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東京オリンピックの妨害を狙ったIoT攻撃をAIが無害化

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19
Sep 2021
19
Sep 2021
When a cyber-attack struck a national sporting body one week before the start of the Tokyo Olympics, Darktrace was on hand to autonomously stop the threat. This blog breaks down the attack in detail.

セキュリティにおける最大の問題の1つは、大規模な侵害が発生した際の高ストレス事態にどう対処するかです。するべき事はあまりにも多く、時間はあまりにも少ないのです。あらゆるCISOにとっての悪夢のシナリオは、組織にとってクリティカルな瞬間にこれが発生することです。たとえば、重要な企業買収、大事なニュース発表、あるいはこのケースのように、何百万もの視聴者を引き付ける世界的なスポーツイベントなどです。

脅威アクター達はしばしばこれらのイベントにかかるプレッシャーを悪用して、中断を引き起こしあるいは大金をせしめようとするのです。スポーツイベント、特にF1レース、スーパーボウル、そしてオリンピックは犯罪者達の大きな関心を集めます。

試合の開始

今年のオリンピックではいくつかの攻撃とデータ侵害が記録されています。これには、バレーボールの解説者が自分のコンピューターパスワードを、オンエア中であることに気づかずに同僚に尋ねたというインシデントも含まれています。

Darktrace が発見したより悪質なケースとしては、オリンピックに直接関与していたある競技連盟に対して密かにRaspberry Piデバイスを仕込み、機密データを盗み出そうとした事例がありました。インシデントはオリンピック開幕1週間前に発生し、この時期にデータ流出が起こっていれば組織の評判、計画の秘密性、ひいてはアスリートの安全にも多大な影響が発生していた可能性があります。

Darktrace AIは組織の「自己」に対する変化する理解に基づいてこのアクティビティを悪意あるものと識別し、Darktraceの自動対処機能であるAntigenaがマシンスピードでアクションを起こして脅威を遮断、人間のセキュリティチームが事態に追いつき攻撃を無害化するための貴重な時間を作り出しました。

以下にこの攻撃を分解して説明します。

1:合計の滞留時間は3日間

攻撃の分解

7月15日 14:09- 最初の侵入

この組織のデジタル環境に、不正なRaspberry Piデバイスが接続されましたが、このデバイスは組織内の命名規則に従ったような名前で偽装されていました。小型のIoTデバイスであるRaspberry Piは簡単に隠すことができ、大規模な環境においては物理的に見つけることが困難です。Raspberry Piは 2018年のNASAへの侵入を含め、これまでもさまざまな有名なハッキング事例で使われています。

IoT devices – from printers to fish tanks – pose a serious risk to security, as they can be exploited to gather information, move laterally, and escalate privileges.

7月15日 15:25- 外部VPNアクティビティ

異常なUDP接続が外部エンドポイントに対して1194番ポートを介して行われました(Open VPNアクティビティ)。 URIは、デバイスがOpen VPNコンフィギュレーションファイルに関係あると思われるデータをダウンロードしたことを示しています。これは、データ抜き出しなどの悪意あるアクティビティのためにセキュアなチャネルを確立しようとしていたものかも知れません。

送信VPNを確立することにより、攻撃者は自らのアクティビティを見えにくくし、この組織のシグネチャベース検知セキュリティをすり抜けました。システムはこの暗号化されたトラフィックを検知できなかったのです。Antigenaは暗号化とは関係なくこの疑わしい接続を即座にブロックしました。このアクティビティが新しいデバイスの「生活パターン」から逸脱していることを識別したためです。

7月15日 16:04 - C2 アクティビティの可能性

Raspberry Piはすぐに、新しい外部エンドポイントに対して繰り返しHTTP接続を開始し、オクテットストリーム、ツールをダウンロードしました。 このアクティビティはWebブラウザではなく、スタンドアロンソフトウェアプロセスにより開始されたように見えます。

Darktraceはこのデバイスが同じエンドポイントに対して不審な外部データ転送を行っており、新しいロケーションとデバイスの名前についてのコールホームデータが含まれると思われる7.5 MBものデータをアップロードしていることを明らかにしました。

7月15日 16:41- 内部偵察

デバイスは3つの内部IPアドレスに対しさまざまなポートを使ってTCPスキャニングを実行しました。ネットワークスキャンは3台の内部サーバーのみに対するものでしたが、このアクティビティはDarktraceによって疑わしい内部接続の増加と内部接続の失敗として識別されました。

Antigena はこのRaspberry Piがスキャニングアクティビティに関係していたポートを使って内部接続を行うのを即座に停止させるとともに、デバイスの「生活パターン」を強制しました。

Darktraceによるネットワークスキャニング検知を可能にしたコンポーネントを示すデバイスイベントログ

7月15日 18:14 - 複数の内部偵察テクニック

Raspberry Piはその後SMBポート445番を使って多数のデバイスをスキャンし、古いSMBバージョン1プロトコルの不審な使用が見られました。これは悪用可能な脆弱性を探すためのより深い偵察を行ったものと思われます。

このスキャニングアクティビティと、安全ではないSMBv1プロトコルの使用に反応してAntigenaはこのソースデバイスからデスティネーションIPへの接続を1時間に渡りブロックしました。

分後、デバイスはオープンソースの脆弱性スキャナー、Nmapへの接続を行いました。Nmapは脆弱性スキャンの目的で正しく使われることもあるため、従来型のセキュリティツールでは警告されないことがしばしばです。しかし、DarktraceのAIはこのツールの使用がきわめて異常であることを検知し、10分間に渡ってすべての送信トラフィックをブロックしました。

7月15日 22:03- 最終的な偵察

3時間後、Raspberry Pi は6個の外部IPに対して別のネットワークスキャンを開始しました。これは最終的なデータ抜き出しの準備を行ったものです。Antigenaはデバイスが接続しようとしていたこの外部IPに対する正確なブロックで瞬時に対応し、データの抜き出しを阻止しました。

異常なUDP接続が外部エンドポイントに対して1194番ポートを介して行われました(Open VPNアクティビティ)。 URIは、デバイスがOpen VPNコンフィギュレーションファイルに関係あると思われるデータをダウンロードしたことを示しています。これは、データ抜き出しなどの悪意あるアクティビティのためにセキュアなチャネルを確立しようとしていたものかも知れません。

この時点までに、Antigenaはセキュリティチームが対処を行うのに十分な時間を稼いでいました。チームはこのRaspberry Piに対して、デバイスの設置されている物理的な場所を見つけ出しネットワークから切断するまでの間、Antigenaの隔離ルール(Antigenaが取ることのできる最も厳しいアクション)を適用しました。

AI Analystはこのインシデントをどうつなぎ合わせたか

Cyber AI Analyst は、この攻撃の3つの主要な場面について自律的にレポートを生成しました:

  • Unusual External Data Transfer
  • HTTP コマンド&コントロールの可能性
  • TCP Scanning of Multiple Devices (the attempted data exfiltration)

Cyber AI Analystは複数日に渡るアクティビティをつなぎ合わせましたが、人間のアナリストであれば見逃されていた可能性も大いにあります。AI はスキャニングアクティビティの範囲も含め、重要な情報を提供しました。こうした情報は人手で計算しようとすると時間がかかるものです。

図3:C2アクティビティの可能性を提示するCyber AI Analystの画面

自律遮断技術

Antigenaは疑わしい挙動を無害化するために、一貫して的を絞ったアクションを取り、通常の業務は妨げられることなく継続することができました。

広範囲なブロックを行うのではなく、 Antigenaは状況に応じて様々なきめ細かい対応を実施し、常に脅威に対処する上で最も小さくて済むアクションを取りました。

図4:データ抜き出しの試みと、Antigenaによる的を絞ったアクションを示すDarktraceのUI

Raspberry Pi: IoTの脅威

オリンピックは206の国と11,000人のアスリートが参加するイベントであり、多くのハクティビスト、犯罪者グループ、国家からの攻撃に直面しており、また多数の放送局が遠方で番組を作り、何百万人もの人が自宅で視聴しています。オリンピックに関わる組織にはこの重圧に耐えられるセキュリティソリューションが必要でした。

このような世界最大のイベントにおいても、脅威は非常に小さなところからやってくることがあります。許可されていないIoTデバイスを検知し、デジタルエステート内のすべてのアクティビティに対する可視性を維持する能力はきわめて重要です。

Autonomous Responseは予期せぬイベントに対する防御を提供し、ユーザーからの入力を何ら必要とすることなく悪意あるアクティビティをマシンスピードで阻止します。このことは特にリソースが逼迫した社内セキュリティチームにとって、迅速な対処と修正のために必要です。システムを防御し攻撃者に先んじることにかけては、AIが常に勝者となります。

この脅威についての考察はDarktraceアナリストEmma FoulgerおよびGreg Chapmanが協力しました

2台のRaspberry Piデバイスがヘルスケア企業を感染させた事例について知る

Darktraceによるモデル検知:

  • Compromise / Ransomware / Suspicious SMB Activity
  • Tags / New Raspberry Pi Device
  • Device / Network Scan
  • Unusual Activity / Unusual Raspberry Pi Activity
  • Antigena / Network / Insider Threat / Antigena Network Scan Block
  • Device / Suspicious Network Scan Activity
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
  • Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach
  • Device / Suspicious SMB Scanning Activity
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
  • Device / Attack and Recon Tools
  • Device / New Device with Attack Tools
  • Device / Anomalous Nmap Activity
  • Device / External Network Scan
  • Device / SMB Session Bruteforce
  • Antigena / Network / Manual / Block All Outgoing Connections
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.
AUTHOR
ABOUT ThE AUTHOR
Oakley Cox
Analyst Technical Director, APAC

Oakley is a technical expert with 5 years’ experience as a Cyber Analyst. After leading a team of Cyber Analysts at the Cambridge headquarters, he relocated to New Zealand and now oversees the defense of critical infrastructure and industrial control systems across the APAC region. His research into cyber-physical security has been published by Cyber Security journals and CISA. Oakley is GIAC certified in Response and Industrial Defense (GRID), and has a Doctorate (PhD) from the University of Oxford.

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Gootloader Malware: Detecting and Containing Multi-Functional Threats with Darktrace

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15
Feb 2024

What is multi-functional malware?

While traditional malware variants were designed with one specific objective in mind, the emergence of multi-functional malware, such as loader malware, means that organizations are likely to be confronted with multiple malicious tools and strains of malware at once. These threats often have non-linear attack patterns and kill chains that can quickly adapt and progress quicker than human security teams are able to react. Therefore, it is more important than ever for organizations to adopt an anomaly approach to combat increasingly versatile and fast-moving threats.

Example of Multi-functional malware

One example of a multi-functional malware recently observed by Darktrace can be seen in Gootloader, a multi-payload loader variant that has been observed in the wild since 2020. It is known to primarily target Windows-based systems across multiple industries in the US, Canada, France, Germany, and South Korea [1].  

How does Gootloader malware work?

Once installed on a target network, Gootloader can download additional malicious payloads that allow threat actors to carry out a range of harmful activities, such as stealing sensitive information or encrypting files for ransom.

The Gootloader malware is known to infect networks via search engine optimization (SEO) poisoning, directing users searching for legitimate documents to compromised websites hosting a malicious payload masquerading as the desired file.

If the malware remains undetected, it paves the way for a second stage payload known as Gootkit, which functions as a banking trojan and information-stealer, or other malware tools including Cobalt Strike and Osiris [2].

Darktrace detection of Gootloader malware

In late 2023, Darktrace observed one instance of Gootloader affecting a customer in the US. Thanks to its anomaly-focused approach, Darktrace DETECT™ quickly identified the anomalous activity surrounding this emerging attack and brought it to the immediate attention of the customer’s security team. All the while, Darktrace RESPOND™ was in place and able to autonomously intervene, containing the suspicious activity and ensuring the Gootloader compromise could not progress any further.

In September 2023, Darktrace identified an instance of the Gootloader malware attempting to propagate within the network of a customer in the US. Darktrace identified the first indications of the compromise when it detected a device beaconing to an unusual external location and performing network scanning. Following this, the device was observed making additional command-and-control (C2) connections, before finally downloading an executable (.exe) file which likely represented the download of a further malicious payload.

As this customer had subscribed to the Proactive Notification Service (PTN), the suspicious activity was escalated to the Darktrace Security Operations Center (SOC) for further investigation by Darktrace’s expert analysts. The SOC team were able to promptly triage the incident and advise urgent follow-up actions.

Gootloader Attack Overview

Figure 1: Timeline of Anomalous Activities seen on the breach device.

Initial Beaconing and Scanning Activity

On September 21, 2023, Darktrace observed the first indications of compromise on the network when a device began to make regular connections to an external endpoint that was considered extremely rare for the network, namely ‘analyzetest[.]ir’.

Although the endpoint did not overtly seem malicious in nature (it appeared to be related to laboratory testing), Darktrace recognized that it had never previously been seen on the customer’s network and therefore should be treated with caution.  This initial beaconing activity was just the beginning of the malicious C2 communications, with several additional instances of beaconing detected to numerous suspicious endpoints, including funadhoo.gov[.]mv, tdgroup[.]ru’ and ‘army.mil[.]ng.

Figure 2: Initial beaconing activity detected on the breach device.

Soon thereafter, Darktrace detected the device performing internal reconnaissance, with an unusually large number of connections to other internal locations observed. This scanning activity appeared to primarily be targeting the SMB protocol by scanning port 445.

Within seconds of DETECT’s detection of this suspicious SMB scanning activity, Darktrace RESPOND moved to contain the compromise by blocking the device from connecting to port 445 and enforcing its ‘pattern of life’. Darktrace’s Self-Learning AI enables it to learn a device’s normal behavior and recognize if it deviates from this; by enforcing a pattern of life on an affected device, malicious activity is inhibited but the device is allowed to continue its expected activity, minimizing disruption to business operations.

Figure 3: The breach device Model Breach Event Log showing Darktrace DETECT identifying suspicious SMB scanning activity and the corresponding RESPOND actions.

Following the initial detection of this anomalous activity, Darktrace’s Cyber AI Analyst launched an autonomous investigation into the beaconing and scanning activity and was able to connect these seemingly separate events into one incident. AI Analyst analyzes thousands of connections to hundreds of different endpoints at machine speed and then summarizes its findings in a single pane of glass, giving customers the necessary information to assess the threat and begin remediation if necessary. This significantly lessens the burden for human security teams, saving them previous time and resources, while ensuring they maintain full visibility over any suspicious activity on their network.

Figure 4: Cyber AI Analyst incident log summarizing the technical details of the device’s beaconing and scanning behavior.

Beaconing Continues

Darktrace continued to observe the device carrying out beaconing activity over the next few days, likely representing threat actors attempting to establish communication with their malicious infrastructure and setting up a foothold within the customer’s environment. In one such example, the device was seen connecting to the suspicious endpoint ‘fysiotherapie-panken[.]nl’. Multiple open-source intelligence (OSINT) vendors reported this endpoint to be a known malware delivery host [3].

Once again, Darktrace RESPOND was in place to quickly intervene in response to these suspicious external connection attempts. Over the course of several days, RESPOND blocked the offending device from connecting to suspicious endpoints via port 443 and enforced its pattern of life. These autonomous actions by RESPOND effectively mitigated and contained the attack, preventing it from escalating further along the kill chain and providing the customer’s security team crucial time to take act and employ their own remediation.

Figure 5: A sample of the autonomous RESPOND actions that was applied on the affected device.

Possible Payload Retrieval

A few days later, on September 26, 2023, Darktrace observed the affected device attempting to download a Windows Portable Executable via file transfer protocol (FTP) from the external location ‘ftp2[.]sim-networks[.]com’, which had never previously been seen on the network. This download likely represented the next step in the Gootloader infection, wherein additional malicious tooling is downloaded to further cement the malicious actors’ control over the device. In response, Darktrace RESPOND immediately blocked the device from making any external connections, ensuring it could not download any suspicious files that may have rapidly escalated the attackers’ efforts.

Figure 6: DETECT’s identification of the offending device downloading a suspicious executable file via FTP.

The observed combination of beaconing activity and a suspicious file download triggered an Enhanced Monitoring breach, a high-fidelity DETECT model designed to detect activities that are more likely to be indicative of compromise. These models are monitored by the Darktrace SOC round the clock and investigated by Darktrace’s expert team of analysts as soon as suspicious activity emerges.

In this case, Darktrace’s SOC triaged the emerging activity and sent an additional notice directly to the customer’s security team, informing them of the compromise and advising on next steps. As this customer had subscribed to Darktrace’s Ask the Expert (ATE) service, they also had a team of expert analysts available to them at any time to aid their investigations.

Figure 7: Enhanced Monitoring Model investigated by the Darktrace SOC.

結論

Loader malware variants such as Gootloader often lay the groundwork for further, potentially more severe threats to be deployed within compromised networks. As such, it is crucial for organizations and their security teams to identify these threats as soon as they emerge and ensure they are effectively contained before additional payloads, like information-stealing malware or ransomware, can be downloaded.

In this instance, Darktrace demonstrated its value when faced with a multi-payload threat by detecting Gootloader at the earliest stage and responding to it with swift targeted actions, halting any suspicious connections and preventing the download of any additional malicious tooling.

Darktrace DETECT recognized that the beaconing and scanning activity performed by the affected device represented a deviation from its expected behavior and was indicative of a potential network compromise. Meanwhile, Darktrace RESPOND ensured that any suspicious activity was promptly shut down, buying crucial time for the customer’s security team to work with Darktrace’s SOC to investigate the threat and quarantine the compromised device.

Credit to: Ashiq Shafee, Cyber Security Analyst, Qing Hong Kwa, Senior Cyber Analyst and Deputy Analyst Team Lead, Singapore

付録

Darktrace DETECT によるモデル検知

Anomalous Connection / Rare External SSL Self-Signed

Device / Suspicious SMB Scanning Activity

Anomalous Connection / Young or Invalid Certificate SSL Connections to Rare

Compromise / High Volume of Connections with Beacon Score

Compromise / Beacon to Young Endpoint

Compromise / Beaconing Activity To External Rare

Compromise / Slow Beaconing Activity To External Rare

Compromise / Beacon for 4 Days

Anomalous Connection / Suspicious Expired SSL

Anomalous Connection / Multiple Failed Connections to Rare Endpoint

Compromise / Sustained SSL or HTTP Increase

Compromise / Large Number of Suspicious Successful Connections

Compromise / Large Number of Suspicious Failed Connections

Device / Large Number of Model Breaches

Anomalous File / FTP Executable from Rare External Location

Device / Initial Breach Chain Compromise

RESPOND Models

Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block

Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

Antigena / Network/Insider Threat/Antigena Network Scan Block

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block

Antigena / Network / External Threat / Antigena Suspicious File Block

Antigena / Network / External Threat / Antigena File then New Outbound Block

Antigena / Network / External Threat / Antigena Suspicious Activity Block

侵害指標(IoC)一覧

Type

Hostname

IoCs + Description

explorer[.]ee - C2 Endpoint

fysiotherapie-panken[.]nl- C2 Endpoint

devcxp2019.theclearingexperience[.]com- C2 Endpoint

campsite.bplaced[.]net- C2 Endpoint

coup2pompes[.]fr- C2 Endpoint

analyzetest[.]ir- Possible C2 Endpoint

tdgroup[.]ru- C2 Endpoint

ciedespuys[.]com- C2 Endpoint

fi.sexydate[.]world- C2 Endpoint

funadhoo.gov[.]mv- C2 Endpoint

geying.qiwufeng[.]com- C2 Endpoint

goodcomix[.]fun- C2 Endpoint

ftp2[.]sim-networks[.]com- Possible Payload Download Host

MITRE ATT&CK マッピング

Tactic – Technique

Reconnaissance - Scanning IP blocks (T1595.001, T1595)

Command and Control - Web Protocols , Application Layer Protocol, One-Way Communication, External Proxy, Non-Application Layer Protocol, Non-Standard Port (T1071.001/T1071, T1071, T1102.003/T1102, T1090.002/T1090, T1095, T1571)

Collection – Man in the Browser (T1185)

Resource Development - Web Services, Malware (T1583.006/T1583, T1588.001/T1588)

Persistence - Browser Extensions (T1176)

参考文献

1.     https://www.blackberry.com/us/en/solutions/endpoint-security/ransomware-protection/gootloader

2.     https://redcanary.com/threat-detection-report/threats/gootloader/

3.     https://www.virustotal.com/gui/domain/fysiotherapie-panken.nl

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著者について
Ashiq Shafee
Cyber Security Analyst

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Seven Cyber Security Predictions for 2024

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13
Feb 2024

2024 Cyber Threat Predictions

After analyzing the observed threats and trends that have affected customers across the Darktrace fleet in the second half of 2023, the Darktrace Threat Research team have made a series of predictions. These assessments highlight the threats that are expected to impact Darktrace customers and the wider threat landscape in 2024.  

1. Initial access broker malware, especially loader malware, is likely to be a prominent threat.  

Initial access malware such as loaders, information stealers, remote access trojans (RATs), and downloaders, will probably remain some of the most relevant threats to most organizations, especially when noted in the context that many are interoperable, tailorable Malware-as-a-Service (MaaS) tools.  

These types of malware often serve as a gateway for threat actors to compromise a target network before launching subsequent, and often more severe, attacks. Would-be cyber criminals are now able to purchase and deploy these malware without the need for technical expertise.  

2. Infrastructure complexity will increase SaaS attacks and leave cloud environments vulnerable.

The increasing reliance on SaaS solutions and platforms for business operations, coupled with larger attack surfaces than ever before, make it likely that attackers will continue targeting organizations’ cloud environments with account takeovers granting unauthorized access to privileged accounts. These account hijacks can be further exploited to perform a variety of nefarious activities, such as data exfiltration or launching phishing campaigns.  

It is paramount for organizations to not only fortify their SaaS environments with security strategies including multifactor authentication (MFA), regular monitoring of credential usage, and strict access control, but moreover augment SaaS security using anomaly detection.  

3. The prevalence and evolution of ransomware will surge.

The Darktrace Threat Research team anticipates a surge in Ransomware-as-a-Service (RaaS) attacks, marking a shift away from conventional ransomware. The uptick in RaaS observed in 2023 evidences that ransomware itself is becoming increasingly accessible, lowering the barrier to entry for threat actors. This surge also demonstrates how lucrative RaaS is for ransomware operators in the current threat landscape, further reinforcing a rise in RaaS.  

This development is likely to coincide with a pivot away from traditional encryption-centric ransomware tactics towards more sophisticated and advanced extortion methods. Rather than relying solely on encrypting a target’s data for ransom, malicious actors are expected to employ double or even triple extortion strategies, encrypting sensitive data but also threatening to leak or sell stolen data unless their ransom demands are met.  

4. Threat actors will continue to rely on living-off-the-land techniques.

With evolving sophistication of security tools and greater industry adoption of AI techniques, threat actors have focused more and more on living-off-the-land. The extremely high volume of vulnerabilities discovered in 2023 highlights threat actors’ persistent need to compromise trusted organizational mechanisms and infrastructure to gain a foothold in networks. Although inbox intrusions remain prevalent, the exploitation of edge infrastructure has demonstrably expanded compared to previously endpoint-focused attacks.

Given the prevalence of endpoint evasion techniques and the high proportion of tactics utilizing native programs, threat actors will likely progressively live off the land, even utilizing new techniques or vulnerabilities to do so, rather than relying on unidentified malicious programs which evade traditional detection.

5. The “as-a-Service” marketplace will contribute to an increase in multi-phase compromises.

With the increasing “as-a-Service” marketplaces, it is likely that organizations will face more multi-phase compromises, where one strain of malware is observed stealing information and that data is sold to additional threat actors or utilized for second and/or third-stage malware or ransomware.  

This trend builds on the concept of initial access brokers but utilizes basic browser scraping and data harvesting to make as much profit throughout the compromise process as possible. This will likely result in security teams observing multiple malicious tools and strains of malware during incident response and/or multi-functional malware, with attack cycles and kill chains morphing into less linear and more abstract chains of activity. This makes it more essential than ever for security teams to apply an anomaly approach to stay ahead of asymmetric threats.  

6. Generative AI will let attackers phish across language barriers.

Classic phishing scams play a numbers game, targeting as many inboxes as possible and hoping that some users take the bait, even if there are spelling and grammar errors in the email. Now, Generative AI has reduced the barrier for entry, so malicious actors do not have to speak English to produce a convincing phishing email.  

In 2024, we anticipate this to extend to other languages and regions. For example, many countries in Asia have not yet been greatly impacted by phishing. Yet Generative AI continues to develop, with improved data input yielding improved output. More phishing emails will start to be generated in various languages with increasing sophistication.    

7. AI regulation and data privacy rules will stifle AI adoption.

AI regulation, like the European Union’s AI Act and NIS2, is starting to be implemented around the world. As policies continue to come out about AI and data privacy, practical and pragmatic AI adoption becomes more complex.  

Businesses will likely have to take a second look at AI they are adopting into their tech stacks to consider what may happen if a tool is suddenly deprecated because it is no longer fit for purpose or loses the approvals in place. Many will also have to use completely different supply chain evaluations from their usual ones based on developing compliance registrars. This increased complication may make businesses reticent to adopt innovative AI solutions as legislation scrambles to keep up.  

Learn more about observed threat trends and future predictions in the 2023 End of Year Threat Report

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