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September 23, 2025

ShadowV2: An emerging DDoS for hire botnet

Darktrace exposed a cybercrime-as-a-service campaign using Python and Go-based malware, Docker containerization, and a full operator UI. With DDoS-as-a-service features, modular APIs, and advanced evasion, this platform highlights the need for defenders to monitor cloud workloads, container orchestration, and API activity to counter evolving threats.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Nate Bill
Threat Researcher
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23
Sep 2025

Introduction: ShadowV2 DDoS

Darktrace's latest investigation uncovered a novel campaign that blends traditional malware with modern devops technology.

At the center of this campaign is a Python-based command-and-control (C2) framework hosted on GitHub CodeSpaces. This campaign also utilizes a Python based spreader with a multi-stage Docker deployment as the initial access vector.

The campaign further makes use of a Go-based Remote Access Trojan (RAT) that implements a RESTful registration and polling mechanism, enabling command execution and communication with its operators.

ShadowV2 attack techniques

What sets this campaign apart is the sophistication of its attack toolkit.

The threat actors employ advanced methods such as HTTP/2 rapid reset, a Cloudflare under attack mode (UAM) bypass, and large-scale HTTP floods, demonstrating a capability to combine distributed denial-of-service (DDoS) techniques with targeted exploitation.

With the inclusion of an OpenAPI specification, implemented with FastAPI and Pydantic and a fully developed login panel and operator interface, the infrastructure seems to resemble a “DDoS-as-a-service” platform rather than a traditional botnet, showing the extent to which modern malware increasingly mirrors legitimate cloud-native applications in both design and usability.

Analysis of a ShadowV2 attack

Initial access

The initial compromise originates from a Python script hosted on GitHub CodeSpaces. This can be inferred from the observed headers:

User-Agent: docker-sdk-python/7.1.0

X-Meta-Source-Client: github/codespaces

The user agent shows that the attacker is using the Python Docker SDK, a library for Python programs that allows them to interact with Docker to create containers. The X-Meta-Source-Client appears to have been injected by GitHub into the request to allow for attribution, although there is no documentation online about this header.

The IP the connections originate from is 23.97.62[.]139, which is a Microsoft IP based in Singapore. This aligns with expectations as GitHub is owned by Microsoft.

This campaign targets exposed Docker daemons, specifically those running on AWS EC2. Darktrace runs a number of honeypots across multiple cloud providers and has only observed attacks against honeypots running on AWS EC2. By default, Docker is not accessible to the Internet, however, can be configured to allow external access. This can be useful for managing complex deployments where remote access to the Docker API is needed.

Typically, most campaigns targeting Docker will either take an existing image from Docker Hub and deploy their tools within it, or upload their own pre-prepared image to deploy. This campaign works slightly differently; it first spawns a generic “setup” container and installs a number of tools within it. This container is then imaged and deployed as a live container with the malware arguments passed in via environmental variables.

Attacker creates a blank container from an Ubuntu image.
Figure 1: Attacker creates a blank container from an Ubuntu image.
Attacker sets up their tools for the attack.
Figure 2: Attacker sets up their tools for the attack.
 Attacker deploys a new container using the image from the setup container.
Figure 3: Attacker deploys a new container using the image from the setup container.

It is unclear why the attackers chose this approach - one possibility is that the actor is attempting to avoid inadvertently leaving forensic artifacts by performing the build on the victim machine, rather than building it themselves and uploading it.

Malware analysis

The Docker container acts as a wrapper around a single binary, dropped in /app/deployment. This is an ELF binary written in Go, a popular choice for modern malware. Helpfully, the binary is unstripped, making analysis significantly easier.

The current version of the malware has not been reported by OSINT providers such as VirusTotal. Using the domain name from the MASTER_ADDR variable and other IoCs, we were able to locate two older versions of the malware that were submitted to VirusTotal on the June 25 and July 30 respectively [1] [2].  Neither of these had any detections and were only submitted once each using the web portal from the US and Canada respectively. Darktrace first observed the attack against its honeypot on June 24, so it could be a victim of this campaign submitting the malware to VirusTotal. Due to the proximity of the start of the attacks, it could also be the attacker testing for detections, however it is not possible to know for certain.

The malware begins by phoning home, using the MASTER_ADDR and VPS_NAME identifiers passed in from the Docker run environmental variables. In addition, the malware derives a unique VPS_ID, which is the VPS_NAME concatenated with the current unix timestamp. The VPS_ID is used for all communications with the C2 server as the identifier for the specific implant. If the malware is restarted, or the victim is re-infected, the C2 server will inform the implant of its original VPS_ID to ensure continuity.

Snippet that performs the registration by sending a POST request to the C2 API with a JSON structure.
Figure 4: Snippet that performs the registration by sending a POST request to the C2 API with a JSON structure.

From there, the malware then spawns two main loops that will remain active for the lifetime of the implant. Every second, it sends a heartbeat to the C2 by sending the VPS_ID to hxxps://shadow.aurozacloud[.]xyz/api/vps/heartbeat via POST request. Every 5 seconds, it retrieves hxxps://shadow.aurozacloud[.]xyz/api/vps/poll/<VPS ID> via a GET request to poll for new commands.

The poll mechanism shadow v2
Figure 5: The poll mechanism.

At this stage, Darktrace security researchers wrote a custom client that ran on the server infected by the attacker that mimicked their implant. The goal was to intercept commands from the C2. Based on this, it was observed initiating an attack against chache08[.]werkecdn[.]me using a 120 thread HTTP2 rapid reset attack. This site appears to be hosted on an Amsterdam VPS provided by FDCServers, a server hosting company. It was not possible to identify what normally runs on this site, as it returns a 403 Forbidden error when visited.

Darktrace’s code analysis found that the returned commands contain the following fields:

  • Method (e.g. GET, POST)
  • A unique ID for the attack
  • A URL endpoint used to report attack statistics
  • The target URL & port
  • The duration of the attack
  • The number of threads to use
  • An optional proxy to send HTTP requests through

The malware then spins up several threads, each running a configurable number of HTTP clients using Valyala’s fasthttp library, an open source Go library for making high-performance HTTP requests. After this is complete, it uses these clients to perform an HTTP flood attack against the target.

A snippet showing the fasthttp client creation loop, as well as a function to report the worker count back to the C2.
Figure 6: A snippet showing the fasthttp client creation loop, as well as a function to report the worker count back to the C2.

In addition, it also features several flags to enable different bypass mechanisms to augment the malware:

  • WordPress bypass (does not appear to be implemented - the flag is not used anywhere)
  • Random query strings appended to the URL
  • Spoofed forwarding headers with random IP addresses
  • Cloudflare under-attack-mode (UAM) bypass
  • HTTP2 rapid reset

The most interesting of these is the Cloudflare UAM bypass mechanism. When this is enabled, the malware will attempt to use a bundled ChromeDP binary to solve the Cloudflare JavaScript challenge that is presented to new visitors. If this succeeds, the clearance cookie obtained is then included in subsequent requests. This is unlikely to work in most cases as headless Chrome browsers are often flagged, and a regular CAPTCHA is instead served.

The UAM bypass success snippet.
Figure 7: The UAM bypass success snippet.

Additionally, the malware has a flag to enable an HTTP2 rapid reset attack mode instead of a regular HTTP flood. In HTTP2, a client can create thousands of requests within a single connection using multiplexing, allowing sites to load faster. The number of request streams per connection is capped however, so in a rapid reset attack many requests are made and then immediately cancelled to allow more requests to be created. This allows a single client to execute vastly more requests per second and use more server resources than it otherwise would, allowing for more effective denial-of-service (DoS) attacks.

 The HTTP2 rapid reset snippet from the main attack function.
Figure 8: The HTTP2 rapid reset snippet from the main attack function.

API/C2 analysis

As mentioned throughout the malware analysis section, the malware communicates with a C2 server using HTTP. The server is behind Cloudflare, which obscures its hosting location and prevents analysis. However, based on analysis of the spreader, it's likely running on GitHub CodeSpaces.

When sending a malformed request to the API, an error generated by the Pydantic library is returned:

{"detail":[{"type":"missing","loc":["body","vps_id"],"msg":"Field required","input":{"vps_name":"xxxxx"},"url":"https://errors.pydantic.dev/2.11/v/missing"}]}

This shows they are using Python for the API, which is the same language that the spreader is written in.

One of the larger frameworks that ships with Pydantic is FastAPI, which also ships with Swagger. The malware author left this publicly exposed, and Darktrace’s researchers were able to obtain a copy of their API documentation. The author appears to have noticed this however, as subsequent attempts to access it now returns a HTTP 404 Not Found error.

Swagger UI view based on the obtained OpenAPI spec.
Figure 9: Swagger UI view based on the obtained OpenAPI spec.

This is useful to have as it shows all the API endpoints, including the exact fields they take and return, along with comments on each endpoint written by the attacker themselves.

It is very likely a DDoS for hire platform (or at the very least, designed for multi-tenant use) based on the extensive user API, which features authentication, distinctions between privilege level (admin vs user), and limitations on what types of attack a user can execute. The screenshot below shows the admin-only user create endpoint, with the default limits.

The admin-only user create endpoint shadow v2
Figure 10: The admin-only user create endpoint.

The endpoint used to launch attacks can also be seen, which lines up with the options previously seen in the malware itself. Interestingly, this endpoint requires a list of zombie systems to launch the attack from. This is unusual as most DDoS for hire services will decide this internally or just launch the attack from every infected host (zombie). No endpoints that returned a list of zombies were found, however, it’s possible one exists as the return types are not documented for all the API endpoints.

The attack start endpoint shadow v2
Figure 11: The attack start endpoint.

There is also an endpoint to manage a blacklist of hosts that cannot be attacked. This could be to stop users from launching attacks against sites operated by the malware author, however it’s also possible the author could be attempting to sell protection to victims, which has been seen previously with other DDoS for hire services.

Blacklist endpoints shadow v2 DDoS
Figure 12: Blacklist endpoints.

Attempting to visit shadow[.]aurozacloud[.]xyz results in a seizure notice. It is most likely fake the same backend is still in use and all of the API endpoints continue to work. Appending /login to the end of the path instead brings up the login screen for the DDoS platform. It describes itself as an “advanced attack platform”, which highlights that it is almost certainly a DDoS for hire service. The UI is high quality, written in Tailwind, and even features animations.

The fake seizure notice.
Figure 13: The fake seizure notice.
The login UI at /login.
Figure 14: The login UI at /login.

Conclusion

By leveraging containerization, an extensive API, and with a full user interface, this campaign shows the continued development of cybercrime-as-a-service. The ability to deliver modular functionality through a Go-based RAT and expose a structured API for operator interaction highlights how sophisticated some threat actors are.

For defenders, the implications are significant. Effective defense requires deep visibility into containerized environments, continuous monitoring of cloud workloads, and behavioral analytics capable of identifying anomalous API usage and container orchestration patterns. The presence of a DDoS-as-a-service panel with full user functionality further emphasizes the need for defenders to think of these campaigns not as isolated tools but as evolving platforms.

Appendices

References

1. https://www.virustotal.com/gui/file/1b552d19a3083572bc433714dfbc2b75eb6930a644696dedd600f9bd755042f6

2. https://www.virustotal.com/gui/file/1f70c78c018175a3e4fa2b3822f1a3bd48a3b923d1fbdeaa5446960ca8133e9c

IoCs

Malware hashes (SHA256)

●      2462467c89b4a62619d0b2957b21876dc4871db41b5d5fe230aa7ad107504c99

●      1b552d19a3083572bc433714dfbc2b75eb6930a644696dedd600f9bd755042f6

●      1f70c78c018175a3e4fa2b3822f1a3bd48a3b923d1fbdeaa5446960ca8133e9c

C2 domain

●      shadow.aurozacloud[.]xyz

Spreader IPs

●      23.97.62[.]139

●      23.97.62[.]136

Yara rule

rule ShadowV2 {

meta:

author = "[email protected]"

description = "Detects ShadowV2 botnet implant"

strings:

$string1 = "shadow-go"

$string2 = "shadow.aurozacloud.xyz"

$string3 = "[SHADOW-NODE]"

$symbol1 = "main.registerWithMaster"

$symbol2 = "main.handleStartAttack"

$symbol3 = "attacker.bypassUAM"

$symbol4 = "attacker.performHTTP2RapidReset"

$code1 = { 48 8B 05 ?? ?? ?? ?? 48 8B 1D ?? ?? ?? ?? E8 ?? ?? ?? ?? 48 8D 0D ?? ?? ?? ?? 48 89 8C 24 38 01 00 00 48 89 84 24 40 01 00 00 48 8B 4C 24 40 48 BA 00 09 6E 88 F1 FF FF FF 48 8D 04 0A E8 ?? ?? ?? ?? 48 8D 0D ?? ?? ?? ?? 48 89 8C 24 48 01 00 00 48 89 84 24 50 01 00 00 48 8D 05 ?? ?? ?? ?? BB 05 00 00 00 48 8D 8C 24 38 01 00 00 BF 02 00 00 00 48 89 FE E8 ?? ?? ?? ?? }

$code2 = { 48 89 35 ?? ?? ?? ?? 0F B6 94 24 80 02 00 00 88 15 ?? ?? ?? ?? 0F B6 94 24 81 02 00 00 88 15 ?? ?? ?? ?? 0F B6 94 24 82 02 00 00 88 15 ?? ?? ?? ?? 0F B6 94 24 83 02 00 00 88 15 ?? ?? ?? ?? 48 8B 05 ?? ?? ?? ?? }

$code3 = { 48 8D 15 ?? ?? ?? ?? 48 89 94 24 68 04 00 00 48 C7 84 24 78 04 00 00 15 00 00 00 48 8D 15 ?? ?? ?? ?? 48 89 94 24 70 04 00 00 48 8D 15 ?? ?? ?? ?? 48 89 94 24 80 04 00 00 48 8D 35 ?? ?? ?? ?? 48 89 B4 24 88 04 00 00 90 }

condition:

uint16(0) == 0x457f and (2 of ($string*) or 2 of ($symbol*) or any of ($code*))

}

The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.

Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein. Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.

Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content without notice.

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Nate Bill
Threat Researcher

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July 21, 2026

スタジアム運営を任されるAI。セキュリティチームはこれをどう保護すべきか?

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スタジアム運営に使われるAIをどう保護するべきか

主なポイント

  • AIはアクセス管理、群衆管理、チケット発行、設備管理、監視カメラ等、スタジアムの運営に重要な機能に導入されつつある。  
  • シャドーAIやサードパーティAIの利用はスタジアムのセキュリティチームの目の届かないところでリスクを生み出している。
  • セキュリティチームはどのようなAIシステムが存在しているかだけでなく、それらが何にアクセスし、どのようなアクションを実行できるかを理解できなければならない。
  • イベントのレジリエンスにはAI、IT、OT、アイデンティティ、サードパーティ全体に渡る継続的監視と対応が必要。

現代のスタジアムは、他に類を見ないインフラです。以前にも書いたことがありますが、イベント開催日には、スタジアムに生命が吹き込まれ、グッズや食べ物の販売、交通のハブ、広大な通信インフラ、そしてフィールド上のさまざまな技術など、これらが連携した1つの巨大なエコシステムとして機能します。その規模と複雑性により、スタジアムはサイバーセキュリティにとって最も過酷な環境の1つとなっています。その運営に今、AIを導入することで私たちは新たな次元のリスクを負おうとしています。

スタジアム運営にAIを導入することの利点は明らかです。スタジアム運営者はAIを使うことにより、混雑したゲートからファンを安全に誘導し、売店の需要を予測し、生体認証システムを管理し、不審な動きを監視カメラで見つけ、また空調や換気を制御することができます。上手く使えば、イベントをより安全に、迅速に、より効率的にすることができます。

ただし、AIはセキュリティモデルも変化させます。

ダークトレースが最近発表したスポーツを取り巻く脅威状況についての調査では、プロスポーツ組織のサイバーセキュリティプロフェッショナルを対象に、自組織の運用範囲の中でサイバー侵害が最も重大な影響を及ぼすのはどこかという質問をしました。調査対象のプロフェッショナルの最も多い34%が指摘した分野は、スタジアムの運営でした。それと同時に、35%は自組織がすでにスタジアムの運営にAIを使用している、あるいは今後12か月間に導入の予定があると回答しています。

セキュリティチームはもはや、スタジアムを中心とした従来型のITシステムだけを保護しているのではありません。スタジアムの基盤となる重要機能を動かしている、AIシステムの保護も求められるようになっているのです。

承認済みAIとシャドーAIの違い

スタジアムのセキュリティチームが知っているAIと、そうでないAIの間には明確な違いがあります。

承認済みAIとは、審査およびテストされ、施設の運用環境に統合されたAIです。こうしたAIは、監視カメラのアナリティクス、アクセス管理、設備管理、チケット発行、ロジスティクス、放送オペレーション、海賊版対策などに使われているかもしれません。それらのAIには、明確なオーナーシップ、アクセス制御、ログ記録、ベンダーレビュー、データ保護規則などがあるはずです。それによってリスクがなくなるわけではありませんが、セキュリティチームは適切なガバナンスを整備できます。

シャドーAIはそれとは違います。シャドーAIとは、従業員、請負業者、サプライヤーによる承認されていないAIの利用です。多くの場合それは善良な意図で始まります。たとえば、作業をもっと早く進めたいと思う人がいるかもしれません。あるいは、ブリーフィングの原稿を作るためにスタッフが内部情報をパブリックAIにペーストする、開発者がチケット発行プログラムのデバッグのためにAIアシスタントを使用する、サプライヤーがAIスケジューリングツールを配送ルートに接続する、デザイナーがモックアップ作成のために未公開の会場設計図やスポンサーの資料をAIにアップロードするなどの事です。

これらの行動はいずれも、それを行っている人からすればセキュリティ上の判断のようには感じられません。しかし、これらの行為は機密性の高い運営データを、スタジアムが管理していない環境に送出し、隠れたリスクを生みます。

承認済みAIスタックは、セキュリティチームから見えています。シャドーAIスタックは多くの場合そうではありません。

試合開催日に増幅するリスクと影響

通常のエンタープライズ環境であれば、不審なログイン、普段とは異なるデータ転送、予期しないサードパーティサービスへの接続をセキュリティチームが調査するための時間は数時間あるでしょう。スタジアムでは、インシデントが起こる可能性の高い瞬間は、チームに最も余力がなくインシデントが最も大きな影響を及ぼし得るタイミング、つまり試合開催日です。

群衆管理に使われているAIシステムが予期せぬ振る舞いをしたとき、その問題は単に技術上の問題ではありません。それは会場内の物理的な動きに影響を及ぼすかもしれません。

サプライヤーツールが運営データを承認されていないAIプラットフォームに送信すれば、それはデータガバナンスだけの問題ではありません。配送ルートやアクセス制限のスケジュール、スタッフ配備計画などが漏洩するかもしれません。

最も危険なシナリオは必ずしも派手な、劇的な攻撃とは限らず、外部ベンダーがソフトウェア更新でAI機能を追加した、あるいはスタッフのワークフローで未承認のツールが使用されているなど、誰も想定していなかった隠れた依存関係から発生することがあります。

イベントが始まると、これらの隠れたつながりが運営上のリスクになる可能性があります。

サプライチェーンはスタジアムのアタックサーフェス(攻撃対象領域)の一部

すべての大規模スポーツイベントは、さまざまなサプライチェーンとパートナーシップで構築されています。ケータリングサービスや輸送、放送システム、施設管理チームなど、あらゆるピースが必須であり、それぞれがセキュリティチャネルを作り出しています。サプライチェーン侵害のリスクはすでによく知られており私たちが目にしてきたいくつかの有名な侵害事例の原因となっています。マジソン・スクエア・ガーデンを所有するMSG Entertainment社のデータ侵害事例は3月に大きく報道されましたが、これはMSG Entertainment社のバックオフィスシステムで使用されていたOracleのE-Business Suiteから発生していました。また、2018年の平昌冬季オリンピックを標的としたOlympic Destroyer攻撃はこの大会のメインITサービスプロバイダーに対する侵害から始まったと言われています。そして、AIの導入によりこのリスクは増大しつつあります。

スタジアム自体は自社のAIシステムに厳しいルールを設けているかもしれませんが、ベンダーは別のツールを使っている場合があります。スタッフの配備や、配送のタイミング、在庫、顧客とのやり取りの管理にAIを使っている業者もあるでしょう。また、既に使用しているソフトウェアにAI機能が追加されていることに気が付いていないケースもあります。

スタジアムの運営でAIを保護する際の最も難しい問題の1つはこの点です。リスクはスタジアムが選択したツールから来るとは限らないのです。サプライヤーが選択したツールや、有効に設定されていることをサプライヤーが知らなかった機能からリスクが発生する可能性があります。

セキュリティチームは、ベンダーのアクセスを管理するのと同じようにベンダーのAIを扱う必要があります。サプライヤーが何に接続できるか、どのようなデータを見ることができるか、どのようなツールを使っているか、そしてこれらのツールがデータ露出や水平移動(ラテラルムーブメント)の新たな経路を作り出さないかを知る必要があるのです。

サードパーティAIツールがリスクを作り出すのに深いアクセス権は必要ありません。特定の情報が不適切なタイミングで露出するだけでリスクを招きます。

スタジアム運営におけるAIについてセキュリティチームが確認すべき4つの質問

AIがスタジアム運営の一部となるなかで、セキュリティチームは基本的な承認リストの先へ進む必要があります。次の4つの点を問う必要があります:

1.  AIはどこで使われているか?

すぐに思いつくのは、コンピュータービジョン、アクセス管理、チケット発行、ロジスティクス、設備管理等のツールです。しかし、SaaSプラットフォーム、ベンダーツール、ブラウザ拡張、開発者ワークフロー、スマートビルディングシステム、コラボレーションツールにも見えにくい形でAIが含まれています。

2. AIは何にアクセスできるか?

そのAIはインシデントログ、スタジアム設計図、チケット発行データ、ビデオ映像、建物管理、ファン情報、認証情報、サプライヤーシステムを見ることができるでしょうか?それは情報を分析するだけでしょうか、それともアクションを実行することもできるでしょうか?

3. AIは何を実行できるか?

AIエージェントは単なる受動的ツールではありません。APIを呼び出す、記録を更新する、命令を生成する、ワークフローをトリガーする、あるいはユーザーやサービスアカウントの権限を持って行動できるものもあります。スタジアムにおいて、その違いはきわめて重要な意味を持ちます。アクションを提案するAIシステムと、アクションを実行できるAIの間には大きな違いがあります。

4. 何が正常な状態か?

セキュリティアーキテクチャとしては、静的なルールだけでは不十分となるでしょう。AIの使用状況は急激に変化します:既存のプラットフォーム内にAIツールが出現し、ベンダーがAIを使った新しいサービスを追加し、多忙をきわめたスタッフはAIを使った回避策を見つけるかもしれません。セキュリティチームは、何かが変化したときにそれを発見できるよう、人、アイデンティティ、デバイス、ネットワーク、クラウドサービス、サプライヤー、AIツールのすべてにわたって正常な振る舞いを理解している必要があります。

このことは、わずかな異常が重大な意味をもつかもしれない、イベント開催中の環境において特に重要です。未承認のAIサービスへの接続は、ある状況では無害かもしれませんが別の文脈では深刻なものとなる可能性があります。そしてAIエージェントによるアクションの実行は、午前3時にセットアップを行っている状況では予期されたものかもしれませんが、試合開催中にそのアクションが発生した場合、疑わしいものかもしれません。あるアクティビティを意味のあるセキュリティ情報にするのはコンテキストです。また、迅速な対応を可能にするのもコンテキストであると言えます。組織内のAIベースセキュリティシステムが、アクションを実行する必要があることを知るためのリアルタイムのコンテキストを構築できれば、マシンスピードで脅威に対応できます。

AIはスタジアムの安全に貢献できる、ただしAIが安全であることが条件

AIにはスタジアム運営に貢献できる役割があります。観客の混雑をより早期に検知し、ボトルネックを解消し、施設をより効率的に管理し、ファン体験を向上させ、プレッシャーのかかる状況下でイベント運営チームをサポートすることができます。

問題への答えはすべてのAIの導入のペースを落とすことではありません。それが解決策ではありません。答えは、AIを可視化し、管理し、試合日の運営の一部となる前に安全にすることです。

スタジアムの運営チームやイベント主催者にとって、これはAIの使用を会場とサプライヤーエコシステム全体にわたってマッピングすることを意味します。また、各AIシステムが何にアクセスでき、どのようなアクションを実行できるかを理解することです。スタッフの判断で回避策を探すのに任せるのではなく、彼らのニーズを満たす承認済みのツールを提供することも重要です。ベンダーとの契約や監査にAIの利用について明記することも必要です。さらに、わかりやすい主なシステムだけではなく、環境全体で動作を監視することも大事です。スタジアムから見えないものを安全にすることはできません。

AIが、スタジアム内の人の移動、アクセスの制御、設備の管理、サプライヤーのサポート、メディアの権利保護の一部となるとき、それはもはや追加機能ではなく、イベントインフラの一部となるのです。

イベントインフラは、スタジアムのゲートが開く前に入念に準備され、安全でシームレスかつ信頼性の高いイベント体験を実現するのに必要な、オペレーショナルレジリエンスによって維持されなければなりません。

Darktraceはスタジアム運営のためのAIをどう保護できるか

ダークトレースは、10年以上にわたり構築してきたビヘイビアAIの専門技術を、複雑で曖昧な環境で機能するように設計された、組織全体をカバーするプラットフォームで提供しています。2022 FIFAワールドカップカタール大会からF1グランプリまで、Darktraceは世界のさまざまな会場や米国中のスタジアムにおいて運営を支える統合された大規模なITおよびOT環境を保護しています。

他のサイバーセキュリティ技術は、過去の攻撃に基づいて新しい攻撃を予測しようとします。しかし問題は、AIが人間のように動作することです。あらゆるアクションが新たな情報をもたらし、それによってAIの動作は変化するため、予測不可能です。過去に見られた攻撃の戦術はもはや方程式の小さな部分に過ぎません。その結果多くのベンダーは実証されていない技術を買収し、改修することによりAIの保護を行おうとしています。  

ダークトレースのアプローチは他とは根本的に異なります。ダークトレースの適応型AIは、人とAIの振る舞いを学習し続けることにより組織についての理解を構築するため、動作の逸脱が起こった時にそれを検知し自律的に対応することが可能です。ダークトレースの提供するビヘイビアベースの防御プラットフォームは、新たなワークフロー、エージェント、アプリケーションの導入に対応して組織内のAI、人、インフラを保護することにより、大規模なAI変革を可能にします。

AIによって変化する組織の潜在力。Darktraceは組織が自信を持って前へ進むのに役立ちます。ダークトレースはスタジアムインフラ内の人とテクノロジーを保護するセキュリティチームに対し、新たなテクノロジーの導入を保護するのに必要な理解、可視性、自律的アクションを提供し、AI時代を構築するための変革を後押しします。

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Karim Benslimane
VP, Field CISO

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July 15, 2026

Security After Signatures: Operating in a World of Pre‑CVE Disclosure Exploitation, Collapsed Trust Boundaries, and Autonomous Systems

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Three shifts have reshaped what it means to defend an enterprise securely.  

First, exploitation often begins before defenders have a Common Vulnerabilities and Exposures (CVE) identifier, a security advisory, or an entry in the Cybersecurity and Infrastructure Security Agency's (CISA) Known Exploited Vulnerabilities (KEV) catalog.

Secondly, the trust boundary has moved beyond the network edge into identities, tokens, APIs, and Software-as-a-Service (SaaS) workflows.  

Third, an increasing share of business activity is executed through automation, integrations, and AI agent-like systems that can act faster than teams can verify intent.  

If your security model still relies on detecting known bad artefacts, triaging isolated alerts, and waiting for confirmation before acting, you are already behind the threat.  

This is not a failure of security teams; it’s a failure of the operating model to keep pace with how the environment has changed.

A SOC built around alerts and signatures assumes that malicious activity will eventually surface as an event. In real incidents, however, the decisive evidence is rarely a single event. Instead, it is a chain of individually explainable actions that only appears malicious once you connect the dots across identity, non-human identity, cloud, email, SaaS, operational technology (OT), and network telemetry.

The defenders succeeding today observe behaviors, link them into sequences, understand what those sequences mean, and contain impact before the full story unfolds. That is the operating model the current threat environment demands.  

Exploitation before disclosure

The first shift is the straightforward: the time to exploit has dropped to nearly zero.  

In one example, Darktrace observed a sequence of subtle but strategically significant anomalies within a customer environment that later aligned with exploitation of CVE‑2025‑0994 in Trimble Cityworks by likely Chinese-nexus threat actors. Behavioral indicators were visible at least 18 days before public disclosure, with related anomalies emerging 40 to 50 days earlier during the intrusion window.  

This case illustrates a familiar pattern: clusters of weak‑signal anomalies combing to form an actionable picture of intrusion long before a CVE is published. Such activity reflects long‑horizon, option‑preserving operator models often associated with mature state‑linked activity.  

Figure 1: Darktrace’s detection of malicious exploitation of CVE 2025-0994, later tied to Chinese-nexus threat actors targeting critical national infrastructure (CNI) in the US, weeks before public disclosure.

Throughout 2025 and 2026, Darktrace has continued to observe the value of anomaly-based detections across a range of incidents.

CVE CVE Public Disclosure Date Darktrace Detection Date Days Between Detection of Exploitation and CVE Public Disclosure
CVE 2025 0994
(Trimble City Works)
2025-02-06 2025-01-19 18 Days
CVE 2025-24183
(Apache)
2025-03-10 2025-02-18 20 days
CVE 2025-10035
(Fortra GoAnywhere)
2025-09-18 2025-09-11 7 days

Identity is the real control plane

The second shift is that identity has replaced perimeter as the primary control plane. As Darktrace’s Annual Threat Report 2026 illustrated, identity remains the main challenge in defending against modern intrusions. A clear example is the Adversary-in-the-Middle (AiTM) case published by Darktrace in December 2025. A phishing email led to the compromise of an Office 365 account. Session hijacking bypassed multi-factor authentication (MFA), and the compromised account was used for follow-on phishing and persistence activities including the creation of malicious email rules.  

Every step in that sequence mattered. A successful login alone does not prove legitimacy. An inbox rule, on its own, may not appear catastrophic. Mail activity, viewed in isolation, may seem operationally normal. But the behavioral chain tells a different story: credential theft, token abuse, persistence, and onward compromise through a trusted identity.  

This is why the question is no longer “Did the user authenticate successfully”. The more important question is, “Does this identity action make sense right now, in this context, given what came before it?” The AiTM case shows how identity can be compromised. In practice, however, attacks rarely remained confined to identity alone.  

In another Darktrace case, a compromised SaaS account triggered activity across the email, SaaS, and network layers, including inbox rule changes, phishing propagation, and connections to suspicious infrastructure. Viewed in isolation, none of these events were decisive. Together, however,  they formed a behavioral sequence that revealed the intrusion, with the full attack story automatically correlated and surfaced to defenders by Darktrace’s Cyber AI Analyst.  

Figure 2: Cyber AI Analyst correlated and appended additional events to the incident, including other users who connected to the suspicious redirect link after outbound phishing emails were sent.

AI accelerates the threat  

The third shift is the one many teams still underestimate: trusted tooling, integrations, and AI agent-like systems can create actions that appear legitimate but are strategically dangerous.  

The shift becomes clearer when examining how governments are now framing AI risk. In 2026, guidance published by CISA, UK’s National Cyber Security Centre (NCSC) and Five Eyes partners warned that agentic systems expand attack surfaces, accumulate privilege, and can behave in ways that are difficult to predict or explain [1]. The advice is simple: assume unexpected behavior and design controls around it.  

The real risk is not AI usage. It is unknown autonomy: systems with credentials, data access, and action paths that can execute workflow steps without sufficient behavioral validation, traceability, or human oversight. Darktrace’s Model Context Protocol (MCP) risk analysis provides a useful framework for understanding this challenge. Over-privileged agents, content injection, and tool abuse become high-consequence risks when connected systems can dynamically retrieve data, execute actions, and communicate externally.  

Whether security teams like it or not, AI is already in the enterprise. It will help drive innovation, but it will also be abused, whether accidentally or maliciously. In each of the cases below, AI either scaled the attacker, built the tooling, or existed within the environment as something to exploit or misuse.

1. AI as an Attack Multiplier

In one campaign targeting Mexican government entities, a single operator used commercial AI platforms to generate exploits, automate reconnaissance, and process large volumes of data, compressing work that would traditionally have required an entire team into a single workflow [2].  

Darktrace is also observing this trend further down the stack. In one case, Darktrace identified AI-generated malware exploiting React2Shell, where an attacker used a Large Language Model (LLM) to produce working exploit code and deploy it at scale.  

[darktrace.com], [darktrace.com]

2. AI as an Attack Surface

Attempted AI exploitation is now appearing within customer environments. In one case involving an automation technology manufacturer, a compromised LLM proxy was seemingly used as a stepping stone to access additional AI services. When that attempt failed, the attacker pivoted to cryptomining.

What is clear is that the AI layer has already become an asset worth probing, exploiting, and pivoting through. It is also clear that defenders benefit from rapidly understanding how these activities connect. In this case, Cyber AI Analyst automatically pieced together the intrusion, while Darktrace’s Managed Threat Detection service alerted to the customer, enabling the activity to be contained before it could progress further.

Figure 3: Cyber AI Analyst's investigation into a compromised LLM proxy that was abused for cryptomining activity.

AI as a trusted but dangerous actor

This does not require a cinematic vision of “rogue AI.” The Salesloft incident provides a more grounded example, where AI and automation operate with legitimate access but served malicious intent. In that case, attackers abused compromised OAuth tokens associated with the Drift AI chat agent to export significant volumes of data from Salesforce environments.  

The activity resembled legitimate API usage and relied on trusted SaaS integrations rather than malware or other obvious signs of intrusion. That is precisely the challenge. Traditional security controls are good at detecting forced entry, but far less effective when a trusted application integration behaves in a way that is technically permitted yet operationally harmful.  

In these scenarios, the security challenge shifts from validating access to validating behavior.

This is what that looks like in practice: AI-linked identities executing legitimate actions that require behavioral validation rather than access validation.

Figure 4: Darktrace / SECURE AI highlights anomalous activity across AI identities, surfacing critical behavior that requires validation and containment.

Early observations from Darktrace / SECURE AI deployments reinforce this reality. Across Darktrace's observed fleet, AI service connections per deployment increased 13% during the first half of 2026, reaching over 16 million connections overall. The typical organisation now interacts with seven different AI providers, evidence that AI is no longer operating at the edges of the enterprise. It is increasingly woven into day-to-day business activity.

The most common risks are not compromised models or advanced AI attacks. Instead, they stem from employees and business functions exposing sensitive information through entirely legitimate-looking interactions. Darktrace has observed repeated submission of personally identifiable information (PII), tax information, identification documents, and medical data into LLM prompts, alongside widespread use of unsanctioned (shadow) AI services and growing AI activity from mobile devices.  

For defenders, the challenge is increasingly one of context: understanding when legitimate business use crosses into material risk, while preserving privacy and user trust.

Conclusion

Across all three shifts, the pattern is the same: behavior precedes understanding. Security teams are not losing because adversaries have become invisible. An increasingly outdated security model assumes that malicious activity will reveal itself cleanly and early. It no longer does.  

In 2026 and beyond, defenders win by understanding behavioral sequences, continuously validating trust, and acting before certainty becomes hindsight. That is security after signatures. That is security in the AI era.

Credit to: Daniel Levy, Threat Hunting Data Scientist

Edited by: Ryan Traill, Content Manager

References

[1] https://www.cyber.gov.au/business-government/secure-design/artificial-intelligence/careful-adoption-of-agentic-ai-services  

[2]https://www.latimes.com/business/story/2026-02-26/hacker-used-anthropics-claude-ai-to-steal-mexican-government-data

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Nathaniel Jones
VP, Security & AI Strategy, Field CISO
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