CloudIntel: Hunting Cloud-Specific Threats With a Daily IOC Feed for AWS, Azure, and GCP
Hook
Traditional threat intelligence feeds miss a critical blind spot: attacks that only succeed in cloud environments. CloudIntel fills that gap by tracking threats that specifically target public cloud infrastructure—a dataset approximately 30 times larger than what’s publicly visible on GitHub.
Context
Most threat intelligence platforms emerged during the on-premise era, optimizing for corporate networks, firewalls, and endpoint protection. But cloud infrastructure operates differently, presenting unique attack surfaces that don’t appear in legacy IOC feeds focused on traditional infrastructure.
CloudIntel addresses this gap by collecting daily indicators of compromise (malicious IPs, malware samples, and analysis reports) and making them available through both a public GitHub repository and a more comprehensive REST API. The project, maintained by security researcher Himanshu Anand, provides the full dataset through an API—reportedly about 30 times larger than the public GitHub subset. For security teams defending cloud workloads, this specialized intelligence provides context focused specifically on cloud environments rather than generic threat data.
Technical Insight
CloudIntel’s delivery architecture leverages Cloudflare Workers with R2 object storage (implementation credited to Michel Bamps), keeping infrastructure costs minimal while maintaining global availability.
The API exposes a single endpoint for retrieving daily IOC feeds. Authentication requires two headers—an API key and email address—and date-based queries return JSON payloads of malicious IPs observed on that specific day:
curl -X GET \
'https://api.cloudintel.info/v1/maliciousip?date=12-25-2023' \
-H 'x-api-key: key{democloudintel}' \
-H 'x-email: democloudintel@himanshuanand.com'The date format follows MM-DD-YYYY convention, which differs from ISO 8601 standards but matches the repository’s folder structure. Each day’s data lives in a corresponding GitHub directory (DD-MM-YYYY), creating a browsable archive of historical threats. This dual-format approach—API for automation, GitHub for manual investigation—serves different operational needs.
What makes CloudIntel valuable is its cloud-specific focus. The repository includes malware samples and analysis reports, providing context beyond simple IP addresses. This specificity helps security teams distinguish between generic internet scanning and threats relevant to cloud infrastructure.
The API’s simplicity is both a strength and a limitation. The README doesn’t document response schemas, pagination, or filtering capabilities. The serverless backend choice (Cloudflare Workers) suggests the system prioritizes availability and static data delivery, which makes sense for a feed that updates once daily.
One notable implementation detail: the public GitHub repository contains only a subset of the full dataset. The API provides access to the complete data (stated as approximately 30 times more IOCs), creating a tiered access model where casual users can browse samples on GitHub while operations teams request API access for comprehensive data.
Gotcha
CloudIntel’s biggest friction point is access control. Unlike modern SaaS threat intelligence platforms with instant API key generation, you must email the maintainer or submit a Google Form to request credentials. The demo key in the README explicitly states it ‘can change without any notice,’ making it unsuitable for production automation. For teams wanting to integrate this feed into continuous monitoring pipelines, this manual approval process creates operational risk.
Documentation gaps compound this problem. The README references an IOC_CONSUMPTION.md guide and CONTRIBUTING.md file for detailed usage and contribution guidelines, though their current state in the repository should be verified. There’s no information about API rate limits, data retention policies, or detailed response schemas beyond the basic curl example. The Wiki link exists but specific technical documentation depth should be confirmed before integration.
Another consideration: the README doesn’t discuss confidence scoring, false positive rates, or threat classification methodologies. Cloud infrastructure receives massive amounts of automated scanning from research organizations and security companies. Without documented validation guidance, security teams must independently assess IOCs before implementing blocking rules to avoid potential impacts on legitimate traffic.
Verdict
Use CloudIntel if you’re defending cloud infrastructure and need specialized threat intelligence beyond generic IP reputation feeds. It’s particularly valuable for security operations teams building custom threat intelligence platforms, where you can combine CloudIntel’s cloud-focused IOCs with other feeds to create enriched context. The daily update cadence (every 24 hours as stated in the README) works well for batch processing workflows. Also consider it if you’re a security researcher studying cloud attack patterns—the repository provides malware samples and analysis reports beyond just IP addresses.
Skip CloudIntel if you need immediate API access without manual approval, enterprise SLAs, or real-time threat feeds with sub-hour latency. The single-maintainer model (project explicitly states it’s ‘a personal project, not associated with any employer’) makes it less suitable as a primary security control. Also skip if you require extensive documentation or guaranteed API stability—the demo credentials can change without notice, and some referenced documentation guides should be verified before production use. This tool works best as a supplementary feed enriching existing security infrastructure, not as your sole cloud threat intelligence source.
