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ラテンアメリカでのデータ流出事例

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19
Jul 2021
19
Jul 2021
Latin America has been one of hardest hit regions for cyber-crime this last year. This blog unpacks an intrusion at a pharmaceutical organization based in LATAM, and how Self-Learning AI detected the data exfiltration attack at every stage.

データ抜き出しはサイバー犯罪者に人気のあるビジネスです。政府機関から中小企業に至るまで、あらゆる組織には機密データが存在し、これが盗まれて脅迫に使われることも、競合他社に利用されることも、あるいはその組織のシステムにさらに入り込むために使われることもあります。これはランサムウェアアクターにとっても現在好まれる手法となっています。一部のRaaS(Ransomware-as-a-Service)グループは新しいタイプの恐喝型マルウェア、‘extortionware’ を使い始めました。これは暗号化を行わないデータ盗み出しに的を絞ったマルウェアです。

カネになる標的

ラテンアメリカのある医薬品メーカーにおいて、Darktraceは最近、社内の重要ファイルの抜き出しを検知しました。

この組織は2つの理由で魅力的な標的でした。1つ目の理由は、医薬品メーカーには価値の高い知的財産や患者データが豊富にあることです。昨年も、脅威アクターや国家組織によりワクチン研究および流通システムへの侵入を狙った絶え間ない攻撃がありました。

データ侵害事例が最も多い業界:医薬品

2つ目の理由は、ラテンアメリカがサイバー犯罪者にとって宝の山であることです。近年の大きな経済成長、主な産業のデジタル化とともに、不十分なサイバー保険、実質的に存在しないに等しい規制などが特徴的です。

COVID-19パンデミック以前にも、ブラジルとメキシコは欧州刑事警察機構が発表した被害国トップ10に入っていました。それ以後も、事件数は急激に増大し、多くの企業は準備が整わないまま、政府当局からのサポートや圧力の少なさに直面しています。驚くべきことに、推定80億ドルもの損失を被ったにも関わらず、ブラジルにはまだデータ保護法が制定されていません。

金銭目的の犯罪に加えて、ラテンアメリカ地域はロシア、中国、イランに支援されたグループにも狙われています。サイバースパイ活動は投資や商取引において交渉を有利にし、対外利権を拡大するための方法として使われています。

さらに、犯罪者の世界のサプライチェーンがパンデミックによる影響を受けた結果、犯罪組織がデジタル世界、特に詐欺やフィッシングなどを収入源として利用し始めるでしょう。メキシコの悪名高いドラッグカルテル、La Familia Michoacanaもダークウェブのハッカー達を引き入れ始めたと言われています。

ラテンアメリカ地域が多数の脅威に直面しているにも関わらず、さまざまな組織への防御技術の導入は遅れがちでした。したがって、以下の事例においてこの攻撃者がラテンアメリカ地域の小規模な組織を選んだとき、そこにあるのはシグネチャベースの、従来型セキュリティツールだけだと考えていたことでしょう。これはほとんど抵抗もなく大きな利益が見込まれる簡単な獲物と思っていた攻撃者は最初のステップを実行しました。

侵入の経緯

その会社においてProof of Valueトライアルが実施されていたDarktraceは、サーバーから不審なアクティビティを検知し、続いて外部リモート接続を観測しました。

図1:攻撃のタイムライン

攻撃が開始されたのは、内部サーバーがRDPを介して外部IPから不審な接続を受信したときでした。この接続は5時間に渡り継続しました。この外部IPはこのサーバーに対し、管理者認証情報を使って新しいSMBセッションを確立しました。外部IPはSMBを使って、暗号化されていないパスワードが含まれていそうな1つのファイルにアクセスしました。

現在、コロンビアの人口の65%がインターネットを使用していますが、2000年にはわずか3%でした。

その後、外部IPはSMBを介して18,000個を超えるファイルをダウンロードしました。ファイル名から見て、これらのデータは機密性が高いものであったと思われます。この外部IPは社内のサーバーから合わせて約150 MBものデータをダウンロードしました。

リモート接続後の不審なアクティビティ

自己学習型AIはこのIPアドレスがこの組織とサーバーにとって100%未知であることを検知しました。また、このデータ転送もデバイスの通常の「生活パターン」に照らして不審であることが検知されました。残念ながら、Darktrace RESPONDはパッシブモードで試用中であったため、Darktraceが介入して攻撃を中断させることはできませんでした。

しかし、Darktraceは確度の高い多数のアラートを生成してセキュリティチームに警告しました。以下の図は、同じ状況にあったサンプルデバイスからの5日間のアクティビティを示したものですが、クラスター化したアラートが大量に確認できます。これには、侵害されたデバイスから外部に転送されたデータ量の不審な増大も反映されています。

図2:類似のデバイスがリモートデスクトップ接続を受信。これは最初のモデル違反(オレンジ色の点)が示しています。その直後、外部デバイスが暗号化されていないパスワードファイルにアクセスしました。同時にこのデバイスは、通常は見られない大量のデータを未知の外部ソースIPに転送しました。

データ抜き出し手法:RDPおよびパスワードファイルへのアクセス

脅威アクターはその会社内の既存のセキュリティ製品をすべて回避することに成功していました。それが可能であったのは正当な管理者認証情報を使ったためであり、これはRDPおよびSMB接続の確立にも使用されました。RDP 認証情報はダークウェブでで簡単に購入することができ、特にリモートワークが続いている今年は初期アクセスの方法としてよく使われています。

これに加えて、パスワードの不適切な管理によりその組織のデジタル王国への鍵が開かれることもあります。アクセスされたファイルの1つはパスワードファイルであったため、脅威アクターはすぐに権限を引き上げることができました。この段階になると、侵入のスピードに対応できるのはAIを使った防御ツールだけになります。

SMBのような一般的なプロトコルを使ってデータを抜き出すことはよくある戦術です。攻撃者はオープンなポート、未使用ポートを悪用するため、インターネットに公開されたサーバーは組織にとって大きなリスクです。

さらに、この間に転送されたファイルはパートナー企業や顧客の名前が付けられた領収証として保存されていたものでした。これはきわめて危険であり、この会社の信用を深刻な危険にさらす可能性がありました。幸い、自己学習型AIが悪意あるアクションを検知し即座にセキュリティチームに警告したため、彼らはさらなる抜き出しを阻止しフォローアップを行うことができました。

機密データの保護

上記の事例は、非常に小規模な企業であっても被害者になることがあることを示しています。中小企業が標的とされるのは、重要なデータやIPを保有しているにもかかわらず、強力なセキュリティやリソースが欠けている場合があるからです。このことから、大規模な企業や政府機関と比較して簡単な獲物になり得ます。

DarktraceのAIは動作のかすかな変化から悪意あるデータ抜き出しを検知する能力を持っています。 このケースでは、標的とされたサーバーは日常的に組織の内外とデータの転送を行っていましたが、Darktraceはこの攻撃における外部IPからの着信接続を最も高い未知度でスコア付けしました。つまり、Darktraceはこのデータ転送アクティビティが普段と大きく異なるものであり、サーバーの通常の「生活パターン」から外れていると見なしたのです。

これにより、セキュリティチームはこの脅威に対応しサーバーをオフラインにして詳細な調査を行うことができました。もしこの環境内でDarktrace RESPONDがアクティブモードで運用されていれば、最初の侵害から数秒後に反応しマシンスピードで脅威を阻止していたはずです。

この調査結果についての考察はDarktraceアナリストKendra Gonzalez Duran が協力しました。

Learn how to defend your company from data exfiltration and malicious insiders

Darktraceによるモデル検知:

  • Compliance / Incoming Remote Desktop
  • Compliance / Possible Unencrypted Password File On Server
  • Anomalous Connection/ Data Sent to Rare Domain

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
Max Heinemeyer
Chief Product Officer

Max is a cyber security expert with over a decade of experience in the field, specializing in a wide range of areas such as Penetration Testing, Red-Teaming, SIEM and SOC consulting and hunting Advanced Persistent Threat (APT) groups. At Darktrace, Max is closely involved with Darktrace’s strategic customers & prospects. He works with the R&D team at Darktrace, shaping research into new AI innovations and their various defensive and offensive applications. Max’s insights are regularly featured in international media outlets such as the BBC, Forbes and WIRED. Max holds an MSc from the University of Duisburg-Essen and a BSc from the Cooperative State University Stuttgart in International Business Information Systems.

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Inside the SOC

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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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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