The Worlds Largest DDos Attack 3.8 Tbps

Cloudflare’s DDoS defenses mitigated a record-setting Layer 3/4 attack peaking at 3.8 terabits per second and doing so with fully autonomous, server-level detection and mitigation. The campaign spanned roughly a month starting in early September, during which Cloudflare’s systems absorbed more than 100 hyper-volumetric Layer 3/4 DDoS events—many topping 2 billion packets per second—while targeting the same customer across multiple attack waves. The scale mattered because these attacks aim at bandwidth saturation and CPU/resource exhaustion, which can knock out both unprotected internet properties and services protected by providers lacking sufficient network capacity.

The attack traffic was predominantly UDP sent to fixed ports, with sources distributed globally. Cloudflare observed traffic coming from multiple compromised device types, including Microtik devices, DVRs, and web-facing systems, and also flagged large volumes originating from compromised ASUS home routers tied to a recently discovered CVE-9.8 vulnerability. The write-up describes how Layer 3/4 DDoS works in practice: high packet rates force victims to spend CPU cycles parsing and routing packets before they even reach applications, while volumetric floods overwhelm the “pipe” of available ingress bandwidth. In both cases, attackers rely on botnets—often built from IoT and other internet-connected devices—to generate enough traffic to exceed what the target can handle.

Cloudflare’s mitigation approach centers on anycast and a software-defined, distributed defense stack. Anycast lets a single IP address be served by multiple geographically dispersed network nodes, so botnet traffic lands across the network rather than concentrating at one choke point. From there, Cloudflare uses an in-line packet processing pipeline: an XDP-based sampling layer feeds suspicious traffic attributes into a denial-of-service daemon (labeled “DSY” in the transcript). DSY generates “dynamic fingerprints” from observed packet patterns, selects the most effective mitigation rules using streaming algorithms, and then pushes those rules into kernel-level packet filtering.

The actual dropping happens at the network interface card level using an extended Berkeley Packet Filter (eBPF) program, designed to discard bad packets with minimal CPU overhead. The system runs across servers and data centers without out-of-path scrubbing centers, and it shares mitigation instructions via gossip/multicast both locally and globally so that localized or widespread attacks can be handled quickly. Cloudflare also pairs the dynamic fingerprinting engine with additional protections for TCP and DNS-based DDoS, plus traffic profiling and machine learning classification for anomaly detection.

Beyond the technical claims, the transcript includes a broader critique of IoT—arguing that cheap, internet-connected devices (from routers to webcams and appliances) expand the pool of compromised endpoints that power botnets. The practical takeaway is that the largest DDoS campaigns increasingly combine massive packet rates, UDP floods, and widely distributed compromised devices, while mitigation increasingly depends on distributed network capacity and automated, in-line filtering rather than manual or centralized response.