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name: Mini Shai-Hulud C2 and Exfiltration Infrastructure Connection Analyticcategory: 'Command and Control'threatname: 'Exfiltration Over Web Service'functionality: 'Web Proxy'description: | Detects outbound connections to infrastructure used by the Mini Shai-Hulud npm worm (TeamPCP) for C2 communication and credential exfiltration. The worm uses a dual-channel exfiltration architecture for redundancy: Channel 1 - Session Protocol CDN: Stolen credentials encrypted with RSA-4096-OAEP wrapped AES-256-GCM are uploaded to filev2.getsession.org. The worm hardcodes a TLS cert pin for seed1.getsession.org. Session Protocol is a legitimate privacy messaging service — most enterprises have no legitimate traffic to this domain, but validate against your approved application inventory before deploying the session selector without a filter allowlist. Channel 2 - GitHub GraphQL dead-drop: Encrypted data committed to attacker-controlled repos via api.github.com

name: Mini Shai-Hulud Claude Code or VS Code Persistence Hook Injection Analyticcategory: 'Persistence'threatname: 'Event Triggered Execution: Installer Packages'functionality: 'Endpoint Management Systems'description: | Detects unauthorized modifications to Claude Code settings.json or VS Code tasks.json by processes consistent with the Mini Shai-Hulud worm (TeamPCP) persistence mechanism. The worm deploys two IDE-level hooks that re-execute the malicious payload on every developer tool launch: Payload 4: Writes .claude/settings.json with a SessionStart hook that executes .vscode/setup.mjs on every Claude Code session start. Payload 7: Writes .vscode/tasks.json with a folderOpen task that executes .claude/setup.mjs on every VS Code workspace open. Both hooks re-run .claude/router_runtime.js (the Bun worm payload) to re-exfiltrate any newly available credentials after the initial infection. Writes to these files by bun, node, sh, or bash process

name: Mini Shai-Hulud gh-token-monitor Persistence Service Registration Analyticcategory: 'Persistence'threatname: 'Boot or Logon Autostart Execution: Systemd Service'functionality: 'Endpoint Management Systems'description: | Detects the installation of the gh-token-monitor persistence service deployed by the Mini Shai-Hulud npm worm (TeamPCP). After stealing credentials from an npm install context, the worm's Payload 1 installs a platform-native service that continuously monitors for newly available GitHub tokens and re-exfiltrates them on every reboot. The service name gh-token-monitor is campaign-specific and has no known legitimate use. On Linux: ~/.config/systemd/user/gh-token-monitor.service via systemctl --user enable On macOS: ~/Library/LaunchAgents/com.user.gh-token-monitor.plist via launchctl load DEPLOYMENT SCOPE: Developer workstations ONLY. This rule has no value on CI runners of any type. GitHub managed runners are ephemeral VMs with no endpoint visibility. Self

name: Bun Runtime Spawned During npm Package Installation Analyticcategory: 'Execution'threatname: 'Command and Scripting Interpreter: JavaScript'functionality: 'Endpoint Management Systems'description: | Detects the Bun JavaScript runtime being silently installed and executed during an npm package installation lifecycle hook, consistent with the Mini Shai-Hulud worm (TeamPCP). The attack embeds a prepare script in a malicious optional dependency (@tanstack/setup) that installs Bun 1.3.13 silently, then executes a 2.3 MB obfuscated payload via bun run. Bun is used specifically because it lacks the --require hook interception used by most Node.js security and monitoring tools, and because its Bun.gunzipSync function is required to decompress the AES-256-GCM encrypted secondary payloads. Bun being spawned as a child of a node/npm install process with known worm payload filenames in the CommandLine is not a pattern that occurs in legitimate dependency installation. DEPLOYMENT SC

name: Mini Shai-Hulud Runner.Worker Memory Secret Extraction Analyticcategory: 'Credential Access'threatname: 'Unsecured Credentials: CI/CD Object Credentials'functionality: 'Endpoint Management Systems'description: | Detects the secret extraction grep pattern used by the Mini Shai-Hulud npm worm (TeamPCP) to harvest all GitHub Actions secrets from Runner.Worker process memory. The worm executes a Python script via stdin that reads /proc/{Runner.Worker PID}/mem across all readable memory pages, then pipes output through grep targeting the runner's internal masked secret representation: {"value":"...","isSecret":true} The full pipeline executed from within the Bun payload process is: sudo python3 | tr -d '\0' | grep -aoE '"[^"]+":{"value":"[^"]*","isSecret":true}' | sort -u The isSecret string in a grep CommandLine during any package install context has no legitimate use case and is a near-zero-FP indicator of this specific attack technique. DEPLOYMENT SCOPE: Developer works

In Frank Herbert's Dune, a Shai-Hulud is a sandworm: a massive, blind, consuming creature that moves beneath the surface, swallowing everything in its path. It is an apt name for what security researchers discovered on May 11, 2026, buried inside some of the most widely used JavaScript packages in the world.Mini Shai-Hulud is a supply chain worm attributed to a threat group called TeamPCP. It compromised over 160 npm packages, collectively downloaded hundreds of millions of times per week, and it did something that no npm-based attack had managed before: it produced infected packages with valid cryptographic proof of authenticity. To the tools designed to tell you whether a package is trustworthy, these packages looked perfectly legitimate. They were not.This article walks through the technology that makes this attack possible, why it matters, and how to detect it on the systems you actually control.What Is npm and Why Does It Matter?Before understanding how this worm spreads, you need

In Part One, the argument was that AI is undergoing the same miniaturization arc that turned the 1980s brick phone into the supercomputer in your pocket. Pocket-sized devices running 120-billion-parameter models offline. Photonic chips doing in light what used to take racks of silicon. Architecture researchers reverse-engineering frontier models in public. The compute is shrinking, the cost is falling, and the assumptions defenders built their posture on are quietly expiring.But while we have been watching AI shrink downward, into devices, into edge hardware, into your jacket pocket, something else is happening in the other direction entirely. The phone is not just getting smaller. It is also going to orbit.And that changes almost everything about the GPU market, the chip supply chain, and the geopolitics of AI compute.Terafab: The Most Ambitious Vertical Integration Play in Semiconductor HistoryOn March 21, 2026, Elon Musk took the stage at the defunct Seaholm Power Plant in Austin, T

The proverbial phone is shrinking before my eyes.In the 1980s, the "mobile phone" was a brick. A two-pound, briefcase-attached monument to the idea that wireless communication was possible, if inconvenient. Those who carried one weren't just making calls. They were signaling that technology had crossed a threshold. Today, you carry more compute in your pocket than existed in entire buildings forty years ago. The phone didn't just shrink. It became something unrecognizable.AI is doing the same thing. Right now. And the cybersecurity implications are only beginning to surface.From Cloud Cathedrals to Pocket SupercomputersFor the last several years, running a frontier-class AI model meant paying homage to the cloud. Massive GPU clusters. Enormous energy bills. Data that had to leave your network to be processed somewhere in a hyperscaler's geography. The compute was centralized by necessity, and that centralization gave defenders a chokepoint. You could watch egress. You could inspect wha

name: PowerShell Script Block and Transcription Logging Suppression Analyticcategory: "Defense Evasion"threatname: "Impair Defenses: Indicator Blocking"functionality: "Endpoint Management Systems"description: |  Detects registry modifications that disable PowerShell Script Block Logging  or Transcription Logging under the Windows PowerShell policy keys.  Deep#Door suppresses these controls to prevent logging of its obfuscated  PowerShell execution chain, including embedded payload extraction commands.  Disabling these features removes visibility into decoded runtime commands  and hinders forensic reconstruction of the staging sequence.reference:labels:  - attack.defense_evasion  - attack.t1562.006  - Deep#Doorlogsource:  category: registry_event  product: windowsdetection:  selection:    EventType: "SetValue"    TargetObject|contains:      - '\PowerShell\ScriptBlockLogging\EnableScriptBlockLogging'      - '\PowerShell\Transcription\EnableTranscripting'    description:      - "0"      -

name: VBScript Backdoor Launcher Dropped to User Startup Folder Analyticcategory: "Persistence"threatname: "Boot or Logon Autostart Execution: Registry Run Keys / Startup Folder"functionality: "Endpoint Management Systems"description: |  Detects VBScript files created in the user Startup directory, a persistence  mechanism used by Deep#Door to execute the Python backdoor silently at each  user logon without requiring elevated privileges. The implant drops  SystemServices.vbs into the Startup folder to invoke pythonw.exe against  the staged svc.py payload.reference:labels:  - attack.persistence  - attack.t1547.001  - Deep#Doorlogsource:  category: file_event  product: windowsdetection:  selection:    Filename|endswith:      - ".vbs"      - ".vbe"    FilePath|contains:      - '\Microsoft\Windows\Start Menu\Programs\Startup'  condition: selectioncriticality: Highsaveasthreat: falseviolation_summary:  grouping_attribute: "accountname"  level2_attribute:  level2_metadata_attributes:  metada

name: Windows Firewall Connection Logging Disabled via netsh Analyticcategory: "Defense Evasion"threatname: "Impair Defenses: Disable or Modify System Firewall"functionality: "Endpoint Management Systems"description: |  Detects netsh commands that disable both dropped-connection and allowed-connection  logging across all Windows Firewall profiles. Deep#Door issues these commands  during staging to suppress logging of its outbound bore.pub C2 tunneling traffic,  preventing network-based detection and impeding forensic reconstruction of  attacker communications.reference:labels:  - attack.defense_evasion  - attack.t1562.004  - Deep#Doorlogsource:  category: process_creation  product: windowsdetection:  selection_netsh:    Image|endswith: '\netsh.exe'    CommandLine|contains:      - 'advfirewall set allprofiles logging'  selection_logging_types:    CommandLine|contains:      - 'droppedconnections'      - 'allowedconnections'  selection_disable:    CommandLine|contains:      - 'disable'  c

name: Self-Referential Batch Script Payload Extraction Analyticcategory: 'Execution'threatname: 'Command and Scripting Interpreter: Windows Command Shell'functionality: 'Microsoft Windows PowerShell'description: |  Detects PowerShell script block activity consistent with the Deep#Door loader's  self-extraction mechanism. The malware uses an obfuscated batch script  (install_obf.bat) that invokes PowerShell to read its own file contents via  %~f0 (self-file reference) and extract an embedded Python backdoor using  regex pattern matching between #PYTHON_START and #PYTHON_END delimiters.  This staging approach is notable because:    - The dropper is fully self-contained and does not require external downloads    - %~f0 allows the script to reference and parse its own file contents in place    - The payload is recovered using regex extraction rather than a separate archive      or encoded blob fetch    - The extracted svc.py implant is written directly to      %LOCALAPPDATA%\SystemServices

 Have a Product Idea? Help Shape the Future of Securonix Innovation at Securonix is driven by you — our customers and partners. Whether you’ve identified a gap, experienced a challenge, or have an idea for a new feature, we want to hear from you. We’ve created a dedicated Product Ideation space where you can share ideas, collaborate with others, and help influence our product roadmap.  🚀 What You Can Do  Submit feature requests directly to our Product team Share ideas to improve workflows and usability Collaborate with other customers and build on ideas Upvote submissions to help prioritize what matters most   🔐 Access the Full Guide & Submit Your Idea To learn how the ideation process works — including what to submit, when to submit it, and how ideas are reviewed — access the full guide below: 👉 [Log in to view the Product Ideation Guide ] 🔒 This guide is available to logged-in customers only. 🧭 What You’ll Find Inside Once logged in, you’ll get step-by-step guidance on

On April 19, 2026, Vercel — the cloud platform behind the widely adopted Next.js web framework and a core piece of infrastructure for thousands of JavaScript developers and Web3 projects — publicly confirmed a security incident involving unauthorized access to certain internal systems. The breach did not originate from a direct attack on Vercel's infrastructure. Instead, it followed a classic supply chain compromise path: a third-party AI tool was breached first, which gave the attacker the foothold needed to pivot into Vercel's environment.As of April 21, 2026, the investigation remains active. Vercel is working with Mandiant, law enforcement and Context.ai to determine the full scope of the intrusion.Timeline of EventsDate Event February 2026 A Context.ai employee is compromised with Lumma Stealer after downloading malicious Roblox "auto-farm" scripts and executors March 2026 Context.ai identifies and blocks unauthorized access to its AWS environment but does not yet identify