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July 27, 2022

Identifying PrivateLoader Network Threats

Learn how Darktrace identifies network-based indicators of compromise for the PrivateLoader malware. Gain insights into advanced threat detection.
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
Sam Lister
Specialist Security Researcher
Written by
Shuh Chin Goh
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27
Jul 2022

Instead of delivering their malicious payloads themselves, threat actors can pay certain cybercriminals (known as pay-per-install (PPI) providers) to deliver their payloads for them. Since January 2022, Darktrace’s SOC has observed several cases of PPI providers delivering their clients’ payloads using a modular malware downloader known as ‘PrivateLoader’.

This blog will explore how these PPI providers installed PrivateLoader onto systems and outline the steps which the infected PrivateLoader bots took to install further malicious payloads. The details provided here are intended to provide insight into the operations of PrivateLoader and to assist security teams in identifying PrivateLoader bots within their own networks.  

Threat Summary 

Between January and June 2022, Darktrace identified the following sequence of network behaviours within the environments of several Darktrace clients. Patterns of activity involving these steps are paradigmatic examples of PrivateLoader activity:

1. A victim’s device is redirected to a page which instructs them to download a password-protected archive file from a file storage service — typically Discord Content Delivery Network (CDN)

2. The device contacts a file storage service (typically Discord CDN) via SSL connections

3. The device either contacts Pastebin via SSL connections, makes an HTTP GET request with the URI string ‘/server.txt’ or ‘server_p.txt’ to 45.144.225[.]57, or makes an HTTP GET request with the URI string ‘/proxies.txt’ to 212.193.30[.]45

4. The device makes an HTTP GET request with the URI string ‘/base/api/statistics.php’ to either 212.193.30[.]21, 85.202.169[.]116, 2.56.56[.]126 or 2.56.59[.]42

5. The device contacts a file storage service (typically Discord CDN) via SSL connections

6. The device makes a HTTP POST request with the URI string ‘/base/api/getData.php’ to either 212.193.30[.]21, 85.202.169[.]116, 2.56.56[.]126 or 2.56.59[.]42

7. The device finally downloads malicious payloads from a variety of endpoints

The PPI Business 

Before exploring PrivateLoader in more detail, the pay-per-install (PPI) business should be contextualized. This consists of two parties:  

1. PPI clients - actors who want their malicious payloads to be installed onto a large number of target systems. PPI clients are typically entry-level threat actors who seek to widely distribute commodity malware [1]

2. PPI providers - actors who PPI clients can pay to install their malicious payloads 

As the smugglers of the cybercriminal world, PPI providers typically advertise their malware delivery services on underground web forums. In some cases, PPI services can even be accessed via Clearnet websites such as InstallBest and InstallShop [2] (Figure 1).  

Figure 1: A snapshot of the InstallBest PPI login page [2]


To utilize a PPI provider’s service, a PPI client must typically specify: 

(A)  the URLs of the payloads which they want to be installed

(B)  the number of systems onto which they want their payloads to be installed

(C)  their geographical targeting preferences. 

Payment of course, is also required. To fulfil their clients’ requests, PPI providers typically make use of downloaders - malware which instructs the devices on which it is running to download and execute further payloads. PPI providers seek to install their downloaders onto as many systems as possible. Follow-on payloads are usually determined by system information garnered and relayed back to the PPI providers’ command and control (C2) infrastructure. PPI providers may disseminate their downloaders themselves, or they may outsource the dissemination to third parties called ‘affiliates’ [3].  

Back in May 2021, Intel 471 researchers became aware of PPI providers using a novel downloader (dubbed ‘PrivateLoader’) to conduct their operations. Since Intel 471’s public disclosure of the downloader back in Feb 2022 [4], several other threat research teams, such as the Walmart Cyber Intel Team [5], Zscaler ThreatLabz [6], and Trend Micro Research [7] have all provided valuable insights into the downloader’s behaviour. 

Anatomy of a PrivateLoader Infection

The PrivateLoader downloader, which is written in C++, was originally monolithic (i.e, consisted of only one module). At some point, however, the downloader became modular (i.e, consisting of multiple modules). The modules communicate via HTTP and employ various anti-analysis methods. PrivateLoader currently consists of the following three modules [8]: 

  • The loader module: Instructs the system on which it is running to retrieve the IP address of the main C2 server and to download and execute the PrivateLoader core module
  • The core module: Instructs the system on which it is running to send system information to the main C2 server, to download and execute further malicious payloads, and to relay information regarding installed payloads back to the main C2 server
  • The service module: Instructs the system on which it is running to keep the PrivateLoader modules running

Kill Chain Deep-Dive 

The chain of activity starts with the user’s browser being redirected to a webpage which instructs them to download a password-protected archive file from a file storage service such as Discord CDN. Discord is a popular VoIP and instant messaging service, and Discord CDN is the service’s CDN infrastructure. In several cases, the webpages to which users’ browsers were redirected were hosted on ‘hero-files[.]com’ (Figure 2), ‘qd-files[.]com’, and ‘pu-file[.]com’ (Figure 3). 

Figure 2: An image of a page hosted on hero-files[.]com - an endpoint which Darktrace observed systems contacting before downloading PrivateLoader from Discord CDN
Figure 3: An image of a page hosted on pu-file[.]com- an endpoint which Darktrace observed systems contacting before downloading PrivateLoader from Discord CDN


On attempting to download cracked/pirated software, users’ browsers were typically redirected to download instruction pages. In one case however, a user’s device showed signs of being infected with the malicious Chrome extension, ChromeBack [9], immediately before it contacted a webpage providing download instructions (Figure 4). This may suggest that cracked software downloads are not the only cause of users’ browsers being redirected to these download instruction pages (Figure 5). 

Figure 4: The event log for this device (taken from the Darktrace Threat Visualiser interface) shows that the device contacted endpoints associated with ChromeBack ('freychang[.]fun') prior to visiting a page ('qd-file[.]com') which instructed the device’s user to download an archive file from Discord CDN
 Figure 5: An image of the website 'crackright[.]com'- a provider of cracked software. Systems which attempted to download software from this website were subsequently led to pages providing instructions to download a password-protected archive from Discord CDN


After users’ devices were redirected to pages instructing them to download a password-protected archive, they subsequently contacted cdn.discordapp[.]com over SSL. The archive files which users downloaded over these SSL connections likely contained the PrivateLoader loader module. Immediately after contacting the file storage endpoint, users’ devices were observed either contacting Pastebin over SSL, making an HTTP GET request with the URI string ‘/server.txt’ or ‘server_p.txt’ to 45.144.225[.]57, or making an HTTP GET request with the URI string ‘/proxies.txt’ to 212.193.30[.]45 (Figure 6).

Distinctive user-agent strings such as those containing question marks (e.g. ‘????ll’) and strings referencing outdated Chrome browser versions were consistently seen in these HTTP requests. The following chrome agent was repeatedly observed: ‘Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36’.

In some cases, devices also displayed signs of infection with other strains of malware such as the RedLine infostealer and the BeamWinHTTP malware downloader. This may suggest that the password-protected archives embedded several payloads.

Figure 6: This figure, obtained from Darktrace's Advanced Search interface, represents the post-infection behaviour displayed by a PrivateLoader bot. After visiting hero-files[.]com and downloading the PrivateLoader loader module from Discord CDN, the device can be seen making HTTP GET requests for ‘/proxies.txt’ and ‘/server.txt’ and contacting pastebin[.]com

It seems that PrivateLoader bots contact Pastebin, 45.144.225[.]57, and 212.193.30[.]45 in order to retrieve the IP address of PrivateLoader’s main C2 server - the server which provides PrivateLoader bots with payload URLs. This technique used by the operators of PrivateLoader closely mirrors the well-known espionage tactic known as ‘dead drop’.

The dead drop is a method of espionage tradecraft in which an individual leaves a physical object such as papers, cash, or weapons in an agreed hiding spot so that the intended recipient can retrieve the object later on without having to come in to contact with the source. When threat actors host information about core C2 infrastructure on intermediary endpoints, the hosted information is analogously called a ‘Dead Drop Resolver’ or ‘DDR’. Example URLs of DDRs used by PrivateLoader:

  • https://pastebin[.]com/...
  • http://212.193.30[.]45/proxies.txt
  • http://45.144.225[.]57/server.txt
  • http://45.144.255[.]57/server_p.txt

The ‘proxies.txt’ DDR hosted on 212.193.40[.]45 contains a list of 132 IP address / port pairs. The 119th line of this list includes a scrambled version of the IP address of PrivateLoader’s main C2 server (Figures 7 & 8). Prior to June, it seems that the main C2 IP address was ‘212.193.30[.]21’, however, the IP address appears to have recently changed to ‘85.202.169[.]116’. In a limited set of cases, Darktrace also observed PrivateLoader bots retrieving payload URLs from 2.56.56[.]126 and 2.56.59[.]42 (rather than from 212.193.30[.]21 or 85.202.169[.]116). These IP addresses may be hardcoded secondary C2 address which PrivateLoader bots use in cases where they are unable to retrieve the primary C2 address from Pastebin, 212.193.30[.]45 or 45.144.255[.]57 [10]. 

Figure 7: Before June, the 119th entry of the ‘proxies.txt’ file lists '30.212.21.193' -  a scrambling of the ‘212.193.30[.]21’ main C2 IP address
Figure 8: Since June, the 119th entry of the ‘proxies.txt’ file lists '169.85.116.202' - a scrambling of the '85.202.169[.]116' main C2 IP address

Once PrivateLoader bots had retrieved C2 information from either Pastebin, 45.144.225[.]57, or 212.193.30[.]45, they went on to make HTTP GET requests for ‘/base/api/statistics.php’ to either 212.193.30[.]21, 85.202.169[.]116, 2.56.56[.]126, or 2.56.59[.]42 (Figure 9). The server responded to these requests with an XOR encrypted string. The strings were encrypted using a 1-byte key [11], such as 0001101 (Figure 10). Decrypting the string revealed a URL for a BMP file hosted on Discord CDN, such as ‘hxxps://cdn.discordapp[.]com/attachments/978284851323088960/986671030670078012/PL_Client.bmp’. These encrypted URLs appear to be file download paths for the PrivateLoader core module. 

Figure 9: HTTP response from server to an HTTP GET request for '/base/api/statistics.php'
Figure 10: XOR decrypting the string with the one-byte key, 00011101, outputs a URL in CyberChef

After PrivateLoader bots retrieved the 'cdn.discordapp[.]com’ URL from 212.193.30[.]21, 85.202.169[.]116, 2.56.56[.]126, or 2.56.59[.]42, they immediately contacted Discord CDN via SSL connections in order to obtain the PrivateLoader core module. Execution of this module resulted in the bots making HTTP POST requests (with the URI string ‘/base/api/getData.php’) to the main C2 address (Figures 11 & 12). Both the data which the PrivateLoader bots sent over these HTTP POST requests and the data returned via the C2 server’s HTTP responses were heavily encrypted using a combination of password-based key derivation, base64 encoding, AES encryption, and HMAC validation [12]. 

Figure 11: The above image, taken from Darktrace's Advanced Search interface, shows a PrivateLoader bot carrying out the following steps: contact ‘hero-files[.]com’ --> contact ‘cdn.discordapp[.]com’ --> retrieve ‘/proxies.txt’ from 212.193.30[.]45 --> retrieve ‘/base/api/statistics.php’ from 212.193.30[.]21 --> contact ‘cdn.discordapp[.]com --> make HTTP POST request with the URI ‘base/api/getData.php’ to 212.193.30[.]21
Figure 12: A PCAP of the data sent via the HTTP POST (in red), and the data returned by the C2 endpoint (in blue)

These ‘/base/api/getData.php’ POST requests contain a command, a campaign name and a JSON object. The response may either contain a simple status message (such as “success”) or a JSON object containing URLs of payloads. After making these HTTP connections, PrivateLoader bots were observed downloading and executing large volumes of payloads (Figure 13), ranging from crypto-miners to infostealers (such as Mars stealer), and even to other malware downloaders (such as SmokeLoader). In some cases, bots were also seen downloading files with ‘.bmp’ extensions, such as ‘Service.bmp’, ‘Cube_WW14.bmp’, and ‘NiceProcessX64.bmp’, from 45.144.225[.]57 - the same DDR endpoint from which PrivateLoader bots retrieved main C2 information. These ‘.bmp’ payloads are likely related to the PrivateLoader service module [13]. Certain bots made follow-up HTTP POST requests (with the URI string ‘/service/communication.php’) to either 212.193.30[.]21 or 85.202.169[.]116, indicating the presence of the PrivateLoader service module, which has the purpose of establishing persistence on the device (Figure 14). 

Figure 13: The above image, taken from Darktrace's Advanced Search interface, outlines the plethora of malware payloads downloaded by a PrivateLoader bot after it made an HTTP POST request to the ‘/base/api/getData.php’ endpoint. The PrivateLoader service module is highlighted in red
Figure 14: The event log for a PrivateLoader bot, obtained from the Threat Visualiser interface, shows a device making HTTP POST requests to ‘/service/communication.php’ and connecting to the NanoPool mining pool, indicating successful execution of downloaded payloads

In several observed cases, PrivateLoader bots downloaded another malware downloader called ‘SmokeLoader’ (payloads named ‘toolspab2.exe’ and ‘toolspab3.exe’) from “Privacy Tools” endpoints [14], such as ‘privacy-tools-for-you-802[.]com’ and ‘privacy-tools-for-you-783[.]com’. These “Privacy Tools” domains are likely impersonation attempts of the legitimate ‘privacytools[.]io’ website - a website run by volunteers who advocate for data privacy [15]. 

After downloading and executing malicious payloads, PrivateLoader bots were typically seen contacting crypto-mining pools, such as NanoPool, and making HTTP POST requests to external hosts associated with SmokeLoader, such as hosts named ‘host-data-coin-11[.]com’ and ‘file-coin-host-12[.]com’ [16]. In one case, a PrivateLoader bot went on to exfiltrate data over HTTP to an external host named ‘cheapf[.]link’, which was registered on the 14th March 2022 [17]. The name of the file which the PrivateLoader bot used to exfiltrate data was ‘NOP8QIMGV3W47Y.zip’, indicating information stealing activities by Mars Stealer (Figure 15) [18]. By saving the HTTP stream as raw data and utilizing a hex editor to remove the HTTP header portions, the hex data of the ZIP file was obtained. Saving the hex data using a ‘.zip’ extension and extracting the contents, a file directory consisting of system information and Chrome and Edge browsers’ Autofill data in cleartext .txt file format could be seen (Figure 16).

Figure 15: A PCAP of a PrivateLoader bot’s HTTP POST request to cheapf[.]link, with data sent by the bot appearing to include Chrome and Edge autofill data, as well as system information
Figure 16: File directory structure and files of the ZIP archive 

When left unattended, PrivateLoader bots continued to contact C2 infrastructure in order to relay details of executed payloads and to retrieve URLs of further payloads. 

Figure 17: Timeline of the attack

Darktrace Coverage 

Most of the incidents surveyed for this article belonged to prospective customers who were trialling Darktrace with RESPOND in passive mode, and thus without the ability for autonomous intervention. However in all observed cases, Darktrace DETECT was able to provide visibility into the actions taken by PrivateLoader bots. In one case, despite the infected bot being disconnected from the client’s network, Darktrace was still able to provide visibility into the device’s network behaviour due to the client’s usage of Darktrace/Endpoint. 

If a system within an organization’s network becomes infected with PrivateLoader, it will display a range of anomalous network behaviours before it downloads and executes malicious payloads. For example, it will contact Pastebin or make HTTP requests with new and unusual user-agent strings to rare external endpoints. These network behaviours will generate some of the following alerts on the Darktrace UI:

  • Compliance / Pastebin 
  • Device / New User Agent and New IP
  • Device / New User Agent
  • Device / Three or More New User Agents
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous Connection / POST to PHP on New External Host
  • Anomalous Connection / Posting HTTP to IP Without Hostname

Once the infected host obtains URLs for malware payloads from a C2 endpoint, it will likely start to download and execute large volumes of malicious files. These file downloads will usually cause Darktrace to generate some of the following alerts:

  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Numeric Exe Download
  • Anomalous File / Masqueraded File Transfer
  • Anomalous File / Multiple EXE from Rare External Locations
  • Device / Initial Breach Chain Compromise

If RESPOND is deployed in active mode, Darktrace will be able to autonomously block the download of additional malware payloads onto the target machine and the subsequent beaconing or crypto-mining activities through network inhibitors such as ‘Block matching connections’, ‘Enforce pattern of life’ and ‘Block all outgoing traffic’. The ‘Enforce pattern of life’ action results in a device only being able to make connections and data transfers which Darktrace considers normal for that device. The ‘Block all outgoing traffic’ action will cause all traffic originating from the device to be blocked. If the customer has Darktrace’s Proactive Threat Notification (PTN) service, then a breach of an Enhanced Monitoring model such as ‘Device / Initial Breach Chain Compromise’ will result in a Darktrace SOC analyst proactively notifying the customer of the suspicious activity. Below is a list of Darktrace RESPOND (Antigena) models which would be expected to breach due to PrivateLoader activity. Such models can seriously hamper attempts made by PrivateLoader bots to download malicious payloads. 

  • Antigena / Network / External Threat / Antigena Suspicious File Block
  • Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach
  • Antigena / Network / External Threat / Antigena File then New Outbound Block
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block 
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block

In one observed case, the infected bot began to download malicious payloads within one minute of becoming infected with PrivateLoader. Since RESPOND was correctly configured, it was able to immediately intervene by autonomously enforcing the device’s pattern of life for 2 hours and blocking all of the device’s outgoing traffic for 10 minutes (Figure 17). When malware moves at such a fast pace, the availability of autonomous response technology, which can respond immediately to detected threats, is key for the prevention of further damage.  

Figure 18: The event log for a Darktrace RESPOND (Antigena) model breach shows Darktrace RESPOND performing inhibitive actions once the PrivateLoader bot begins to download payloads

Conclusion

By investigating PrivateLoader infections over the past couple of months, Darktrace has observed PrivateLoader operators making changes to the downloader’s main C2 IP address and to the user-agent strings which the downloader uses in its C2 communications. It is relatively easy for the operators of PrivateLoader to change these superficial network-based features of the malware in order to evade detection [19]. However, once a system becomes infected with PrivateLoader, it will inevitably start to display anomalous patterns of network behaviour characteristic of the Tactics, Techniques and Procedures (TTPs) discussed in this blog.

Throughout 2022, Darktrace observed overlapping patterns of network activity within the environments of several customers, which reveal the archetypal steps of a PrivateLoader infection. Despite the changes made to PrivateLoader’s network-based features, Darktrace’s Self-Learning AI was able to continually identify infected bots, detecting every stage of an infection without relying on known indicators of compromise. When configured, RESPOND was able to immediately respond to such infections, preventing further advancement in the cyber kill chain and ultimately preventing the delivery of floods of payloads onto infected devices.

IoCs

MITRE ATT&CK Techniques Observed

References

[1], [8],[13] https://www.youtube.com/watch?v=Ldp7eESQotM  

[2] https://news.sophos.com/en-us/2021/09/01/fake-pirated-software-sites-serve-up-malware-droppers-as-a-service/

[3] https://www.researchgate.net/publication/228873118_Measuring_Pay-per Install_The_Commoditization_of_Malware_Distribution 

[4], [15] https://intel471.com/blog/privateloader-malware

[5] https://medium.com/walmartglobaltech/privateloader-to-anubis-loader-55d066a2653e 

[6], [10],[11], [12] https://www.zscaler.com/blogs/security-research/peeking-privateloader 

[7] https://www.trendmicro.com/en_us/research/22/e/netdooka-framework-distributed-via-privateloader-ppi.html

[9] https://www.gosecure.net/blog/2022/02/10/malicious-chrome-browser-extension-exposed-chromeback-leverages-silent-extension-loading/

[14] https://www.proofpoint.com/us/blog/threat-insight/malware-masquerades-privacy-tool 

[16] https://asec.ahnlab.com/en/30513/ 

[17]https://twitter.com/0xrb/status/1515956690642161669

[18] https://isc.sans.edu/forums/diary/Arkei+Variants+From+Vidar+to+Mars+Stealer/28468

[19] http://detect-respond.blogspot.com/2013/03/the-pyramid-of-pain.html

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
Sam Lister
Specialist Security Researcher
Written by
Shuh Chin Goh

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July 20, 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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