3CX Breach Incident Score: Analysis & Impact (3CX2832428111125)

In this Rankiteo incident briefing, we review the 3CX breach identified under incident ID 3CX2832428111125.

The analysis begins with a detailed overview of 3CX's information like the linkedin page: https://www.linkedin.com/company/3cx, the number of followers: 58550, the industry type: Software Development and the number of employees: 137 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 753 and after the incident was 707 with a difference of -46 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 3CX and their customers.

3CX recently reported "AI-Enabled Supply Chain Attacks Surge 156% with Advanced Polymorphic Malware and AI-Generated Threats", a noteworthy cybersecurity incident.

AI-enabled supply chain attacks have surged 156% in the past year, leveraging polymorphic, context-aware, and temporally evasive malware.

The disruption is felt across the environment, affecting 600,000 companies (3CX breach), Thousands of systems (PyTorch/torchtriton) and AI/ML environments (NullBulge, Hugging Face/GitHub), and exposing Private Keys (Solana Web3.js), Sensitive ML Environment Data (PyTorch/torchtriton) and User Data (Wondershare RepairIt hardcoded credentials), plus an estimated financial loss of ['$160,000–$190,000 (Solana Web3.js attack)', 'Potential fines up to €35M or 7% global revenue (EU AI Act violations)'].

In response, moved swiftly to contain the threat with measures like Runtime Application Self-Protection (RASP) by Netflix, Behavioral Provenance Analysis (commit pattern tracking) and AI-Specific Detection (statistical analysis of code patterns), and began remediation that includes Dependency Audits for Typosquatting Variants, Commit Signing Enforcement (GPG) and Review of Recently Added Packages (90-day lookback).

The case underscores how Ongoing (multiple incidents; some resolved, others active), teams are taking away lessons such as Traditional security tools (static analysis, signature-based detection) are ineffective against AI-generated polymorphic malware, AI supply chain attacks exploit trust in open-source ecosystems, requiring behavioral and provenance-based defenses and Delayed breach detection (avg. 276 days) exacerbates impact; real-time monitoring is critical, and recommending next steps like {'immediate': ["Audit dependencies for typosquatting variants (e.g., 'tensorfllow').", 'Enable commit signing (GPG) for critical repositories.', 'Review all packages added in the last 90 days for suspicious activity.']}, {'short_term': ['Deploy behavioral analysis tools in CI/CD pipelines.', 'Implement runtime protection (RASP) for critical applications.', "Establish 'proof of humanity' requirements for new contributors (e.g., verified identities).", 'Integrate AI-specific detection tools (e.g., Google OSS-Fuzz statistical analysis).']} and {'long_term': ['Develop an AI incident response playbook tailored to supply chain threats.', 'Align security controls with regulatory requirements (e.g., EU AI Act transparency obligations).', 'Adopt zero-trust architectures with continuous authentication and least-privilege access.', 'Invest in defensive AI capabilities (e.g., red teaming, adversarial ML testing).', 'Implement automated dependency hygiene tools to block high-risk packages.', 'Conduct regular AI model integrity audits to detect data poisoning.']}, with advisories going out to stakeholders covering CISOs: Prioritize AI-aware security tools and zero-trust architectures, Developers: Verify open-source dependencies with behavioral analysis and Compliance Teams: Align with EU AI Act requirements for AI supply chain security.

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.

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 Dependencies (T1195.002) with high confidence (100%), with evidence including compromised Open-Source Packages (PyPI, npm, GitHub, Hugging Face), and 600,000 companies (3CX breach) via trojanized desktop app, Phishing: Spearphishing Link (T1566.002) with high confidence (90%), supported by evidence indicating phished Publish-Access Credentials (Solana Web3.js), and Valid Accounts: Cloud Accounts (T1078.004) with high confidence (95%), supported by evidence indicating hardcoded Cloud Credentials in Binaries (Wondershare RepairIt). Under the Execution tactic, the analysis identified Command and Scripting Interpreter: JavaScript (T1059.007) with high confidence (90%), supported by evidence indicating javaScript Libraries (npm) used for malicious packages and Command and Scripting Interpreter: Python (T1059.006) with high confidence (90%), supported by evidence indicating python Packages (PyPI) like torchtriton exploited. Under the Persistence tactic, the analysis identified Create or Modify System Process: Windows Service (T1543.003) with moderate to high confidence (85%), supported by evidence indicating trojanized 3CX desktop app installed malware on end-user systems and Server Software Component: Web Shell (T1505.003) with moderate to high confidence (80%), supported by evidence indicating backdoored Dependencies (e.g., torchtriton, ComfyUI_LLMVISION). Under the Privilege Escalation tactic, the analysis identified Valid Accounts: Local Accounts (T1078.003) with high confidence (90%), supported by evidence indicating private Keys (Solana Web3.js) stolen for escalation. Under the Defense Evasion tactic, the analysis identified Obfuscated Files or Information: Software Packing (T1027.002) with high confidence (100%), with evidence including polymorphic malware evading signature-based detection, and aI-like characteristics such as unique payloads per victim, Obfuscated Files or Information: HTML Smuggling (T1027.006) with moderate to high confidence (70%), supported by evidence indicating automated Social Engineering (context-aware pull requests), Impair Defenses: Disable or Modify Tools (T1562.001) with moderate to high confidence (85%), with evidence including delayed activation to evade sandboxing, and signature-Based Detection Gaps exploited, and Masquerading: Match Legitimate Name or Location (T1036.005) with high confidence (95%), with evidence including typosquatting (e.g., tensorfllow), and fake Developer Personas (SockPuppet attacks). Under the Credential Access tactic, the analysis identified Unsecured Credentials: Credentials In Files (T1552.001) with high confidence (100%), with evidence including hardcoded Credentials in Binaries (Wondershare RepairIt), and user Credentials (hardcoded cloud credentials), Credentials from Password Stores: Credentials from Web Browsers (T1555.003) with moderate to high confidence (80%), supported by evidence indicating private Cryptographic Keys (Solana Web3.js) exfiltrated, and Forge Web Credentials: Web Cookies (T1606.002) with moderate to high confidence (70%), supported by evidence indicating phished Publish-Access Credentials (Solana Web3.js). Under the Discovery tactic, the analysis identified System Information Discovery (T1082) with moderate to high confidence (85%), supported by evidence indicating aI Model Tampering suggests environment reconnaissance and File and Directory Discovery (T1083) with moderate to high confidence (80%), supported by evidence indicating sensitive ML Environment Data (PyTorch/torchtriton) targeted. Under the Lateral Movement tactic, the analysis identified Remote Services: SSH (T1021.004) with moderate to high confidence (80%), supported by evidence indicating private Keys (Solana Web3.js) used for lateral movement and Internal Spearphishing (T1534) with moderate to high confidence (75%), supported by evidence indicating automated Social Engineering (context-aware pull requests). Under the Collection tactic, the analysis identified Data from Local System (T1005) with high confidence (95%), with evidence including sensitive ML Environment Data (PyTorch/torchtriton) collected, and user Data (Wondershare RepairIt hardcoded credentials) and Screen Capture (T1113) with moderate to high confidence (70%), supported by evidence indicating aI Model Integrity (data poisoning risks) suggests screen/environment capture. Under the Command and Control tactic, the analysis identified Web Service: One-Way Communication (T1102.003) with high confidence (95%), with evidence including discord Webhook Exfiltration (NullBulge attacks), and automated Transfer to Attacker-Controlled Servers and Proxy: External Proxy (T1090.004) with moderate to high confidence (80%), supported by evidence indicating polymorphic malware likely used proxies for C2. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with high confidence (100%), with evidence including data exfiltration via Discord webhooks, and automated Transfer to Attacker-Controlled Servers and Exfiltration Over Alternative Protocol: Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol (T1048.003) with high confidence (90%), supported by evidence indicating exfiltration via seemingly legitimate webhooks (Discord). Under the Impact tactic, the analysis identified Data Encrypted for Impact (T1486) with high confidence (95%), with evidence including lockBit Ransomware (NullBulge attacks), and ransomware Deployment (LockBit variant), Data Destruction (T1485) with moderate to high confidence (80%), supported by evidence indicating aI Model Sabotage (data poisoning), Server Software Component: Resource Hijacking (T1648) with high confidence (90%), with evidence including cryptocurrency Theft ($160K–$190K in Solana attack), and cryptocurrency Wallets (Solana Web3.js) drained, and Defacement: Internal Defacement (T1491.002) with moderate to high confidence (75%), supported by evidence indicating aI Model Tampering (Wondershare RepairIt). These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.