There has been an extensive malware campaign, dubbed FakeGit, that utilizes thousands of counterfeit GitHub repositories to distribute SmartLoader malware, which is increasingly targeted at exploiting artificial intelligence (AI) tools and Model Context Protocol (MCP) servers in order to distribute the malware. Researchers at Island have discovered that approximately 7,600 malicious GitHub repositories have been constructed by using approximately 6,600 false developers profiles, creating nearly 7,600 malicious GitHub repositories. Thousands of repositories are masquerading as AI skills or MCP servers, offering integration with services such as Google Mail, WhatsApp, Docker, Jenkins, and Databricks. It is believed that FakeGit is an evolution of a previous malware operation that was previously associated with Water Kurita and that used Lumma Stealer. Research by Island researchers indicates that in March 2026, the campaign began focusing on artificial intelligence-based repositories, peaking in April with hundreds of repositories impersonating artificial intelligence tools before expanding into a broader ecosystem of fake AI agents, workflows, and MCP servers. By copying code, creating convincing README files, and impersonating developer identities, the fake repositories are very closely resembling legitimate open-source projects. A multi-stage infection chain is triggered by the download of malicious ZIP archives. Upon activation, the attack launches a LuaJIT-based loader that launches an obfuscated Lua script to install SmartLoader. SmartLoader establishes persistence on the compromised system and launches StealC, a malicious program capable of harvesting sensitive data from infected devices once it has been activated. After installation, SmartLoader creates persistence using scheduled tasks, retrieves its C2 server using the Polygon blockchain smart contract, downloads encrypted payloads hosted on GitHub, and ultimately deploys the StealC information stealer by deploying the C2 server. A new advanced tactic, AgentBaiting, has also been identified, which highlights how AI-powered coding assistants and autonomous agents can unintentionally aid hackers in gaining control of a computer. By optimizing fake repositories, threat actors can provide users with legitimate resources instead of forcing them to visit malicious links. Research conducted by Island researchers demonstrated that Claude Code automatically replicated malicious repositories and downloaded the associated files onto a test system, resulting in the discovery and recommendation of legitimate resources by AI models searching for free AI skills or MCP servers. In spite of this, the AI assistant detected suspicious indicators before executing the payload, which suggests that even though AI agents can be manipulated into retrieving malicious content, they may still be capable of detecting threats later on during the execution phase. In spite of the fact that these limited tests were not intended to measure the overall detection capabilities of artificial intelligence coding assistants, Island research demonstrated that AI assistants, such as Anthropic Claude Code, Google Gemini, and OpenAI ChatGPT, could detect malicious repositories during routine searches in response to user requests. Through artificial intelligence-assisted discovery processes, attackers can potentially pass malicious installation instructions to users without direct human interaction. More than 14 million downloads were recorded between the 335 malicious release assets hosted in approximately 211 FakeGit repositories as a result of GitHub's public statistics. In analyzing this figure, researchers cautioned that it represents cumulative download requests, including automated activity, and should not be interpreted as a count of successful infections. According to security experts, FakeGit illustrates how trust in open-source ecosystems and AI-assisted software discovery can be exploited without directly compromising any platforms. It is more common for attackers to distribute malware through convincing branding, fictitious developer identities, and public registries. To prevent malicious code from entering development environments, organizations should verify repository publishers, evaluate AI skills and MCP servers in isolated environments before deployment, maintain approved catalogs of trusted AI plugins, and monitor AI-assisted workflows to ensure that they are not compromised. A number of the fake repositories were also observed to be more credible by using duplicate project descriptions, fabricating star ratings and fork counts, and impersonating legitimate developer identities, as well as impersonating legitimate developers. In this manner, malicious projects were significantly more likely to be trusted and downloaded by developers and AI-assisted coding tools. AI agents are increasingly involved in the discovery and deployment of software, but researchers warn that the security of these automated workflows is as important as ensuring that human users are protected from traditional social engineering attacks. Using trusted developer platforms and AI-assisted workflows, cybercriminals are adjusting to the AI era through the FakeGit campaign. The increasing reliance on AI tools and open-source repositories calls for verification of software sources, limiting untrusted AI integrations, and strengthening supply chain security.
