ブログ
/
Network
/
March 22, 2024

What are Botnets and How Darktrace Uncovers Them

Learn how Darktrace detected and implemented defense protocols against Socks5Systemz botnet before any threat to intelligence had been published.
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
Adam Potter
Senior Cyber Analyst
Default blog image
22
Mar 2024

What are botnets?

Although not a recent addition to the threat landscape, botnets persist as a significant concern for organizations, with many threat actors utilizing them for political, strategic, or financial gain. Botnets pose a particularly persistent threat to security teams; even if one compromised device is detected, attackers will likely have infected multiple devices and can continue to operate. Moreover, threat actors are able to easily replace the malware communication channels between infected devices and their command-and-control (C2) servers, making it incredibly difficult to remove the infection.

Botnet example: Socks5Systemz

One example of a botnet recently investigated by the Darktrace Threat Research team is Socks5Systemz. Socks5Systemz is a proxy-for-rent botnet, whereby actors can rent blocks of infected devices to perform proxying services.  Between August and November 2023, Darktrace detected indicators of Socks5Systemz botnet compromise within a cross-industry section of the customer base. Although open-source intelligence (OSINT) research of the botnet only appeared in November 2023, the anomaly-based approach of Darktrace DETECT™ allowed it to identify multiple stages of the network-based activity on affected customer systems well before traditional rules and signatures would have been implemented.

Darktrace’s Cyber AI Analyst™ complemented DETECT’s successful identification of Socks5Systemz activity on customer networks, playing a pivotal role in piecing together the seemingly separate events that comprised the wider compromise. This allowed Darktrace to build a clearer picture of the attack, empowering its customers with full visibility over emerging incidents.

In the customer environments highlighted in this blog, Darktrace RESPOND™ was not configured to operate autonomously. As a result, Socks5Systemz attacks were able to advance through their kill chains until customer security teams acted upon Darktrace’s detections and began their remediation procedures.

What is Socks5Systemz?

The Socks5Systemz botnet is a proxy service where individuals can use infected devices as proxy servers.

These devices act as ‘middlemen’, forwarding connections from malicious actors on to their intended destination. As this additional connectivity conceals the true origin of the connections, threat actors often use botnets to increase their anonymity. Although unauthorized proxy servers on a corporate network may not appear at first glance to be a priority for organizations and their security teams, complicity in proxy botnets could result in reputational damage and significant financial losses.

Since it was first observed in the wild in 2016, the Socks5Systemz botnet has grown steadily, seemingly unnoticed by cyber security professionals, and has infected a reported 10,000 devices worldwide [1]. Cyber security researchers noted a high concentration of compromised devices in India, with lower concentrations of devices infected in the United States, Latin America, Australia and multiple European and African countries [2]. Renting sections of the Socks5Systemz botnet costs between 1 USD and 4,000 USD, with options to increase the threading and time-range of the rentals [2]. Due to the lack of affected devices in Russia, some threat researchers have concluded that the botnet’s operators are likely Russian [2].

Darktrace’s Coverage of Socks5Systemz

The Darktrace Threat Research team conducted investigations into campaign-like activity across the customer base between August and November 2023, where multiple indicators of compromise (IoCs) relating to the Socks5Systemz proxy botnet were observed. Darktrace identified several stages of the attack chain described in static malware analysis by external researchers. Darktrace was also able to uncover additional IoCs and stages of the Socks5Systemz attack chain that had not featured in external threat research.

Delivery and Execution

Prior research on Socks5Systemz notes how the malware is typically delivered via user input, with delivery methods including phishing emails, exploit kits, malicious ads, and trojanized executables downloaded from peer-to-peer (P2P) networks [1].

Threat actors have also used separate malware loaders such as PrivateLoader and Amadey deliver the Socks5Systemz payload. These loaders will drop executable files that are responsible for setting up persistence and injecting the proxy bot into the infected device’s memory [2]. Although evidence of initial payload delivery did not appear during its investigations, Darktrace did discover IoCs relating to PrivateLoader and Amadey on multiple customer networks. Such activity included HTTP POST requests using PHP to rare external IPs and HTTP connections with a referrer header field, indicative of a redirected connection.

However, additional adjacent activity that may suggest initial user execution and was observed during Darktrace’s investigations. For example, an infected device on one deployment made a HTTP GET request to a rare external domain with a “.fun” top-level domain (TLD) for a PDF file. The URI also appears to have contained a client ID. While this download and HTTP request likely corresponded to the gathering and transmission of further telemetry data and infection verification [2], the downloaded PDF file may have represented a malicious payload.

Advanced Search log details highlighting a device infected by Socks5Systemz downloading a suspicious PDF file.
Figure 1: Advanced Search log details highlighting a device infected by Socks5Systemz downloading a suspicious PDF file.

Establishing C2 Communication  

Once the proxy bot has been injected into the device’s memory, the malware attempts to contact servers owned by the botnet’s operators. Across several customer environments, Darktrace identified infected devices attempting to establish connections with such C2 servers. First, affected devices would make repeated HTTP GET requests over port 80 to rare external domains; these endpoints typically had “.ua” and “.ru” TLDs. The majority of these connection attempts were not preceded by a DNS host lookup, suggesting that the domains were already loaded in the device’s cache memory or hardcoded into the code of running processes.

Figure 2: Breach log data connections identifying repeated unusual HTTP connections over port 80 for domains without prior DNS host lookup.

While most initial HTTP GET requests across investigated incidents did not feature DNS host lookups, Darktrace did identify affected devices on a small number of customer environments performing a series of DNS host lookups for seemingly algorithmically generated domains (DGA). These domains feature the same TLDs as those seen in connections without prior DNS host lookups.  

Figure 3: Cyber AI Analyst data indicating a subset of DGAs queried via DNS by infected devices.

These DNS requests follow the activity reported by researchers, where infected devices query a hardcoded DNS server controlled by the threat actor for an DGA domain [2]. However, as the bulk of Darktrace’s investigations presented HTTP requests without a prior DNS host lookup, this activity indicates a significant deviation from the behavior reported by OSINT sources. This could indicate that multiple variations of the Socks5Systemz botnet were circulating at the time of investigation.

Most hostnames observed during this time of investigation follow a specific regular expression format: /[a-z]{7}\.(ua|net|info|com|ru)/ or /[a-z0-9]{15}\.(ua)/. Darktrace also noticed the HTTP GET requests for DGA domains followed a consistent URI pattern: /single.php?c=<STRING>. The requests were also commonly made using the “Mozilla/5.0 (Windows; U; MSIE 9.0; Windows NT 9.0; en-US)” user agent over port 80.

This URI pattern observed during Darktrace’s investigations appears to reflect infected devices contacting Socks5Systemz C2 servers to register the system and details of the host, and signal it is ready to receive further instructions [2]. These URIs are encrypted with a RC4 stream cipher and contain information relating to the device’s operating system and architecture, as well as details of the infection.

The HTTP GET requests during this time, which involved devices made to a variety a variety of similar DGA domains, appeared alongside IP addresses that were later identified as Socks5Systemz C2 servers.

Figure 4: Cyber AI Analyst investigation details highlighting HTTP GET activity whereby RC4 encrypted data is sent to proxy C2 domains.

However, not all affected devices observed by Darktrace used DGA domains to transmit RC4 encoded data. Some investigated systems were observed making similar HTTP GET requests over port 80, albeit to the external domain: “bddns[.]cc”, using the aforementioned Mozilla user agent. During these requests, Darktrace identified a consistent URI pattern, similar to that seen in the DGA domain GET requests: /sign/<RC4 cipher text>.  

Darktrace DETECT recognized the rarity of the domains and IPs that were connected to by affected devices, as well as the usage of the new Mozilla user agent.  The HTTP connections, and the corresponding Darktrace DETECT model breaches, parallel the analysis made by external researchers: if the initial DGA DNS requests do not return a valid C2 server, infected devices connect to, and request the IP address of a server from, the above-mentioned domain [2].

Connection to Proxy

After sending host and infection details via HTTP and receiving commands from the C2 server, affected devices were frequently observed initiating activity to join the Sock5Systemz botnet. Infected hosts would first make HTTP GET requests to an IP identified as Socks5Systemz’s proxy checker application, usually sending the URI “proxy-activity.txt” to the domain over the HTTP protocol. This likely represents an additional validation check to confirm that the infected device is ready to join the botnet.

Figure 5: Cyber AI Analyst investigation detailing HTTP GET requests over port 80 to the Socks5Systemz Proxy Checker Application.

Following the final validation checks, devices would then attempt TCP connections to a range of IPs, which have been associated with BackConnect proxy servers, over port 1074. At this point, the device is able to receive commands from actors who login to and operate the corresponding BackConnect server. This BackConnect server will transmit traffic from the user renting the segment of the botnet [2].

Darktrace observed a range of activity associated with this stage of the attack, including the use of new or unusual user agents, connections to suspicious IPs, and other anomalous external connectivity which represented a deviation from affected devices’ expected behavior.

Additional Activities Following Proxy Addition

The Darktrace Threat Research team found evidence of the possible deployment of additional malware strains during their investigation into devices affected by Socks5Systemz. IoCs associated with both the Amadey and PrivateLoader loader malware strains, both of which are known to distribute Socks5Systemz, were also observed on affected devices. Additionally, Darktrace observed multiple infected systems performing cryptocurrency mining operations around the time of the Sock5Systemz compromise, utilizing the MinerGate protocol to conduct login and job functions, as well as making DNS requests for mining pools.

While such behavior would fall outside of the expected activity for Socks5Systemz and cannot be definitively attributed to it, Darktrace did observe devices affected by the botnet performing additional malicious downloads and operations during its investigations.

Conclusion

Ultimately, Darktrace’s anomaly-based approach to threat detection enabled it to effectively identify and alert for malicious Socks5Systemz botnet activity long before external researchers had documented its IoCs and tactics, techniques, and procedures (TTPs).  

In fact, Darktrace not only identified multiple distinct attack phases later outlined in external research but also uncovered deviations from these expected patterns of behavior. By proactively detecting emerging threats through anomaly detection rather than relying on existing threat intelligence, Darktrace is well positioned to detect evolving threats like Socks5Systemz, regardless of what their future iterations might look like.

Faced with the threat of persistent botnets, it is crucial for organizations to detect malicious activity in its early stages before additional devices are compromised, making it increasingly difficult to remediate. Darktrace’s suite of products enables the swift and effective detection of such threats. Moreover, when enabled in autonomous response mode, Darktrace RESPOND is uniquely positioned to take immediate, targeted actions to contain these attacks from the onset.

Credit to Adam Potter, Cyber Security Analyst, Anna Gilbertson, Cyber Security Analyst

Appendices

DETECT Model Breaches

  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Anomalous Connection / Multiple Connections to New External TCP Port
  • Compromise / Beaconing Activity To External Rare
  • Compromise / DGA Beacon
  • Compromise / Beacon to Young Endpoint
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / HTTP Beaconing to Rare Destination
  • Compromise / Quick and Regular Windows HTTP Beaconing
  • Compromise / Agent Beacon (Medium Period)
  • Compromise / Agent Beacon (Long Period)
  • Device / New User Agent
  • Device / New User Agent and New IP

Cyber AI Analyst Incidents

  • Possible HTTP Command and Control
  • Possible HTTP Command and Control to Multiple Endpoints
  • Unusual Repeated Connections
  • Unusual Repeated Connections to Multiple Endpoints
  • Multiple DNS Requests for Algorithmically Generated Domains

Indicators of Compromise

IoC - Type - Description

185.141.63[.]172 - IP Address - Socks5Systemz C2 Endpoint

193.242.211[.]141 - IP Address - Socks5Systemz C2 Endpoint

109.230.199[.]181 - IP Address - Socks5Systemz C2 Endpoint

109.236.88[.]134 - IP Address - Socks5Systemz C2 Endpoint

217.23.5[.]14 - IP Address - Socks5Systemz Proxy Checker App

88.80.148[.]8 - IP Address - Socks5Systemz Backconnect Endpoint

88.80.148[.]219 - IP Address - Socks5Systemz Backconnect Endpoint

185.141.63[.]4 - IP Address - Socks5Systemz Backconnect Endpoint

185.141.63[.]2 - IP Address - Socks5Systemz Backconnect Endpoint

195.154.188[.]211 - IP Address - Socks5Systemz Backconnect Endpoint

91.92.111[.]132 - IP Address - Socks5Systemz Backconnect Endpoint

91.121.30[.]185 - IP Address - Socks5Systemz Backconnect Endpoint

94.23.58[.]173 - IP Address - Socks5Systemz Backconnect Endpoint

37.187.148[.]204 - IP Address - Socks5Systemz Backconnect Endpoint

188.165.192[.]18 - IP Address - Socks5Systemz Backconnect Endpoint

/single.php?c=<RC4 data hex encoded> - URI - Socks5Systemz HTTP GET Request

/sign/<RC4 data hex encoded> - URI - Socks5Systemz HTTP GET Request

/proxy-activity.txt - URI - Socks5Systemz HTTP GET Request

datasheet[.]fun - Hostname - Socks5Systemz C2 Endpoint

bddns[.]cc - Hostname - Socks5Systemz C2 Endpoint

send-monitoring[.]bit - Hostname - Socks5Systemz C2 Endpoint

MITRE ATT&CK Mapping

Command and Control

T1071 - Application Layer Protocol

T1071.001 – Web protocols

T1568 – Dynamic Resolution

T1568.002 – Domain Generation Algorithms

T1132 – Data Encoding

T1132 – Non-Standard Encoding

T1090 – Proxy

T1090.002 – External Proxy

Exfiltration

T1041 – Exfiltration over C2 channel

Impact

T1496 – Resource Hijacking

References

1. https://www.bleepingcomputer.com/news/security/socks5systemz-proxy-service-infects-10-000-systems-worldwide/

2. https://www.bitsight.com/blog/unveiling-socks5systemz-rise-new-proxy-service-privateloader-and-amadey

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
Adam Potter
Senior Cyber Analyst

More in this series

No items found.

Blog

/

AI

/

July 15, 2026

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

Default blog imageDefault blog image

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

Continue reading
About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

Blog

/

AI

/

July 10, 2026

AIインフラがアタックサーフェスの一部に

Default blog imageDefault blog image

AIインフラとアタックサーフェスの進化

多くの組織が生成AIを実運用環境に導入するなかで、企業のクラウド環境内に新たなインフラのレイヤーが出現しています。それはAIゲートウェイです。AIゲートウェイはユーザー、アプリケーション、基盤モデルの間に位置し、多くの場合クラウドの特権アクセスを保持し、さまざまなAIサービスへのアクセスを大規模に管理しています。

AIゲートウェイとは?

AIゲートウェイはユーザー、アプリケーション、基盤モデルの間に位置し、多くの場合クラウドの特権アクセスを保持し、さまざまなAIサービスへのアクセスを大規模に管理しています。

こうした役割から、AIゲートウェイは企業のアタックサーフェスのますます重要な一部になりつつあります。AIゲートウェイが侵害されれば、攻撃者に対して計算リソースへのアクセスだけでなく、クラウドアイデンティティ、モデルサービス、機密性の高いプロンプト、そして他の接続されたシステムへのアクセスも提供してしまいます。

このブログでは、Amazon Bedrock サービスに接続されたAIゲートウェイが侵害され、その後暗号通貨マイニングインフラとの通信が観測された事例をダークトレースがどのように調査したかを解説します。問題のインスタンスは、その構成、ならびに関連するIAM(Identity and Access Management)ロールから、Amazon BedrockでホスティングされるAIサービスへのゲートウェイとして機能していることがわかりました。疑わしい侵害アクティビティが発生した後、このホストは既知の暗号通貨マイニングインフラに繰り返し通信を行い、その後シャットダウンされた様子が観測されました。Darktrace はこのアクティビティを検知し、Enhanced MonitoringおよびManaged Threat Detectionサービスを通じてエスカレーションを行いました。

この事例では最終的影響は不正な暗号通貨マイニングでしたが、このインシデントが注目に値するのはその発生場所です。侵害されたアセットは、クラウドインフラ、アイデンティティ、各種AIサービスの交差する場所に位置していました。最近の調査では、LiteLLM等のAIゲートウェイが、認証情報、モデルへのアクセス、クラウド権限を中央管理するその能力から、攻撃者にとって魅力的な標的となる可能性が明らかになっています。このアクティビティと公開されているLiteLLM脆弱性を直接結びつける証拠は見つかっていませんが、このインシデントは、AIインフラを個別のアプリケーション層として見るのではなく、重要なアタックサーフェスの一部として扱う必要性があることを表しています[1]。

暗号通貨マイニングがクラウド侵害後のアクティビティとしてよく見られる背景

暗号通貨マイニングはクラウド環境において、侵害後のアクティビティとして収益性の高いものとなり得ます。クラウド資産にアクセスできるようになった後、攻撃者はマイニングソフトウェアを展開して被害者の計算リソースを悪用し金銭的利益を得ることができます。この種のアクティビティは多くの場合機会主義的なものであり、露出したサービス、弱い認証情報、漏洩したアクセスキー、脆弱なアプリケーション、あるいはクラウドワークロードの設定ミスなどを標的として実行されます。

典型的なクラウド上での暗号通貨マイニング侵入には次のようなアクティビティが含まれます:

  • 露出したあるいは脆弱なクラウドインフラの特定
  • 露出したサービス、認証情報、またはアプリケーションの脆弱性を通じたアクセスの獲得
  • マイニングソフトウェアのダウンロードおよび実行
  • マイニングプールインフラへのアウトバウンド接続を繰り返し確立
  • アクティビティが検知され停止されるまで継続して計算リソースを消費

この事例において注目すべき要素は暗号通貨マイニングだけではありません。それが発生した場所が、AI関連アクティビティをサポートするクラウドインフラ上だったことです。この事例は、AIサービスを実現するためのアセットも、よくあるクラウド侵害リスクにさらされる可能性があることを示しています。

Amazon Bedrockに接続されたAIゲートウェイの侵害を調査

2026年6月12日、DarktraceはLiteLLM-Proxyという名前のAmazon Web Service (AWS) EC2インスタンスから暗号通貨マイニング発生中とみられるアクティビティを観測しました。このインスタンスはLiteLLMアクティビティをサポートしており、Amazon Bedrockリソースへのアクセス権を有するインスタンスプロファイルと関連付けられていました。  

AIゲートウェイは大規模言語モデルへのアクセスを中央管理するよう設計されており、多くの場合AIアプリケーションに対する認証、ルーティング、ログ、ポリシー適用を扱っています。セキュリティの視点から見ると、クラウド権限、モデルアクセス、アプリケーションワークフローを単一の制御ポイントに集約する役割も果たしています。その結果、AIゲートウェイの侵害は、侵害されたホストだけにとどまらない影響を及ぼす可能性があります。

確定的な初期アクセスベクトルは確認できませんでしたが、このアクティビティはインターネットに接続されているシステムの侵害でよく見られる次のような順序に従っていました。ブルートフォースアクセス、ペイロードの投下、そしてマイニングプールインフラに対する繰り返しのアウトバウンド接続です。

ステージ1: インターネットに露出したSSHからの初期アクセス

暗号通貨マイニングアクティビティが観測される前、LiteLLM-Proxy EC2インスタンスはSSH(ポート22)が0.0.0.0/0に対して開かれ、外部に公開されていました。

図1:EC2インスタンスがSSHポート22に対してすべてのインバウンドトラフィックを許可している設定ミスをDarktraceが警告

暗号通貨マイニングアクティビティに先立って、Darktraceはこのインスタンスに対する大量のインバウンド接続の試みが外部IPアドレス(主に145.241.123[.]102)からポート22に対して行われていることを観測しました。これはブルートフォースアクティビティを示唆するものです [2]。これらの接続の多くは短命であり、数秒しか続いておらず、スキャニングまたはログインの失敗を示していました。

図2:Darktraceがデバイスのポート22に対する不審なインバウンド接続試行を検知

入手できたテレメトリーではこれらのインバウンドSSH接続のいずれかが認証の成功につながったかどうかの確認に至らず、このアクティビティが初期アクセスベクトルであると断定することはできませんでした。しかしながら、SSHの露出、外部IPアドレスからのインバウンド接続、それに続くマイニングアクティビティは、SSHがアクセス経路の可能性が高いことを示唆しています。

ステージ2: AIゲートウェイへのXMRigマルウェアのダウンロード

最初に観測されたマイニングプールへの接続の後、このEC2インスタンスは3.42 MBのデータをポート80上のHTTP接続を介して外部エンドポイント185.62.1[.]8にダウンロードしました。このエンドポイントは暗号通貨マイニングマルウェアXMRigを含むZIPファイルをホスティングしていました[3][4]。ホストレベルのログは入手できなかったため、ダークトレースはマイニングツールがどのように実行されたか、あるいは前のSSHアクティビティがペイロード投下を直接的に可能にしたかどうかを確認できませんでした。しかしながら、ダウンロードのタイミングとその後ほどなくマイニングプールへの接続が繰り返されたことは、このインスタンスが侵害されて不正な計算アクティビティに使われたという評価を裏付けています。

ステージ3 – 侵害されたAIゲートウェイが暗号通貨マイニングインフラと通信

わずか数分後、DarktraceはLiteLLM-ProxyEC2インスタンスがHTTPs(ポート443)でホスト名pool.hasvault[.]proに対して接続していることを確認しました。最初の接続の後、同じホスト名に対して繰り返しアウトバウンド接続が観測されました。これは、侵害されたホストがマイニングインフラと通信しワークを受け取り、結果を送信するという、暗号通貨マイニングプールとの通信のパターンと一致しています。

このアクティビティがDarktraceのEnhanced Monitoringモデル“Compromise / HighPriority Crypto Currency Mining”をトリガーし、ダークトレースのSOCにより顧客に対してエスカレーションされました。また、このアクティビティはCyber AI Analystによって分析され、関連するイベントが1つの調査ナラティブにまとめられました。これにより、影響を受けたクラウドアセットからマニングプールへの繰り返しの接続を特定することができました。

図3:CyberAI Analystによる暗号通貨マイニングアクティビティの調査  

ポート443上のHTTPSの使用にも注目すべきです。なぜならば、単独で見れば、このトラフィックそのものは疑わしく見えないかもしれないからです。しかしこのケースでは、接続先、接続の量、そして類似のアクティビティが他にないことなどが、この通信を疑わしいものとして特定するのに必要な、動作のコンテキストを提供することになりました。

ステージ4: Managed Threat Detectionサービスによるリソース乱用の特定

暗号通貨マイニングアクティビティがダークトレースのManaged Threat Detectionサービスにより検知され、ダークトレースのSOCによりレビューされました。レビューの結果、このアクティビティは顧客向けにエスカレーションされました。このエスカレーションにより、顧客はAWS環境で現在発生中のリソースの乱用について、タイムリーな通知を受けることができました。

ステージ5: クラウド認証情報の不正使用とみられる疑わしいIAMアクティビティ

これとは別に、6月13日、Darktraceは別のIAMユーザーから発生した疑わしいアクティビティを検知しました。

図4: DarktraceのAdvanced Search機能が別のIAMユーザーが実行した疑わしいアクティビティをハイライト

まず、このユーザーは “GetSendQuota”イベントを試行している様子が見られました。このアクションは少なくとも過去3か月間にこのアカウントによって実行されたことのないアクションです。また、このコマンドのソースIPアドレスは14.176.1[.]47でした。地理位置情報はベトナムであり、このユーザーのアクティビティがAmazon IPアドレスから最も多く見られた場所です。さらに、このアクティビティに対してAWS CLIが使用されており、これもこのユーザーにとって通常とは異なる振る舞いでした。このことは、Darktraceの“IaaS / Unusual Activity / UnusualAWS CLI Activity”モデルによって検知されました。

図5: Darktraceによる “GetSendQuota” イベントの検知

このIAMユーザーからは、長期アクセスキーを使った疑わしいアクティビティがさらに観測されました。中でも、“InvokeModel” および “ListFoundationModels”コマンドの失敗が検知されており、モデル列挙や起動などAmazon Bedrockサービスとのやり取りを試行したことがわかります。これは前日観測されたLiteLLM侵害への関連を思わせますが、2つのイベントを確定的に結びつける証拠は不十分でした。

“CreateUser”コマンドの試行も注目に値します。なぜなら要求されたユーザー名は意味が薄いものであり、新しいアカウントを作成することにより永続性を確立する試みと見られるからです。このアクティビティはDarktraceのモデル“IaaS / Admin / New AWS UserAccount Creation”をトリガーしました。

図6:Darktraceによる“CreateUser” イベントの検知

2つのインシデント間に結びつきは確認できなかったものの、このIAMアクティビティには重要な意味があります。これは、クラウド侵害の調査においてワークロードのテレメトリーとコントロールプレーンのテレメトリーの両方を取り入れることの重要性を表しています。EC2暗号通貨マイニングアクティビティが計算リソースの乱用を示す一方、IAMアクティビティは認証情報の侵害や長期アクセスキーの不正使用、そしてクラウトサービスの不正使用の可能性を示唆しているからです。

AIインフラ保護のための重要な教訓

このインシデントの重大性は暗号通貨マイニングアクティビティそのものではなく、それが発生した場所にあります。侵害されたシステムはAmazon Bedrockサービスへのアクセス権を持つAIゲートウェイとして機能し、クラウドインフラ、アイデンティティ、そしてさまざまなAIオペレーションの交差する場所に位置していました。組織がAI機能を実運用環境に導入していくなかで、これらのプラットフォームは、露出したサービス、認証情報窃取、クラウドの設定ミスなどを通じて攻撃者がすでに狙っているアタックサーフェスの一部となりつつあるのです。

このケースでは詳細な侵入経路は特定されておらず、ワークロードの侵害と調査中に検知された疑わしいIAMアクティビティの間に決定的なつながりは確認されませんでしたが、これらのイベントは全体的な現状を裏付けています。つまり、AIインフラは個別のテクノロジースタックとして扱うのではなく、クラウド環境全体の一部として保護しなければならないとうことです。

このケースでは、最も目立った侵害の兆候は暗号通貨マイニングインフラとの通信でした。しかしここで得られたより重要な教訓は、このインシデントの全貌が理解される前にDarktraceのビヘイビア分析により明らかになった、高い権限を持つAI関連アセットを取り巻くリスクです。AIゲートウェイによりクラウド権限、モデルアクセス、アプリケーションワークフローがますます集約されるなかで、防御者は個別のアラートに集中するよりも、ワークロード、アイデンティティ、サービスの間でどのように動作がつながっているかを理解することに重点を置く必要があるでしょう。

協力:Angel Arribas Lopez (Associate Principal Cyber Analyst)、Nathaniel Jones (Field CISO/VP Threat Research)、Emma Foulger (Global Threat Ops)、Mark Turner(Security Researcher)

編集:Ryan Traill (Content Manager)

付録

Darktraceによるモデル検知結果

·       Compromise / High Priority Crypto Currency Mining

·       Compromise / Monero Mining

·       Device / Internet Facing Device with High Priority Alert

·       IaaS / Unusual Activity / Unusual AWS CLI Activity

·       IaaS / Admin / New AWS User Account Creation

MITRE ATT&CK マッピング

初期アクセス – 外部リモートサービス – T1133

初期アクセス – 有効なアカウント – T1078

実行 – コマンドおよびスクリプトインタプリタ – T1059

永続化 – アカウント作成 – T1136

探索 – クラウドサービス探索 – T1526

影響 – リソースハイジャッキング– T1496

参考資料

[1] https://docs.litellm.ai/blog/security-update-march-2026

[2] https://www.abuseipdb.com/check/145.241.123.102

[3] https://urlscan.io/search/#185.62.1.8

[4] https://www.virustotal.com/gui/file/85de36ff66fae9f4b059cbedf6d36e017ebc26c828f99f911a96e78636f21200/community

Continue reading
About the author
Angel Arribas Lopez
Associate Principal Cyber Analyst
あなたのデータ × DarktraceのAI
唯一無二のDarktrace AIで、ネットワークセキュリティを次の次元へ