Search: “ensemble”

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CVE-2026-84501: Apache ZooKeeper: Operational log forgery via newline injection in EnsembleAuthenticationProvider

Unauthenticated attackers can forge Apache ZooKeeper operational log lines via newline injection in crafted ensemble authentication requests.

CVE-2026-84501 (moderate severity) affects Apache ZooKeeper 3.9.0-3.9.5 and 3.8.0-3.8.6. An unauthenticated attacker can inject arbitrary fake log lines into the operational log by sending a crafted add_auth("ensemble", ...) request containing newline characters. The forged entries could mislead operators or corrupt log-based monitoring and forensics.

oss-securitySep 15, 18:28 UTC · 1d agoVulnerability CVE-2026-845011

When scanners miss the attack: how Cloudflare Client-Side Security protects storefronts

Cloudflare's Page Shield ML uncovered four malicious JavaScript campaigns on storefronts, including affiliate fraud and a remote-backdoor script, that VirusTotal and URLScan missed.

Cloudflare's Page Shield ML detected four client-side JavaScript operations (eight payloads) in live traffic on online storefronts, enabling affiliate commission hijacking, clickless affiliate theft via hidden iframes, user tracking with a remote-code backdoor, and cloaking of paid mobile visitors. Seven of the eight payloads were absent from VirusTotal and URLScan returned no malicious verdict for any, including a Lnkr-family payload indexed unclassified for roughly 2.5 years. Detection relies on a graph neural network over JavaScript syntax trees, an LLM second opinion on Workers AI, and a frontier-model ensemble voting across benign, magecart, other malware, and cryptomining labels.

Cloudflare BlogSep 16, 20:06 UTC · 16h agoMalware in the wild

A GAN-Based Framework for Robust DDoS Attack Detection

WGAN-GP-generated adversarial DDoS traffic augments training data, improving detection resilience against evasion attempts.

Researchers built a DDoS detection framework combining Random Forests, deep neural ensembles, and Transformer-based models trained on CICDDoS2019 with synthetic adversarial flows generated by a Wasserstein GAN with gradient penalty. Hybrid datasets of benign, malicious, and generated traffic taught models more generalizable decision boundaries. Experiments showed improved accuracy and resilience against unseen adversarial traffic, validated on real-world generated flows.

arXiv cs.CRSep 16, 08:04 UTC · 1d agoResearch