Detect Serverless in Splunk
Adversaries may compromise serverless cloud infrastructure, such as Cloudflare Workers, AWS Lambda functions, or Google Apps Scripts, to proxy command-and-control (C2) communications between implants on victim systems and adversary-controlled backend servers. Because traffic destined for compromised serverless functions originates from subdomains of trusted cloud providers (e.g., *.workers.dev, *.execute-api.amazonaws.com, script.google.com), network-layer defenses relying on domain or IP reputation are largely ineffective. Detection pivots to behavioral analysis of victim-side telemetry: identifying processes on endpoints communicating with serverless platforms in patterns consistent with C2 beaconing (periodic connections, low-variance timing, small symmetric payloads), correlating process context with destination domains, and monitoring cloud audit logs for unauthorized modifications to serverless functions within environments the defender controls.
MITRE ATT&CK
- Tactic
- Resource Development
- Technique
- T1584 Compromise Infrastructure
- Sub-technique
- T1584.007 Serverless
- Canonical reference
- https://attack.mitre.org/techniques/T1584/007/
SPL Detection Query
index=proxy OR index=network sourcetype=proxy OR sourcetype="stream:http" OR sourcetype="pan:traffic" OR sourcetype="cisco:asa"
| eval dest_domain=if(isnotnull(cs_host), cs_host, if(isnotnull(url), replace(url, "https?://([^/]+).*", "\1"), null()))
| where match(dest_domain, "(workers\.dev|cloudflareworkers\.com|execute-api\.[a-z0-9-]+\.amazonaws\.com|lambda-url\.[a-z0-9-]+\.amazonaws\.com|script\.google\.com|cloudfunctions\.net|\.run\.app|azurewebsites\.net|pages\.dev|netlify\.app|vercel\.app|supabase\.co|deno\.dev)")
| eval hour_bucket=strftime(_time, "%Y-%m-%d %H")
| eval bytes_total=if(isnotnull(sc_bytes), tonumber(sc_bytes) + tonumber(cs_bytes), 0)
| stats
count as connection_count,
dc(hour_bucket) as hourly_buckets,
sum(sc_bytes) as total_bytes_received,
sum(cs_bytes) as total_bytes_sent,
earliest(_time) as first_seen,
latest(_time) as last_seen,
values(dest_domain) as destinations
by src_ip, cs_username, c_ip
| where connection_count >= 12 AND hourly_buckets >= 3
| eval avg_connections_per_hour=round(connection_count / hourly_buckets, 1)
| eval avg_bytes_sent_per_conn=round(total_bytes_sent / connection_count, 0)
| eval avg_bytes_recv_per_conn=round(total_bytes_received / connection_count, 0)
| eval beaconing_likely=if(avg_bytes_sent_per_conn < 4096 AND avg_bytes_recv_per_conn < 16384 AND avg_connections_per_hour >= 2, 1, 0)
| where beaconing_likely=1
| eval first_seen=strftime(first_seen, "%Y-%m-%d %H:%M:%S")
| eval last_seen=strftime(last_seen, "%Y-%m-%d %H:%M:%S")
| table first_seen, last_seen, src_ip, cs_username, destinations, connection_count, hourly_buckets, avg_connections_per_hour, avg_bytes_sent_per_conn, avg_bytes_recv_per_conn, beaconing_likely
| sort - connection_countDetects C2 beaconing through compromised serverless infrastructure by analyzing proxy and network flow logs. Matches destination domains against a comprehensive list of serverless hosting platforms and computes per-source-IP connection statistics over a rolling window. Flags sources with connections spread across multiple hourly buckets (indicating sustained, recurring activity rather than a single session) combined with low per-connection byte counts characteristic of automated beaconing protocols. Works with web proxy logs (Blue Coat, Squid, Zscaler), Palo Alto NGFW, and Cisco ASA logs when normalized to standard proxy sourcetypes.
Data Sources
Required Sourcetypes
False Positives & Tuning
- Legitimate SaaS applications with serverless backends making regular telemetry or heartbeat calls
- Developer workstations actively using serverless functions for legitimate application code
- Browser-based applications using edge computing platforms (Cloudflare Workers, Vercel) for content delivery
- IT automation and CI/CD tools communicating with cloud-hosted serverless orchestration endpoints
- Security monitoring tools with cloud-based processing backends
Other platforms for T1584.007
Testing Methodology
Validate this detection against 4 adversary techniques from Atomic Red Team. Each test below lists the behaviour to exercise and the telemetry you should expect to see. Executable commands and cleanup steps are available with Pro.
- Test 1Simulate C2 Beacon to Cloudflare Workers Domain
Expected signal: DeviceNetworkEvents: 15 ConnectionSuccess events from PowerShell.exe to httpbin.org, spaced ~30 seconds apart. Sysmon Event ID 3: Network connections with consistent DestinationPort=443 and DestinationHostname=httpbin.org. Inter-arrival time analysis will show MeanInterval ~30 seconds with low standard deviation (JitterCoefficient < 0.05).
- Test 2Non-Browser Process Connecting to AWS Lambda URL
Expected signal: Sysmon Event ID 3: Network connections with Image=C:\Windows\System32\WindowsPowerShell\v1.0\powershell.exe making POST requests to httpbin.org:443. DeviceNetworkEvents: InitiatingProcessFileName=powershell.exe, connection count=5 with small SentBytes values. Sysmon Event ID 22: DNS query for httpbin.org from powershell.exe.
- Test 3Enumerate and Test Unauthorized AWS Lambda Function (Controlled Lab)
Expected signal: AWS CloudTrail: ListFunctions and InvokeFunction API calls logged in CloudTrail with sourceIPAddress of the calling machine, userAgent=aws-cli, and requestParameters including the function name. If forwarded to a SIEM via CloudWatch/Kinesis, these appear in your cloud audit log index. EventName=InvokeFunction with errorCode absent indicates successful invocation.
- Test 4Deploy Test Cloudflare Worker via wrangler CLI (Simulates Infrastructure Compromise Foothold)
Expected signal: Cloudflare Audit Log: Worker script deployment event with actor email, source IP, and timestamp. If Cloudflare Logpush is configured to forward audit logs to your SIEM, the event appears as audit_log.action.type=deploy with resources referencing the worker name. Process telemetry: node.exe or cmd.exe spawning wrangler commands visible in Sysmon Event ID 1.
References (9)
- https://attack.mitre.org/techniques/T1584/007/
- https://www.bleepingcomputer.com/news/security/blackwater-malware-abuses-cloudflare-workers-for-c2-communication/
- https://blog.xpnsec.com/aws-lambda-redirector/
- https://awakesecurity.com/blog/threat-hunting-series-detecting-command-control-in-the-cloud/
- https://www.bleepingcomputer.com/news/security/hackers-abuse-google-apps-script-to-steal-credit-cards-bypass-csp/
- https://docs.aws.amazon.com/lambda/latest/dg/logging-using-cloudtrail.html
- https://developers.cloudflare.com/workers/observability/logging/
- https://cloud.google.com/logging/docs/audit/understanding-audit-logs
- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1584.007/T1584.007.md
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