Employee Uses Rogue Devices & Attempts to Steal Passwords
Learn how Darktrace detected rogue devices on the network of a healthcare provider and how Darktrace ensured no users fell victim to the attack.
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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.
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26
Nov 2017
Darktrace recently detected two rogue devices on the network of a major healthcare provider. They were brought onto the network by a trusted employee, who – for reasons still unknown – was attempting to harvest user credentials and profile the network’s defenses.
Darktrace’s AI algorithms had built a detailed understanding of the organization’s normal network activity and digital infrastructure. When the two new devices entered the network and sent ‘Redirect Datagram for the host’ messages to the subnet router, Darktrace identified the anomaly and raised an alert in real time. This represented the first of three anomalies:
Two unknown Raspberry Pis are introduced to the network Based on the MAC addresses, the newly introduced devices were determined to be two Raspberry Pis. Once on the network, they began acting like gateways, which use remote hosts to send data packets on alternative routes.
It was initially believed that the insider was using the devices to engage in ARP spoofing. However, the subnet router did not respond to the messages.
The devices attempt to redirect users to a fake security survey The second anomaly occurred when the devices began beaconing to a rare external endpoint, which resolved to Amazon cloud services. This activity is typically seen in attempted communications with a command-and-control center, but there was no returning inbound traffic.
Instead, the rare destination turned out to be a website, which was identical to an internal website being used to host a security survey. Before accessing the survey, employees needed to enter their user credentials.
In addition to harvesting user credentials, the survey was asking a series of questions that would have been extremely useful for an attacker. The survey included questions designed to learn the status of their anti-virus and firewalls, and whether users were using the same passwords across multiple services.
The insider tries to hide the devices via DHCP allocation requests The final anomaly came when one of the devices made a DHCP allocation request for an IP address on a separate subnet. It had the same hostname of the infringing device, but a new MAC address.
Essentially, the insider was attempting to hide the devices by manually changing their IP addresses through DHCP release and allocation requests.
Each of these anomalies represented a subtle deviation from the organization’s normal ‘pattern of life’. By correlating these weak indicators of threat, Darktrace was able to discover a larger pattern that revealed the whole story: a malicious insider had smuggled Raspberry Pis onto the network to lure users to a fake website, steal their credentials, and test the network’s defenses, all while remaining hidden from network defenses.
By raising alerts in real time, Darktrace helped ensure that no users fell victim to the attack. Soon after, the Pis disappeared from the network. While the culprit was never caught, the organization has yet to experience a similar threat, indicating that the insider either left the organization, or remains in hiding.
Since the incident, the healthcare provider has undergone a restructuring of its network, and they’ve adopted a host of new IoT devices. Darktrace’s algorithms are continuously learning and re-learning normal, so if the malicious insider were to re-emerge, Darktrace would immediately detect their presence on the network.
No items found.
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.
“I wonder if running that tool influenced the connectivity in some way.”
“I might need to target down or reboot, but first, I'll investigate the network using ARP. I wonder if ARP is responding properly. If I see "ip neigh stale," that could indicate a problem. I think arping might help in this situation, but I’ll need to keep an eye on how the network is behaving. It's all about figuring out where the issue lies!”
図10：エージェントの異常なLDAPアクティビティに対してトリガーされたこのLinuxサーバーに対するモデル検知は、Cyber AI Analystによって自律的に調査され、その調査結果にはエージェントの異常なSMBセッションアクティビティに関する内容が含まれ、Cyber AIAnalystインシデントに記録されました。
DarktraceのCyber AI Analystは、エージェントのアクティビティによってトリガーされたモデル検知を自律的に調査し、その調査結果を1つの重要なインシデントレポートにまとめました。インシデント報告書の内容は、エージェントによるネットワークスキャンから始まりコーディング演習内容のSMBベースの上書きで終わる、エージェントの行動の各段階を詳しくカバーしています。
図20：Cyber AI Analystは、モデル検知に対する自律的な調査を通じて得られた所見をつなぎ合わせ、エージェントの進行状況のタイムラインを作成し、アクティビティを視覚的に表したグラフとともに提供しました。
Cyber Verification Program下のAnthropic Opus 4.6モデルなど、他のフロンティアモデルを使用したエージェントも、Darktraceのシミュレートされた環境で同じ設定とタスクを使ってテストされました。反復の度に、エージェントは演習の内容または採点プログラムを変更して不正を行おうとしました。基盤となるモデルに関係なく、エージェントは自分が置かれた環境を侵害することで、100％のスコアを達成する方法を見つけることに成功していました。これらのエージェントによるハッキングは、アクティビティの内容こそ違っていましたが、すべてその環境のベースラインの振る舞いから大きく逸脱した動作を引き起こしていました。これらの逸脱はDarktraceプラットフォームに十分な検知の機会を提供し、エージェントの逃げ場はありませんでした。
エージェンティックハーネスは会話履歴をローカルに保存しますが、ダークトレースの研究者たちは、保存されたAIの応答が本当にモデルによって生成されたものであるかどうかの検証が行われていないことを発見しました。また、この設計上の選択がAnthropic Claude Code、AWS Kiro-CLI、OpenAI Codex、およびオープンソースのPiにわたって共通していることを確認しました。
会話履歴は多くの場合クライアント側に保存されます。例えば、Anthropic Claude Code、OpenAI Codex、AWS Kiro-CLI、Piなどでもそうです。そのため、ユーザーはセッションを自由に再開でき、一部の製品にはその履歴を操作する機能が組み込まれています。たとえば、会話の中の特定の時点に戻り、送信したメッセージを編集して、会話を別の軌道へ進めていくことができます。きわめて重要な点として、私たちが調査したすべてのケースにおいて、保存されているAIの応答が対応するモデルから生成されたものであり改ざんされていないことの検証は行われていませんでした。
