Incident Score: Analysis & Impact (ANYNPMWINGOO1775593675)
The details regarding individual company incidents & reports gives you full view from every side.
Rankiteo Score Impact Analysis
Key Highlights From The Incident Analysis
- Timeline of Anysphere's Cyber Attack and lateral movement inside company's environment.
- Overview of affected data sets, including SSNs and PHI, and why they materially increase incident severity.
- How Rankiteo’s incident engine converts technical details into a normalized incident score.
- How this cyber incident impacts Anysphere Rankiteo cyber scoring and cyber rating.
- Rankiteo’s MITRE ATT&CK correlation analysis for this incident, with associated confidence level.
Full Incident Analysis Transcript
In this Rankiteo incident briefing, we review the Anysphere breach identified under incident ID ANYNPMWINGOO1775593675.
The analysis begins with a detailed overview of Anysphere's information like the linkedin page: https://www.linkedin.com/company/anysphereinc, the number of followers: 32243, the industry type: Software Development and the number of employees: 1426 employees
After the initial compromise, the video explains how Rankiteo's incident engine converts technical details into a normalized incident score. The incident score before the incident was 747 and after the incident was 727 with a difference of -20 which is could be a good indicator of the severity and impact of the incident.
In the next step of the video, we will analyze in more details the incident and the impact it had on Anysphere and their customers.
On 20 March 2026, a cybersecurity incident called "New Supply Chain Attack Targets AI Developers with Malicious npm Package" came to light.
A sophisticated supply chain attack emerged when a threat actor published a malicious npm package, *gemini-ai-checker*, under the account *gemini-check*.
The disruption is felt across the environment, affecting AI developer tools (Cursor, Claude, Windsurf, PearAI, Gemini CLI, Eigent AI), and exposing API keys, conversation logs, source code, browser credentials, cryptocurrency wallets.
In response, moved swiftly to contain the threat with measures like Removal of malicious npm packages (*gemini-ai-checker* removed before April 1, 2026), and began remediation that includes Monitor outbound connections to Vercel, use Microsoft’s KQL queries to detect suspicious Node.js behavior.
The case underscores how Ongoing, teams are taking away lessons such as Risks of unverified npm packages, need to treat AI tool directories with the same caution as sensitive system folders, and recommending next steps like Monitor outbound connections to Vercel, use Microsoft’s KQL queries for detection, verify third-party packages before installation.
Finally, we try to match the incident with the MITRE ATT&CK framework to see if there is any correlation between the incident and the MITRE ATT&CK framework.
The MITRE ATT&CK framework is a knowledge base of techniques and sub-techniques that are used to describe the tactics and procedures of cyber adversaries. It is a powerful tool for understanding the threat landscape and for developing effective defense strategies.
MITRE ATT&CK® Correlation Analysis
Rankiteo's analysis has identified several MITRE ATT&CK tactics and techniques associated with this incident, each with varying levels of confidence based on available evidence. Under the Initial Access tactic, the analysis identified Supply Chain Compromise: Compromise Software Supply Chain (T1195.002) with high confidence (95%), supported by evidence indicating malicious npm package, *gemini-ai-checker*, under the account *gemini-check* and User Execution: Malicious File (T1204.002) with high confidence (90%), supported by evidence indicating upon installation, the malware silently contacted a Vercel-hosted staging server. Under the Execution tactic, the analysis identified Command and Scripting Interpreter: JavaScript (T1059.007) with high confidence (95%), supported by evidence indicating javaScript payload downloaded and executed directly in memory and Reflective Code Loading (T1620) with high confidence (90%), supported by evidence indicating executed in memory using Function.constructor instead of eval. Under the Persistence tactic, the analysis identified Hijack Execution Flow: DLL Search Order Hijacking (T1574.001) with moderate to high confidence (70%), supported by evidence indicating 44 files and four dependencies, appearing legitimate with a SECURITY.md file. Under the Defense Evasion tactic, the analysis identified Obfuscated Files or Information (T1027) with high confidence (90%), supported by evidence indicating hidden *libconfig.js* split C2 configuration into fragments, reassembled at runtime, Reflective Code Loading (T1620) with high confidence (90%), supported by evidence indicating executed in memory using Function.constructor to bypass static analysis, Masquerading (T1036) with moderate to high confidence (85%), supported by evidence indicating rEADME mimicked legitimate *chai-await-async* library, unrelated red flag, and Virtualization/Sandbox Evasion: Time Based Evasion (T1497.003) with moderate to high confidence (70%), supported by evidence indicating module 3 monitored clipboard with delayed startup to avoid sandbox detection. Under the Credential Access tactic, the analysis identified Credentials from Password Stores: Credentials from Web Browsers (T1555.003) with high confidence (90%), supported by evidence indicating module 1 targeted browser databases and cryptocurrency wallets and Steal Application Access Token (T1528) with high confidence (95%), supported by evidence indicating designed to steal credentials, files, and tokens from AI coding environments. Under the Collection tactic, the analysis identified Data from Local System (T1005) with high confidence (95%), supported by evidence indicating module 2 scanned for sensitive files in AI tool directories, Automated Collection (T1119) with high confidence (90%), supported by evidence indicating four-module architecture, each running as a separate Node.js process, and Clipboard Data (T1115) with moderate to high confidence (80%), supported by evidence indicating module 3 monitored the clipboard with delayed startup. Under the Command and Control tactic, the analysis identified Application Layer Protocol: Web Protocols (T1071.001) with high confidence (90%), supported by evidence indicating contacted a Vercel-hosted staging server (*server-check-genimi.vercel.app*), Encrypted Channel: Asymmetric Cryptography (T1573.002) with moderate to high confidence (70%), supported by evidence indicating socket.IO used for remote access (Module 0), and Ingress Tool Transfer (T1105) with high confidence (90%), supported by evidence indicating download and execute a JavaScript payload directly in memory. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with high confidence (95%), supported by evidence indicating data exfiltration, unauthorized access to AI environments and Automated Exfiltration (T1020) with high confidence (90%), supported by evidence indicating four-module architecture connected to 216.126.237.71 on dedicated ports. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.
Sources & References
- Anysphere Rankiteo Cyber Incident Details: https://www.rankiteo.com/company/anysphereinc/incident/ANYNPMWINGOO1775593675
- Anysphere CyberSecurity Rating page: https://www.rankiteo.com/company/anysphereinc
- Anysphere Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/anynpmwingoo1775593675-windsurf-cursor-npm-google-cyber-attack-march-2026/
- Anysphere CyberSecurity Score History: https://www.rankiteo.com/company/anysphereinc/history
- Anysphere CyberSecurity Incident Source: https://cybersecuritynews.com/hackers-use-fake-gemini-npm-package/
- Rankiteo A.I CyberSecurity Rating methodology: https://www.rankiteo.com/Images/rankiteo_algo.pdf
- Rankiteo TPRM Scoring methodology: https://static.rankiteo.com/model/rankiteo_tprm_methodology.pdf