The Yo-Yo Attack: Bankrupting Cloud Infrastructure
A comprehensive guide to the Yo-Yo attack, an Economic Denial of Sustainability (EDoS) technique that targets auto-scaling mechanisms in cloud environments.
Feb 28, 2026Cybersecurity
Traditional security detection has long relied on logs: authentication events, firewall alerts, and application error messages. While logs remain essential, they are no longer sufficient for detecting modern threats that exploit distributed systems, APIs, and cloud-native architectures.
OpenTelemetry (OTel) changes this paradigm by providing end-to-end visibility across logs, metrics, and traces—allowing security teams to detect attacks that would otherwise remain invisible.
For compliance-driven organizations (ISO 27001, SOC 2, NIST), OpenTelemetry also creates verifiable evidence of control effectiveness, not just system activity.
Logs answer what happened, but often fail to answer:
An attacker may:
Result:
Logs look normal. The system is compromised.
OpenTelemetry provides security-relevant context that logs cannot capture alone:
| Signal | Security Value |
|---|---|
| Traces | Attack path reconstruction |
| Metrics | Behavioral anomaly detection |
| Logs | Evidence & forensics |
| Attributes | Identity, tenant, risk context |
The real power lies in correlation.
With OpenTelemetry traces, every request carries a trace ID across services.
API Gateway → Auth Service → Billing Service → Export ServiceIf an attacker exploits an over-privileged token:
from opentelemetry import trace
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("export_customer_data") as span:
span.set_attribute("security.user_id", user_id)
span.set_attribute("security.role", user_role)
span.set_attribute("security.data_classification", "PII")Security teams can now query:
Show all traces where
data_classification=PIIaccessed by non-admin roles
Logs detect events. Metrics detect behavior.
An attacker rotates IPs and credentials slowly.
Metric-based detection:
metric: auth.login.attempts
labels:
- user_id
- source_regionSecurity rule:
IF login_attempts(user_id) > baseline * 5
AND error_rate < 1%
THEN flag anomalyThis pattern is invisible in logs but obvious in metrics.
OpenTelemetry allows attaching security context to telemetry:
span.setAttribute("security.tenant_id", tenantId);
span.setAttribute("security.auth_method", "oauth");
span.setAttribute("security.trust_level", "low");Now detections can be:
This is critical for multi-tenant SaaS platforms like ISO compliance portals.
A compromised library may:
Correlated detection:
OpenTelemetry makes this visible without signature-based detection.
OTel does not replace SIEM—it upgrades it.
Pipeline example:
Application → OpenTelemetry SDK
→ OTel Collector
→ SIEM / XDREnriched events now include:
This dramatically reduces:
OpenTelemetry supports multiple ISO 27001 objectives:
| Control Area | Benefit |
|---|---|
| A.8 Logging | Unified, consistent telemetry |
| A.12 Monitoring | Continuous behavioral monitoring |
| A.16 Incident Response | Faster root cause analysis |
| A.18 Evidence | Verifiable audit trails |
Auditors increasingly ask:
“How do you detect misuse without errors?”
OTel is a strong answer.
Attackers no longer “break in.” They blend in.
OpenTelemetry shifts detection from:
For modern security teams, observability is no longer optional—it is foundational.
If your security detection still relies primarily on logs, you are:
OpenTelemetry provides the missing visibility layer—turning runtime behavior into actionable security intelligence.
Logs tell you something happened. OpenTelemetry tells you why it matters.
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