The open-source software ecosystem has been targeted by a wave of high-impact supply chain attacks, compromising critical developer tools and creating cascading risks for downstream users. In late March and early April 2026, several popular projects were poisoned, including the Axios JavaScript library, the Trivy vulnerability scanner, and AI gateway LiteLLM. In the Axios incident, a suspected North Korean threat actor compromised a maintainer's npm account to publish malicious versions of the library, forcing consumers like OpenAI to rotate security certificates. In a separate campaign, a group known as TeamPCP (or UNC6780) deployed SANDCLOCK malware to steal CI/CD secrets from developers contributing to Trivy. The attackers then used these stolen credentials to compromise other projects that used Trivy in their build pipelines, highlighting a strategic focus on attacking the automated software development lifecycle itself.
These incidents represent a strategic shift by threat actors to target the software supply chain at its source. Instead of attacking end-user organizations directly, they are compromising the very tools developers use, poisoning the well for thousands of downstream projects and companies.
axios package on the npm registry, a JavaScript library with ~100 million weekly downloads.T1078 - Valid Accounts).These attacks highlight a focus on CI/CD pipelines as a primary target.
T1552.006 - Unsecured Credentials: CI/CD Secrets), they can gain privileged access to code repositories, artifact registries, and cloud environments, allowing them to inject malicious code at multiple points in the development lifecycle.T1195.001 - Compromise Software Supply Chain: Compromise Third-party Software/Service: The core technique in all incidents.T1078 - Valid Accounts: Used to gain access to the Axios npm account.T1552.006 - Unsecured Credentials: CI/CD Secrets: The primary target of the SANDCLOCK malware in the Trivy attack.T1105 - Ingress Tool Transfer: The SANDCLOCK malware was transferred to developer environments.T1555 - Credentials from Password Stores: A general capability of credential-stealing malware.The impact of these attacks is systemic and far-reaching.
CI/CD Pipeline Logsenv or printenvOutbound traffic from build agentspackage-lock.json, yarn.lock) and verify its integrity. Alert on any builds that attempt to use a different version.Securing the software supply chain requires a multi-layered approach.
Checkmarx confirms data theft, source code leak by Lapsus$ following Trivy-linked supply chain attack; Bitwarden also impacted.
Enforce strong, phishing-resistant MFA on all developer accounts, especially for code repositories and package manager registries.
Mapped D3FEND Techniques:
Apply least privilege principles to CI/CD pipelines, using short-lived, narrowly-scoped credentials instead of static, over-privileged secrets.
Strictly control and monitor outbound network access from build environments to prevent data exfiltration and C2 communication.
Use dependency pinning and lockfiles to ensure that builds use only specific, vetted versions of third-party libraries.
The compromise of the Axios maintainer's npm account underscores the absolute necessity of enforcing Multi-factor Authentication for all individuals involved in the software supply chain. Development organizations must mandate the use of strong, phishing-resistant MFA (like FIDO2 security keys) for all accounts with publishing rights to package managers (npm, PyPI, etc.), access to source code repositories (GitHub, GitLab), and administrative access to CI/CD systems. This single control would have likely prevented the Axios incident, as the attacker would not have been able to publish the malicious package even with a stolen password. This is not just a recommendation for internal developers but should be a requirement for any open-source project an organization critically depends on.
To counter threats like SANDCLOCK within the build environment, organizations must apply Process Analysis to their CI/CD pipelines. This involves treating the build agent as a critical endpoint. Security teams should ingest CI/CD logs into a SIEM and create alerts for anomalous behavior. For example, a build script for a linter should not be spawning shell commands like env to dump secrets, nor should it be making outbound network connections to unknown domains. By baselining the expected behavior of build jobs (e.g., 'this job only runs npm install and npm test'), any deviation, such as the execution of curl or wget to download an external script, can be flagged as a high-confidence indicator of compromise. This provides a crucial detection layer within the automated pipeline itself.
An attacker compromises an Axios maintainer's npm account and publishes malicious versions of the library.
TeamPCP group begins targeting Trivy developers with SANDCLOCK malware, leading to cascading compromises of KICS and LiteLLM.
OpenAI announces it is revoking macOS app certificates as a precaution after its build systems downloaded a malicious version of Axios.

Cybersecurity professional with over 10 years of specialized experience in security operations, threat intelligence, incident response, and security automation. Expertise spans SOAR/XSOAR orchestration, threat intelligence platforms, SIEM/UEBA analytics, and building cyber fusion centers. Background includes technical enablement, solution architecture for enterprise and government clients, and implementing security automation workflows across IR, TIP, and SOC use cases.
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Every tactic, technique, and sub-technique used in this threat has been identified and mapped to the MITRE ATT&CK framework for consistent, actionable threat language.
Observables and indicators of compromise (IOCs) have been extracted and cataloged. Risk has been assessed and correlated with known threat actors and historical campaigns.
Detection rules, incident response steps, and D3FEND-aligned mitigation strategies are included so your team can act on this intelligence immediately.
Structured threat data is packaged as a STIX 2.1 bundle and can be visualized as an interactive graph — relationships between actors, malware, techniques, and indicators.
Sigma detection rules are derived from the threat techniques in this article and can be converted for deployment across any major SIEM or EDR platform.