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Unit 42

The Machine With Many Faces: Post-Exploitation Identity Misuse in SPIFFE/SPIRE Untracked Nightmares: The Threats Hiding Behind Commodity Infrastructure Attackers Expose Ongoing AI Tool Use Targeting Organizations in Latin America An AI-Assisted Cyber Attack: Inside a Unit 42 Investigation Spring Ring: An Inside Look at Voice Phishing Campaigns in Microsoft Teams Perturbation Probing: A New Diagnostic for the Fragility of LLM Safety The State of AI-Enabled Malware August 2026: From Brand Abuse to Agentic Execution Identity Abuse Through Trusted Communication Channels Kimwolf v7: An Evolution of the Kimwolf Botnet The Permanent Threat: Analyzing Aeternum’s Blockchain-Based C2 Operations and Communications Inside the Modern SOC: The Identity Front Door ChainDrop: Inside a Self-Propagating npm Worm Token Jacking: Cybercriminals Could Be Stealing Your AI Resources The Frontier AI Vulnerability Burst: Industrializing Autonomous Zero-Day Discovery in Open-Source Software Almost Half of Malware Samples Communicate Direct to IP Pass the Passkey: A Novel Attack Surface in Passwordless Authentication The Xcode Assassin Returns: A Deep Dive Into the Latest XCSSET Version Chinese-Speaking Threat Actor Harnesses AI Models for Autonomous Cyberattacks Russian Global Webmail Espionage Three Steps to the Terminal: A Siemens ROX II Zero-Day Trilogy AI, Automation and Attacks: Unpacking the Unit 42 2026 Global Incident Response Report AI, Automation and Attacks: Unpacking the Unit 42 2026 Global Incident Response Report TuxBot v3: Inside an IoT Botnet Framework With LLM-Assisted Development No Manners Here: The Ruthless Rise of The Gentlemen Ransomware Vidar Stealer Unmasked: Code Signing Abuse, Go Loaders and File Inflation How We Added WebAuthn to a Browser-Based RDP Client Phantom Squatting: AI-Hallucinated Domains as a Software Supply Chain Vector CL-STA-1062 Targets Southeast Asian Governments and Critical Infrastructure OpenClaw’s Skill Marketplace and the Emerging AI Supply Chain Threat The Global Namespace Risk: Universal Bucket Hijacking Technique for Cloud Data Exfiltration
Connecting the Dots: Securing the Overlooked Corners of t...
Yaron Avital · 2026-08-22 · via Unit 42

While supply chain threats have been quietly compounding over the past decade, the last 12–18 months have triggered a drastic shift in the scale and velocity of these attacks. Rather than just hunting for bugs in finished software, attackers are targeting the everyday tools and code developers rely on.

Unit 42 research shows this happening at every step of the building process. We've observed attackers spending years pretending to be helpful contributors just to hide backdoors in core software, as seen in the XZ Utils vulnerability (CVE-2024-3094). We've seen attackers hijack accounts to drop malware into popular libraries, like in the Axios supply chain attack. And we've seen them misuse setup scripts to automatically steal credentials using the Shai-Hulud npm worm.

Simply put, attackers are more focused on poisoning the digital factory that builds an application as opposed to the application itself. By targeting continuous integration/continuous delivery (CI/CD) pipelines and developer environments, they hijack software at the source before it ever hits production.

Threat Analysis: ChainDrop npm Worm

Consider the recent ChainDrop npm worm, which infected over 400 packages including massively popular libraries like keyv and cacheable-request using a highly evasive three-step chain:

  • The hook: Attackers modified package manifests with a malicious preinstall script that downloaded the legitimate Bun runtime to silently launch a 727 KB obfuscated payload in the background.
  • The theft: Rather than just scraping disk files, a hidden Python script directly read live process memory from GitHub Actions runners to steal temporary OpenID Connect (OIDC) tokens and secrets, alongside a massive sweep for local developer credentials.
  • The payload: The worm used those stolen npm and GitHub tokens to self-propagate, silently infecting and republishing additional packages while leaving their legitimate functionality perfectly intact so that developers don't notice.

ChainDrop secured long-term persistence by establishing cross-linked hooks directly inside developer tools like VS Code and Claude Code, while managing its entire command-and-control (C2) infrastructure dynamically through Ethereum blockchain transactions.

The malware triggers silently the second someone runs npm install by misusing npm's setup scripts (preinstall hooks). From there, it hits three distinct targets:

  1. Cloud secret harvesting: It searches build server memory to scrape unencrypted credentials and platform access tokens
  2. Local endpoint backdooring: It modifies local developer tool configs (like VS Code's tasks.json) so the attacker retains access even after the build finishes
  3. Automated propagation: It uses stolen tokens to automatically create rogue code repositories, turning compromised accounts into new launchpads to spread the worm

Package Visibility Across the SDLC

The main takeaway is that open-source and third-party packages touch every single phase of the software development lifecycle (SDLC). Modern applications aren't built from scratch, they are assembled. Because open-source code makes up 80-90% of modern codebases, the attack surface expands to developer laptops, CI/CD pipelines and cloud infrastructure.

Ten years ago, a project might have relied on a few dozen external libraries. Today, even a simple application pulls in thousands of indirect dependencies. Generating a software bill of materials (SBOM) at the end of a build is great for compliance, but an SBOM alone simply doesn't cut it anymore. An inventory list created at the finish line won't catch malware that was executed during the build process. To truly secure an environment, every single place a third-party package touches needs to be mapped.

The Endpoint Attack Surface

Developers today navigate an endless matrix of language-specific package managers. They routinely execute installations across a massive array of ecosystems — npm install, pip install, cargo build, go get, mvn install and many more — while simultaneously juggling 10–30 integrated development environment (IDE) extensions. Why are these attacks so effective? Because developer tools lack basic guardrails.

Think about your web browser. When you visit a website, the browser locks it in a safe container (a sandbox) so it can’t touch your computer. But setup scripts and code editor extensions don't have those walls. The second they run, they get the same permissions you have, giving malware total freedom to read your files, steal keys and run commands on your machine.

This massive, un-isolated weakness is exactly why registries and marketplaces have become prime targets, as seen in the recent GlassWorm campaign. Whether it’s a malicious script running silently during a routine dependency pull or a compromised IDE extension updating automatically in the background, an attacker instantly gains unrestricted execution rights on a developer's machine and access to valuable cloud knowledge.

CI/CD Pipelines

Build pipelines rely on numerous outside tools, plugins and helper scripts to assemble code. Attackers often target these build environments because they are packed with temporary passwords and cloud access keys. The compromise of Trivy highlights the potential attack surface of pipeline security tools. This means that only scanning app code is insufficient. A pipeline bill of materials (PBOM) is also needed, which is an inventory list of every single tool running inside your build system.

The Cloud Runtime

Finally, we can't forget the cloud. A standard application SBOM typically only lists the code libraries developers explicitly add to their software. However, cloud environments rely on containers, which require a broader approach. A container SBOM provides the full picture by tracking both the application code and the hidden operating system tools built into the container image such as basic security utilities and system libraries like OpenSSL. Standard application scans completely overlook these deeper system layers.

The danger of this weakness was starkly illustrated by the wave of OpenSSL zero-day vulnerabilities disclosed in early 2026. Because these critical flaws sit deep within the foundational cryptographic infrastructure of the container's operating system, the application-layer code will look perfectly clean and pass every repository scan, while the underlying cloud workload remains completely exposed to a remote takeover.

Given this extensive attack surface, relying on static, point-in-time gateway scans is an insufficient strategy. True supply chain resilience requires continuous visibility between local developer endpoints, automated pipelines and cloud runtime workloads. Correlating telemetry across all three domains is the only way to intercept malicious behaviors and halt a compromise before it propagates downstream.

Tips for Hardening Pipelines

Defending against automated supply chain attacks requires shifting from reactive code scanning to strict execution control across the entire build path. Because tools, IDE extensions and package managers run with high privileges, organizations must lock down the developer environment by disabling lifecycle install scripts (--ignore-scripts), enforcing package cooldown periods, restricting CI/CD egress traffic, using ephemeral CI/CD servers and pinning dependencies down to exact commit SHAs.

Beyond environment hardening, neutralizing autonomous malware like Shai-Hulud means eliminating the long-lived credentials that fuel them. By transitioning to brief OIDC authentication and enforcing end-to-end cryptographic provenance, teams can establish an unbroken chain of trust. This trust extends from signed commits at the developer endpoint to signed artifacts and SBOMs in production, helping stop self-propagating worms in their tracks.