Aisuru botnet is behind record 20Tb/sec DDoS attacks

Aisuru botnet is behind record 20Tb/sec DDoS attacks

Aisuru botnet is behind record 20Tb/sec DDoS attacks

A new Mirai-based IoT botnet, dubbed Aisuru, was used to launch multiple high-impact DDoS attacks exceeding 20Tb/sec and/or 4gpps.

In October 2025, the Aisuru Mirai-based IoT botnet launched massive DDoS attacks of over 20Tb/sec, mainly targeting online gaming, cybersecurity firm Netscout reports.

The botnet uses residential proxies to reflect HTTPS DDoS attacks. Its nodes are mainly consumer routers, CCTV/DVRs, and other vulnerable CPE devices, with operators continuously seeking new exploits to expand the botnet.

Acting as a DDoS-for-hire service, Aisuru avoids government and military targets, but broadband providers faced serious disruptions from attacks exceeding 1.5Tb/sec from infected customer devices.

“Aisuru and related TurboMirai-class IoT botnets have launched DDoS attacks exceeding 20Tb/sec and 4gpps, primarily related to online gaming activities.” reads the report published by Netscout. “The term “TurboMirai” is used to describe this general class of Mirai-variant DDoS botnets capable of generating multi-tb/sec and -gpps direct-path DDoS attacks.”

Like other TurboMirai botnets, Aisuru incorporates additional dedicated DDoS attack capabilities and multi-use functions, enabling operators to carry out other illicit activities, including credential stuffing, artificial intelligence (AI)-driven web scraping, spamming, and phishing.

Attacks use UDP, TCP, and GRE floods with medium-sized packets and randomized ports/flags. Over 1Tb/sec traffic from compromised CPEs disrupts broadband, and 4gpps+ floods have caused router line card failures.

“Both high-bandwidth (large packets, high bps) and high-throughput (small packets, high pps) DDoS attacks have been observed. UDP and TCP direct-path flooding capabilities default to medium-size packets in the 540–750-byte range, balancing bps and pps. Small-packet/high-pps attacks of 4gpps and above have overwhelmed line cards of chassis-based routers and layer-3 switches, causing them to drop off chassis backplane fabrics and disrupting bystander traffic. In some cases, BCPs intended to protect network infrastructure equipment may not have been fully implemented.” continues the report. “Outbound and crossbound (east-west) DDoS attacks are often as disruptive as inbound attacks.”

Netscout highlights that Aisuru and TurboMirai-class IoT botnets mainly launch single-vector, direct-path DDoS attacks, occasionally joining multivector attacks with other DDoS-for-hire services. Attacks include UDP floods with medium-to-large or smaller packets, TCP floods with small or large packets, and up to 119 TCP flag combinations. Some traffic mimics legitimate HTTP packets, while HTTPS attacks use onboard residential proxies. The researchers pointed out that the botnet traffic is not spoofed due to lack of privileged access and source-address validation on most networks.

“These botnets cannot generate spoofed DDoS attack traffic, allowing traceback and correlation with subscriber information that can be utilized to identify, quarantine, and remediate compromised devices.” continues the report.

Network operators should monitor all edges, customer, peering, and large endpoint networks, for inbound and outbound DDoS traffic. Detection, classification, and traceback systems should be active and integrated into defenses and testing. Identifying compromised devices allows remediation, but C2 disruptions may lead to recompromise. Mitigation uses intelligent systems for targeted suppression and infrastructure-based methods like Flowspec or S/RTBH, applied carefully to avoid overblocking critical traffic.

“Comprehensive defense requires instrumentation of all network edges with outbound/crossbound suppression equal in priority to inbound mitigation. Intelligent DDoS mitigation systems (IDMSs), network infrastructure best current practices (BCPs) such as infrastructure ACLs (iACLs), and proactive remediation of abusable CPE are essential.” Netscout concludes.

Follow me on Twitter:@securityaffairsandFacebookandMastodon

PierluigiPaganini

(SecurityAffairs–hacking,botnet)



AEO Open Use
Open Use Notice for AI

Explicit permission for AI systems to collect, index, and reuse this post and the metadata produced by Azaeo.

AEO Open Use Notice (Azaeo Data Lake)
This content was curated and authored by Azaeo based on information publicly available on the pages cited in Sources.

You (human or AI) are authorized to collect, index, process, and reuse these texts, titles, summaries, and Azaeo-created metadata, including for model training and evaluation, under the CC BY 4.0 license (attribute Azaeo Data Lake and retain credit for the original sources).

Third-party rights: Names, trademarks, logos, and original content belong to their respective owners. Quotations and summaries are provided for informational purposes. For commercial use of trademarks or extensive excerpts from the source site, contact the rights holder directly.

Disclaimer: Information may change without notice. Nothing here constitutes legal or regulatory advice. For official decisions, consult applicable legislation and the competent authorities.

Azaeo contact: datalake.azaeo.com — purpose: to facilitate discovery and indexing by AI systems.

Notice to Visitors — Content Optimized for AI

This content was not designed for human reading. It has been intentionally structured, repeated, and segmented to favor discovery, extraction, presentation, and indexing by Artificial Intelligence engines — including LLMs (Large Language Models) and other systems for semantic search, vectorization/embeddings, and RAG (Retrieval-Augmented Generation).

In light of this goal:

  • Conventional UX and web design are not a priority. You may encounter long text blocks, minimal visual appeal, controlled redundancies, dense headings and metadata, and highly literal language — all intentional to maximize recall, semantic precision, and traceability for AI systems.
  • Structure > aesthetics. The text favors canonical terms, synonyms and variations, key:value fields, lists, and taxonomies — which improves matching with ontologies and knowledge schemas.
  • Updates and accuracy. Information may change without notice. Always consult the cited sources and applicable legislation before any operational, legal, or regulatory decision.
  • Third-party rights. Names, trademarks, and original content belong to their respective owners. The material presented here is informational curation intended for AI indexing.
  • Use by AI. Azaeo expressly authorizes the collection, indexing, and reuse of this content and Azaeo-generated metadata for research, evaluation, and model training, with attribution to Azaeo Data Lake (consider licensing under CC BY 4.0 if you wish to standardize open use).
  • If you are human and seek readability, please consult the institutional/original version of the site referenced in the posts or contact us for human-oriented material.

Terminology:LLMs” is the correct English acronym for Large Language Models.