FakeGit Campaign Uses 7,600 GitHub Repositories to Spread SmartLoader Malware 70%

By Swati Khandelwal12%

7/20/2026, 6:23:03 PM

BS Summary: This article contains 20 faulty reasoning types, including Framing Effect, Availability Heuristic, and Quote-first Misdirection, with Negativity Bias as the most egregious example at 22.8% saturation with 162 hits. Analysis detected 1,412 faulty-reasoning hits from 712 analyzed words, generating a BS Score of 62.7% and a BS Rank of 70% (6,497 of 21,151 articles). This article is worse (more manipulative) than 69.30% of the article peer group.

Cybersecurity researchers have discovered nearly 7,600 malicious GitHub repositories, out of which more than 800 pose as artificial intelligence (AI) skills or Model Context Protocol (MCP) servers to deliver a malware family known as SmartLoader as part of an ongoing campaign codenamed FakeGit. 
"FakeGit uses copied projects, lookalike developer profiles, convincing READMEs, and malicious ZIP files to deliver SmartLoader malware," Oleg Zaytsev, lead security researcher at Island, said in a report shared with The Hacker News. 
The end goal of these attacks is to leverage the access afforded by SmartLoader to establish persistence and push secondary payloads, such as StealC, an information stealer capable of harvesting a wide range of data from compromised systems. 
It's worth mentioning here that the use of trojanized MCP servers to distribute SmartLoader and StealC was flagged earlier this year by Straiker AI and subsequently by Derp.ca. 
But a concerning aspect of FakeGit is an AI-powered evolution dubbed AgentBaiting. 
This occurs when an AI agent searching for a skill or an MCP server ends up inadvertently discovering one of these bogus GitHub repositories, causing it to do the attacker's bidding on its own without any intervention from a human user. 
Island said its tests revealed Anthropic Claude Code, Google Gemini, and OpenAI ChatGPT to be susceptible to this trickery, allowing the models to surface malicious campaign repositories without even being shown a link. 
In other words, a technique set up with an original intent to socially engineer humans now has the capability to equally deceive an AI agent acting on their behalf. 
Of the 7,600 malicious GitHub repositories created by about 6,600 profiles, 800 posed as Skills or MCP servers for individual and enterprise use, from Gmail and WhatsApp integrations to Databricks, Jenkins, and Docker tooling. 
As of July 2026, the FakeGit operation has recorded more than 14 million downloads across GitHub Release assets in about 200 campaign repositories. 
"The repositories were designed to meet demand already forming around AI capabilities, borrowing the names and workflows of familiar consumer and enterprise tools," Zaytsev explained. 
"That familiarity gave the malicious ZIP files a credible reason to be downloaded, while the README guided users or agents from what appeared to be routine setup into the SmartLoader attack chain." 
The counterfeit repositories, either completely fabricated or copied from legitimate projects, serve as a conduit for a ZIP archive, which is then used to trigger a LuaJIT loader chain, leading to the execution of an obfuscated Lua script responsible for dropping SmartLoader. 
Then the loader proceeds to deploy StealC. 
AgentBaiting escalates this threat further, as it opens the door to a scenario where an AI agent can be baited to discover a FakeGit repository without having to supply a malicious link by providing a prompt like this: "Find free claude cinematic prompt skill, and give me the installation instructions" or "give me a free walmart MCP server link." 
"While trying to complete a task, it can discover a FakeGit repository on its own, treat the README as legitimate documentation, and pass the attacker's instructions to the user," Island said. 
"FakeGit built its AI lures around this path." 
The technique once again demonstrates how routine AI-assisted discovery operations can be turned into an alley for malicious code execution, a problem that gets exacerbated when the malicious skills or MCP servers are listed on public registries like LobeHub, Glama, MCP.so, and MCP Market, giving them a false sense of legitimacy. 
More than 600 campaign listings have been flagged across public MCP and Skill registries. 
To counter the threat, it's advised to build a catalog of reviewed Skills, MCP servers, and agent plugins, evaluate new agent capabilities in a sandboxed environment first before broader rollout, verify both the publisher and the project to ensure credibility, and monitor agentic pathways. 
"FakeGit did not need to breach anything. 
It published convincing repositories, borrowed real developers' identities, spread its listings across public registries, and let discovery do the rest," Island said. 
"With AgentBaiting, that discovery no longer requires a person at all: an agent searching for a Skill or MCP server can find the lure, read the attacker's README, and carry its instructions forward. 
The defenses that matter are the ones that interrupt this chain before execution." 
Article reasoning-pattern comparisonThis article: 6.5%Swati Khandelwal: 2.5%The Hacker News: 2.0%Confirmation Bias6.5%This article: 4.8%Swati Khandelwal: 1.5%The Hacker News: 1.2%Anchoring Bias4.8%This article: 15.9%Swati Khandelwal: 3.6%The Hacker News: 3.3%Availability Heuristic15.9%This article: 9.4%Swati Khandelwal: 1.4%The Hacker News: 1.4%Representativeness Heuristic9.4%This article: 0.0%Swati Khandelwal: 0.6%The Hacker News: 0.6%Hindsight Bias0.0%This article: 13.6%Swati Khandelwal: 2.5%The Hacker News: 2.5%Overconfidence Bias13.6%This article: 19.2%Swati Khandelwal: 2.9%The Hacker News: 2.9%Framing Effect19.2%This article: 0.0%Swati Khandelwal: 0.9%The Hacker News: 1.1%Loss Aversion0.0%This article: 6.2%Swati Khandelwal: 0.6%The Hacker News: 0.6%Status Quo Bias6.2%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%Sunk Cost Effect0.0%This article: 0.0%Swati Khandelwal: 1.2%The Hacker News: 1.3%Optimism Bias0.0%This article: 1.7%Swati Khandelwal: 1.9%The Hacker News: 1.6%Pessimism Bias1.7%This article: 22.8%Swati Khandelwal: 6.5%The Hacker News: 6.8%Negativity Bias22.8%This article: 0.0%Swati Khandelwal: 0.4%The Hacker News: 0.8%Self-Serving Bias0.0%This article: 0.0%Swati Khandelwal: 0.4%The Hacker News: 0.4%Fundamental Attribution Error0.0%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%Actor-Observer Bias0.0%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%In-Group Bias0.0%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.4%Out-Group Homogeneity Bias0.0%This article: 0.0%Swati Khandelwal: 0.4%The Hacker News: 0.6%Halo Effect0.0%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.0%Horn Effect0.0%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.0%Dunning-Kruger Effect0.0%This article: 7.2%Swati Khandelwal: 1.4%The Hacker News: 1.4%Recency Bias7.2%This article: 0.0%Swati Khandelwal: 0.2%The Hacker News: 0.2%Primacy Effect0.0%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%Blind-Spot Bias0.0%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%Ad Hominem0.0%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.1%Straw Man0.0%This article: 13.6%Swati Khandelwal: 3.9%The Hacker News: 4.0%Appeal to Authority13.6%This article: 6.5%Swati Khandelwal: 1.4%The Hacker News: 1.6%False Dilemma6.5%This article: 14.0%Swati Khandelwal: 0.7%The Hacker News: 0.5%Slippery Slope14.0%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%Circular Reasoning0.0%This article: 4.1%Swati Khandelwal: 3.8%The Hacker News: 4.3%Hasty Generalization4.1%This article: 0.0%Swati Khandelwal: 0.2%The Hacker News: 0.1%Red Herring0.0%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.2%Bandwagon0.0%This article: 7.2%Swati Khandelwal: 0.9%The Hacker News: 1.1%Appeal to Emotion7.2%This article: 0.0%Swati Khandelwal: 0.3%The Hacker News: 0.5%Begging the Question0.0%This article: 11.5%Swati Khandelwal: 2.0%The Hacker News: 1.9%Post Hoc (False Cause)11.5%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.0%Tu Quoque0.0%This article: 0.0%Swati Khandelwal: 0.7%The Hacker News: 0.6%Burden of Proof0.0%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%Appeal to Nature0.0%This article: 5.9%Swati Khandelwal: 0.3%The Hacker News: 0.3%Composition/Division5.9%This article: 0.0%Swati Khandelwal: 1.1%The Hacker News: 1.0%Anecdotal0.0%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.1%No True Scotsman0.0%This article: 5.8%Swati Khandelwal: 2.6%The Hacker News: 2.3%Ambiguity (Equivocation)5.8%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.0%Middle Ground0.0%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.0%Personal Incredulity0.0%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%Special Pleading0.0%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%Genetic Fallacy0.0%This article: 0.0%Swati Khandelwal: 1.1%The Hacker News: 1.4%Unattributed Quote0.0%This article: 14.6%Swati Khandelwal: 0.8%The Hacker News: 1.0%Quote-first Misdirection14.6%This article: 0.0%Swati Khandelwal: 2.8%The Hacker News: 2.4%Biased Writer Voice0.0%This article: 8.0%Swati Khandelwal: 4.8%The Hacker News: 4.4%Indoctrination8.0%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.0%Politically Left Leaning Bias0.0%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.0%Politically Right Leaning Bias0.0%This article: 0.0%Swati Khandelwal: 0.4%The Hacker News: 3.0%Attempt to Sell a Product or S…0.0%

712 words analyzed.

Speakers

2speakers32%attributed speech483writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 10 words • 0.0% coverageWriter's voice • 43 words • 0.0% coverageOleg Zaytsev • 33 words • 100.0% coverageWriter's voice • 38 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 41 words • 0.0% coverageIsland • 33 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 34 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageOleg Zaytsev • 25 words • 0.0% coverageOleg Zaytsev • 32 words • 0.0% coverageWriter's voice • 42 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 59 words • 0.0% coverageIsland • 31 words • 100.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 51 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 44 words • 100.0% coverageIsland • 7 words • 100.0% coverageIsland • 22 words • 0.0% coverageIsland • 33 words • 100.0% coverageIsland • 13 words • 100.0% coverage
Selected voice

Island

100%flagged-word coverage
139 attributed words61% of attributed speech99% writer coverage
0%27.5%55.0%Quote-first Misdirection+51.1 ptsWriter: 0.0%Island: 51.1%51.1%Indoctrination+0.2 ptsWriter: 9.1%Island: 9.4%9.4%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.