communicative grounding errors.
  arXiv  Detail & Related papers  (2025-12-13T17:17:51Z)
• The AI Attribution Paradox: Transparency as Social Strategy in Open-Source Software Development [0.0]
  We analyze 14,300 GitHub commits across 7,393 repositories from 2023-2025.<n>We investigated attribution strategies and community responses across eight major AI tools.<n>We find developers strategically balance acknowledging AI assistance with managing community scrutiny.
  arXiv  Detail & Related papers  (2025-11-30T12:30:55Z)
• Reduced AI Acceptance After the Generative AI Boom: Evidence From a Two-Wave Survey Study [40.26767449635043]
  We examine shifts in public attitudes toward AI before and after the launch of ChatGPT.<n>The proportion of respondents finding AI "not acceptable at all" increased from 23% to 30%.<n>These shifts have amplified existing social inequalities in terms of widened educational, linguistic, and gender gaps post-boom.
  arXiv  Detail & Related papers  (2025-10-27T17:47:58Z)
• CoCoNUTS: Concentrating on Content while Neglecting Uninformative Textual Styles for AI-Generated Peer Review Detection [60.52240468810558]
  We introduce CoCoNUTS, a content-oriented benchmark built upon a fine-grained dataset of AI-generated peer reviews.<n>We also develop CoCoDet, an AI review detector via a multi-task learning framework, to achieve more accurate and robust detection of AI involvement in review content.
  arXiv  Detail & Related papers  (2025-08-28T06:03:11Z)
• Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition [101.86739402748995]
  We run the largest public red-teaming competition to date, targeting 22 frontier AI agents across 44 realistic deployment scenarios.<n>We build the Agent Red Teaming benchmark and evaluate it across 19 state-of-the-art models.<n>Our findings highlight critical and persistent vulnerabilities in today's AI agents.
  arXiv  Detail & Related papers  (2025-07-28T05:13:04Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)

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.