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Kernel.org administrators reported that automated clients account for about 98% of traffic to git.kernel.org, imposing substantial load on infrastructure that serves Linux kernel Git repositories. In a recent measurement, 66% of requests failed the Anubis anti-bot challenge, while roughly another third completed the browser-side JavaScript SHA-256 proof-of-work check but still exhibited bot-like behavior.
The traffic includes repeated repository retrievals through Git clone activity and web-interface requests. Across five git.kernel.org systems, Git-serving workloads consume approximately 14–16 of the service's 90 CPU cores during peak periods. IP-address blocking has offered limited relief because clients rotate or acquire new source addresses, leaving maintainers to contend with persistent automated crawling despite the challenge mechanism.
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Kernel.org infrastructure administrators reported that automated clients repeatedly accessed Git repositories through clone-like operations and the web interface, creating substantial resource load. Their measurements found 66% of requests failed the Anubis challenge, while roughly 98% of those that passed still appeared automated; Git servicing consumed about 14–16 of 90 CPU cores at peak.
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Datadog Security Research reported sustained campaigns abusing GitHub’s public and authenticated APIs to enumerate enterprise organizations, repositories, and users while blending into normal developer traffic. The activity relied on automated scraping through endpoints including `/graphql`, dormant “ghost” GitHub accounts, and in some cases compromised OAuth tokens or personal access tokens. Researchers identified more than 50 ghost accounts since October 2025, along with recurring user agents such as `GitHub-Company-Scraper`, `GitHub-Scraper-Tool/1.0`, `GitHubAnalytics/1.5`, `GitHub-Commit-Fetcher/1.3`, `GitHub-Commit-Fetcher/1.4`, and `GitHub-Event-Fetcher/2.2`, with some infrastructure linked to 3xK Tech. While much of the traffic queried public data and appeared benign in isolation, researchers said the aggregate behavior amounted to coordinated reconnaissance that could support later theft of secrets, repository cloning, or broader intrusion activity. In a more serious observed case, a tool identified as `repo-dumper` successfully cloned or accessed a private repository, with GitHub audit logs showing `git.clone` and `api.request` events against private paths. Defenders were urged to baseline GitHub audit activity and hunt for unusual user agents, actor names, token types, source ASNs, and successful access to private resources, while also enforcing MFA, conducting access reviews, enabling audit log streaming, and using repository secret scanning.
Jul 11, 2026
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Mozilla’s **0DIN** researchers demonstrated a supply-chain-style proof of concept in which an AI coding agent such as **Claude Code** can be manipulated into compromising a developer’s machine while cloning and initializing a seemingly benign GitHub repository. The repository itself can appear clean, with a legitimate-looking `README` and no obviously malicious code, but a staged setup flow causes a package to fail until initialization and prompts the agent to run follow-up commands it interprets as routine recovery steps. In the demonstrated chain, an initialization script fetched attacker-controlled data from a **DNS TXT** record and executed it, with the TXT record carrying a base64-encoded reverse-shell payload that could give the attacker interactive access under the developer’s privileges. Researchers warned that such access could expose environment variables, API keys, local configuration files, source code, documents, browser sessions, passwords, and enable persistence, while remaining difficult for scanners, human reviewers, and AI agents to detect because each step appears normal in isolation. They said threat actors could distribute these repositories through fake job offers, tutorials, blog posts, or direct messages, and urged agentic tools to reveal the full execution chain, including dynamically fetched scripts and runtime code.
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