Socket Selected for OpenAI's Cybersecurity Grant Program
Socket has been selected for OpenAI's Cybersecurity Grant Program, gaining API credits and access to frontier models via the Trusted Access for Cyber framework. This partnership enhances Socket's ability to detect malicious packages in open-source registries like npm and PyPI in near real-time, countering the increasing speed and automation of supply chain attacks.
Key Takeaways
- Socket received an OpenAI Cybersecurity Grant to enhance its malicious package detection pipeline.
- OpenAI launched 'Trusted Access for Cyber,' an identity-based framework for defensive acceleration.
- Socket utilizes AI models to analyze npm, PyPI, and other registries in near real-time to detect supply chain attacks.
- AI-assisted analysis enabled Socket to detect the malicious package used in the Axios compromise within six minutes.
Affected Systems
- npm
- PyPI
- Open Source Software
Attack Chain
Threat actors publish malicious packages to open-source registries such as npm and PyPI, often utilizing high-volume publishing, AI-assisted malware authoring, and credential theft targeting maintainers. Downstream projects inadvertently pull these compromised dependencies, leading to supply chain compromises. Defenders utilize AI-assisted analysis to evaluate code, behavior, metadata, and publishing patterns to flag and block these packages before widespread impact.
Detection Availability
- YARA Rules: No
- Sigma Rules: No
- Snort/Suricata Rules: No
- KQL Queries: No
- Splunk SPL Queries: No
- EQL Queries: No
- Other Detection Logic: No
No specific detection rules or queries are provided in the article.
Detection Engineering Assessment
EDR Visibility: None — The article discusses supply chain package analysis prior to endpoint execution, which is outside the scope of traditional EDR. Network Visibility: None — No specific network indicators or traffic patterns are discussed in the text. Detection Difficulty: Hard — Detecting malicious packages requires analyzing code behavior, metadata, and publishing patterns in near real-time before developers pull the dependencies.
Required Log Sources
- Package registry logs
- CI/CD pipeline logs
Hunting Hypotheses
| Hypothesis | Telemetry | ATT&CK Stage | FP Risk |
|---|---|---|---|
| Monitor CI/CD pipelines and developer workstations for the installation of newly published, unverified packages from public registries like npm or PyPI. | Process execution logs, Package manager logs (npm, pip) | Execution | High |
Control Gaps
- Lack of real-time dependency scanning in CI/CD pipelines
Key Behavioral Indicators
- Anomalous package publishing patterns
- Unexpected code behavior in newly updated dependencies
Recommendations
Immediate Mitigation
- Implement automated dependency scanning in CI/CD pipelines to catch malicious packages early.
Infrastructure Hardening
- Restrict direct access to public package registries and use internal, vetted artifact repositories.
User Protection
- Enforce MFA for all package maintainer accounts to prevent credential theft.
Security Awareness
- Educate developers on the risks of typosquatting and blind dependency updates in open-source software.
MITRE ATT&CK Mapping
- T1195.002 - Supply Chain Compromise: Compromise Software Supply Chain
