CloudIntel: A Daily Feed of Malware Actually Targeting Your Cloud Infrastructure

Hook

Generic threat intelligence feeds flag millions of malicious IPs daily, but how many are actually trying to exploit your S3 buckets or Azure storage accounts? CloudIntel answers that question with surgical precision.

Context

Traditional threat intelligence feeds treat the cloud as just another network perimeter. They'll tell you about botnet IPs, spam sources, and brute-force attacks, but they won't differentiate between an IP scanning random SSH ports and one specifically probing AWS metadata endpoints or attempting Azure IMDS exploitation. This creates noise for cloud security teams who need to prioritize threats that actually understand their infrastructure.

CloudIntel emerged to fill this gap by operating honeypots that mimic cloud services and capture attack patterns unique to AWS, Azure, and GCP environments. Instead of generic network threats, it focuses on adversaries who know the difference between EC2 and on-premises servers, who understand Azure Managed Identities, or who target GCP service accounts. The repository publishes daily IOC updates in dated folders, accompanied by malware samples and analysis notes that reveal what attackers are actually doing once they compromise cloud resources.

Technical Insight

CloudIntel operates as a two-tiered intelligence distribution system. The public GitHub repository serves as the free tier, publishing daily folders in DD-MM-YYYY format containing text files of malicious IP addresses observed attacking cloud infrastructure. The premium tier is a REST API built on Cloudflare Workers with R2 object storage, containing approximately 30 times more IOCs than the public listings.

The API architecture is elegantly simple. Cloudflare Workers provide edge compute to handle authentication and data retrieval, while R2 stores the comprehensive IOC dataset. This serverless approach means near-zero operational overhead and global distribution at Cloudflare's edge locations. Here's how you'd integrate the API into a Python-based SIEM ingestion script:

import requests
import json
from datetime import datetime

API_BASE = "https://cloudintel-api.workers.dev"
API_KEY = "your-api-key-here"  # Obtained via email registration

def fetch_daily_iocs(date_str=None):
    """
    Fetch IOCs for a specific date or today
    date_str format: DD-MM-YYYY
    """
    if not date_str:
        date_str = datetime.now().strftime("%d-%m-%Y")
    
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }
    
    response = requests.get(
        f"{API_BASE}/iocs/{date_str}",
        headers=headers,
        timeout=10
    )
    
    if response.status_code == 200:
        iocs = response.json()
        return iocs.get('malicious_ips', [])
    else:
        print(f"Error: {response.status_code} - {response.text}")
        return []

# Fetch today's IOCs and format for firewall rules
iocs = fetch_daily_iocs()
for ip in iocs:
    print(f"aws ec2 authorize-security-group-ingress --group-id sg-xxxxx "
          f"--protocol all --cidr {ip}/32 --port -1")

The repository structure reveals how CloudIntel organizes its intelligence. Each daily folder contains not just IP lists, but also malware binaries (when captured), PCAP files showing network behavior, and markdown analysis notes describing the attack chain. This context transforms raw IOCs into actionable intelligence. For example, an analysis note might reveal that a particular IP was deploying cryptominers that specifically targeted AWS EC2 metadata endpoints to extract IAM credentials.

What makes CloudIntel particularly valuable is its focus on cloud-specific attack patterns. The honeypot infrastructure appears designed to mimic exposed cloud services—misconfigured S3 buckets, publicly accessible Azure blobs, exposed Kubernetes dashboards. When attackers interact with these honeypots, they reveal their tooling and techniques. A malware sample might show scripts that enumerate AWS regions, attempt to pivot through VPC peering, or exfiltrate data through CloudFront distributions.

Integrating CloudIntel into your security stack requires treating it as a supplementary feed rather than gospel. The Cloudflare Workers API makes it trivial to poll for updates and merge the data with other threat feeds. You might combine CloudIntel IPs with GreyNoise data to filter out benign internet background noise, or cross-reference them with your CloudTrail logs to identify if any of these IPs have already touched your infrastructure. The daily update cadence means you can run scheduled jobs that pull new IOCs each morning and automatically update security group rules, WAF policies, or SIEM watchlists.

The REST API's 30x data multiplier compared to the public GitHub repository is significant. The free tier gives you a taste, but serious SOC operations will want API access to get the full picture. This tiered approach is smart from a sustainability perspective—it allows the project maintainer to potentially monetize API access while keeping baseline data public for the community.

Gotcha

CloudIntel's single-maintainer model is both its strength and weakness. One person can move fast, focus narrowly on cloud threats, and avoid committee-driven feature bloat. But this also means no redundancy, no guaranteed uptime, and no formal commitment to data quality or freshness. The repository's commit history might show gaps during holidays or personal emergencies. For organizations with compliance requirements, there's no SLA, no vendor contract, and limited recourse if the feed suddenly stops updating or contains false positives that block legitimate traffic.

The API authentication model deserves scrutiny. Requiring email registration to obtain API keys is reasonable, but the warning that demo credentials "may change without prior notice" is problematic for automated systems. If you're building CloudIntel into your security automation, you need monitoring to detect when API keys rotate unexpectedly. There's also limited documentation on rate limits, data retention policies, or the methodology behind IOC collection. You don't know the geographic distribution of honeypots, how long IPs remain in the dataset, or what validation occurs before an IP is labeled malicious. This opacity makes it difficult to tune false positive rates or understand coverage gaps. An IP might be flagged because it probed a honeypot once, or because it launched sustained attacks for weeks—the data doesn't distinguish between these scenarios.

Verdict

Use CloudIntel if you're running production workloads on AWS, Azure, or GCP and your existing threat feeds aren't giving you cloud-specific context. It's particularly valuable for security teams who've built custom SIEM integrations and can programmatically consume the API, cross-reference IOCs with other feeds, and implement human-in-the-loop validation before automated blocking. DevSecOps teams conducting threat hunting or forensic investigations will appreciate the malware samples and analysis notes that explain what attackers are actually doing in cloud environments. Skip CloudIntel if you need enterprise-grade reliability, formal SLAs, or compliance-ready audit trails for your threat intelligence sources. It's not suitable as your sole IOC feed, and organizations with limited security engineering resources may find the lack of documentation and potential for API key rotation too operationally risky. Treat this as a specialized supplement to commercial threat intelligence platforms, not a replacement for them.