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© 2022 JPCERT/CC
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Fight Against Malware
Development Life Cycle
CODE BLUE 2022
© 2022 JPCERT/CC
1
Problem of Incident Response
Attacker vs Defender
© 2022 JPCERT/CC
2
Problem of Incident Response
Attacker vs Defender
We are in the Malware Development Life Cycle…
© 2022 JPCERT/CC
Surface Analysis
Runtime Analysis
Static Analysis
Create YARA Rule
Finished analysis …
3
Malware Analysis Operations (MAOps)

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VAPT - Vulnerability Assessment & Penetration Testing
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VAPT (Vulnerability Assessment and Penetration Testing) involves evaluating systems and networks to identify vulnerabilities, configuration issues, and potential routes of unauthorized access. It is recommended for SMEs due to common security issues like phishing and ransomware attacks targeting them. The document outlines the types of VAPT testing, why SMEs need it, example data breaches, and estimated costs of common cyber attacks and security services.

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Technical Approach to Red Team Operations
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Presentación realizada en el congreso MoscowCON (Moscu) sobre aspectos técnicos de la realización de ejercicios de Red Team

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© 2022 JPCERT/CC
Surface Analysis
Runtime Analysis
Static Analysis
Create YARA Rule
Automation!
4
All roads lead to AUTOMATION!
© 2022 JPCERT/CC
5
Problem of MAOps
Attacker vs Malware Analyst
© 2022 JPCERT/CC
6
Problem of MAOps
Attacker vs Malware Analyst
Changed C2 server
Changed encrypt key
Added new function
© 2022 JPCERT/CC
7
Problem of MAOps
Attacker vs Malware Analyst
STOP

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MITRE ATT&CKcon 2.0: Using Threat Intelligence to Focus ATT&CK Activities; Da...
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This document discusses Nationwide's experience using threat intelligence to focus their MITRE ATT&CK activities. Their initial broad approach analyzing 240+ techniques at once was unsuccessful. They then prioritized techniques based on threats to the financial sector. This focused their efforts on the 27 most relevant threat actors and the 100+ techniques associated with them. They mapped techniques to the ATT&CK matrix and conducted intelligence research. This intelligence-led approach improved their security posture understanding and enabled prioritized, actionable recommendations. The process is ongoing to constantly evolve their defenses based on the latest intelligence.

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[cb22] Understanding the Chinese underground card shop ecosystem and becoming...
[cb22] Understanding the Chinese underground card shop ecosystem and becoming...[cb22] Understanding the Chinese underground card shop ecosystem and becoming...
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Personal Identifiable Information (PII) leaks have become more frequent in recent years, and losses from credit card fraud in 2021 have set records respectively in Taiwan and Japan. Where did this information get leaked and sold in the first place? The term "Dark web" refers to websites inaccessible without the use of Tor protocol, and given added privacy and anonymity while using Tor, and marketplaces in it are proven to be very attractive to criminals. An anonymous researcher will share experiences of dealing with vendors from card shops on marketplaces among dark web, focused on insights of shops selling Taiwanese and Japanese PIIs, and therefore, TTPs of hackers from these card shops. We hope to inspire audiences to rethink how to reduce credit card frauds.

© 2022 JPCERT/CC
Maintenance of malware analysis tools is
hard work.
Creating a lot of malware analysis
tools requires the maintenance of
many tools.
8
Problem of MAOps
Need the methods to counter the attacker's
development lifecycle
© 2022 JPCERT/CC
9
Goal of This Presentation
This presentation shares
methods for building
analytical tools to counter
the attacker's lifecycle.
© 2022 JPCERT/CC
Approach
Technologies such as CI/CD and serverless are methods
that make system maintenance and release more efficient.
Cloud services can be a solution to the problems of
malware analysts.
We managed the Malware Analysis System on Cloud.
10
Use cloud services CI/CD, serverless, and
Infrastructure as Code (IaC) to fight the malware
development life cycle.
© 2022 JPCERT/CC
IP addresses can be changed dynamically
Easy to update analysis tools
Easy to build scale-out systems
No server provisioning or maintenance required
Low maintenance costs
11
Advantages of Using the Cloud for Malware Analysis

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Social engineering and phishing attacks are the largest threats to companies, as attackers are increasingly relying on tricking users to gain access to systems. Mobile malware and internet-connected devices are also growing vulnerabilities, as more business is conducted and data is stored on mobile and cloud systems. Companies need to invest in protections against these emerging threats like social engineering, mobile malware, cloud vulnerabilities, and weaknesses in the growing Internet of Things. Staying ahead of changing attack types can help reduce vulnerabilities, but protecting against current and future risks is a ongoing challenge.

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Introduction to Software Security and Best Practices
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© 2022 JPCERT/CC
12
Malware Analysis System on Cloud
Scanner
Memory
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Analysis
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Analysis
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Research
© 2022 JPCERT/CC
Presentation Topics
13
1 Malware C2 Monitoring
2 Malware Hunting using Cloud
3 YARA CI/CD system
4 Surface Analysis System on Cloud
5 Memory Forensic on Cloud
© 2022 JPCERT/CC
14
1 Malware C2 Monitoring
2 Malware Hunting using Cloud
3 YARA CI/CD system
4 Surface Analysis System on Cloud
5 Memory Forensic on Cloud
© 2022 JPCERT/CC
Blocked access to the C2 server.
Targeted by an attacker.
Cloud services solve these problems.
15
Malware C2 Monitoring
Monitoring the attacker's C2 server is important to
understand their activity.
Notes

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MITRE ATT&CK is quickly gaining traction and is becoming an important standard to use to assess the overall cyber security posture of an organization. Tools like ATT&CK Navigator facilitate corporate adoption and allow for a holistic overview on attack techniques and how the organization is preventing and detecting them. Furthermore, many vendors, technologies and open-source initiatives are aligning with ATT&CK. Join Erik Van Buggenhout in this presentation, where he will discuss how MITRE ATT&CK can be leveraged in the organization as part of your overall cyber security program, with a focus on adversary emulation. Erik Van Buggenhout is the lead author of SANS SEC599 - Defeating Advanced Adversaries - Purple Team Tactics & Kill Chain Defenses. Next to his activities at SANS, Erik is also a co-founder of NVISO, a European cyber security firm with offices in Brussels, Frankfurt and Munich.

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Lateral Movement: How attackers quietly traverse your Network
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© 2022 JPCERT/CC
16
Case1: Monitoring the Lucky Visitor Scam C2
© 2022 JPCERT/CC
17
Lucky Visitor Scam Infrastructure
C2 Server
Compromised Server
Scam Server
Website access
#1 Send client information
#2
Reply scam
server URL
#3
Send scam
server URL
#4
Access scam website
#5
© 2022 JPCERT/CC
18
PHP Backdoor Installed in Compromised Server
C2 Server
© 2022 JPCERT/CC
$ curl -i -X POST http[:]//144.76.47[.]168/app/assets/api2?action=redir -d
"ip=153.213.10.51&qs=dfaljfadfa.com%2Findex.php%3Ffc858f%3DiPhone-Apple-Windows&ua=Mozilla%2F5.0
(...) &conn=close&uid=fb06bc98-576a-d5df-2195-a4b0a64bec44" -H "User-Agent: " -H "Accept: "
HTTP/1.1 200 OK
Server: nginx/1.14.2
Date: Tue, 23 Feb 2021 23:46:35 GMT
Content-Type: text/html; charset=utf-8
Content-Length: 590
Connection: keep-alive
X-Frame-Options: DENY
X-Content-Type-Options: nosniff
Referrer-Policy: same-origin
a:2:{s:4:"type";s:5:"redir";s:4:"data";a:1:{s:4:"code";s:524:"<html>
<head>
<META http-equiv="refresh" content="1;URL=https[:]//finishsandperson-
9[.]life/?utm_campaign=pEv9cTd8QNHYzqqr5UNFx2COHvnp_JE3r8uVIhm3Qww1&t=main7d">
<script>
window.location = "https[:]//finishsandperson-
9[.]life/?utm_campaign=pEv9cTd8QNHYzqqr5UNFx2COHvnp_JE3r8uVIhm3Qww1&t=main7d";
</script>
</head>
<body>
To the new location please <a href="https[:]//finishsandperson-
9[.]life/?utm_campaign=pEv9cTd8QNHYzqqr5UNFx2COHvnp_JE3r8uVIhm3Qww1&t=main7d"><b>click here.</b></a>
</body>
</html>";}}
19
Request to C2 for Redirect URL

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© 2022 JPCERT/CC
Check
 Displays the number of files stored on
the server
templates
• Creates a .html file for templates
keywords
• Creates a .lst file for keywords
update_sitemap
• Updates sitemap.xml
20
PHP Backdoor Installed in Compromised Server
pages
 Creates a new page
ping
 Sends a sitemap.xml URL to google and
bing
robots
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eval
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PHP Backdoor Features
© 2022 JPCERT/CC
IP Address Status
144.76.47.168 〇
144.76.51.144 ×
178.63.30.186 〇
178.63.30.30 〇
178.63.34.6 ×
5.9.146.0 ー
5.9.235.245 〇
5.9.239.221 ×
5.9.34.13 〇
5.9.37.245 ×
94.130.71.28 ×
21
Lucky Visitor Scam C2
Attackers use different C2 servers over time.
© 2022 JPCERT/CC
Get PHP content form VT
Check C2
Get redirect URL form C2
Report to Google safe browsing
Blocked by web browser
22
Flow of Lucky Visitor Scam Countermeasures
Automatically execute the below process, from discovering
C2 servers to blocking redirects in web browsers.
© 2022 JPCERT/CC
23
Monitoring the Lucky Visitor Scam C2 System (AWS)
C2
AWS Lambda
Amazon EventBridge
GitHub Actions Private
Public
AWS GitHub
Git push
Git push
Event
Get HTML content
Amazon S3
output bucket
Put IoC Get IoC
Git checkout
Google Safe Browsing
Report
VirusTotal

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© 2022 JPCERT/CC
24
Monitoring the Lucky Visitor Scam C2 System
https://github.com/JPCERTCC/Lucky-Visitor-Scam-IoC
© 2022 JPCERT/CC
25
Monitoring the Lucky Visitor Scam C2 System (GCP)
C2
Cloud Functions
Cloud Scheduler
GitHub Actions Private
Public
GitHub
Git push
Git push
Event
Get HTML content
Cloud Storage
Put IoC Get IoC
Git checkout
Google Safe Browsing
Report
© 2022 JPCERT/CC
26
Access Control for C2 Server
C2
C2 server is inaccessible from Japan.
© 2022 JPCERT/CC
27
C2 Activity
0
50
100
150
200
250
300
350
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
(Count)
(Time UTC+9)
Investigation of the attacker’s redirect URL delivery cycle.

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【HITCON FreeTalk 2021 - SolarWinds 供應鏈攻擊事件分析】
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© 2022 JPCERT/CC
28
1 Malware C2 Monitoring
2 Malware Hunting using Cloud
3 YARA CI/CD system
4 Surface Analysis System on Cloud
5 Memory Forensic on Cloud
© 2022 JPCERT/CC
Get IoCs
Download malware
Analyze malware
Hunt and detect incident
29
Malware Hunting using Cloud
Malware hunting gets unknown IoCs and can be used for
incident detection.
© 2022 JPCERT/CC
Get IoCs
Download malware
Analyze malware
Hunt and detect incident
30
Malware Hunting using Cloud
Malware hunting gets unknown IoCs and can be used for
incident detection.
© 2022 JPCERT/CC
31
Case2: Cobalt Strike Beacon Hunting
Cobalt Strike is a popular penetration tool among attackers.
[1]
Cobalt Strike's C2s are found in large numbers and cannot all be analyzed manually.

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How Attackers Use Exposed Prometheus Server to Exploit Kubernetes Clusters. The first step in any pentesting, ethical hacking or cybercriminal groups, is to gather as much information as you can about the target you want to breach. Why? Simple, to know what technique to use or the appropriate tools to achieve intrusion and evasion of defense systems. Information on versions inside the cluster can map to CVE and vulnerabilities that can be exploited. Information on applications, tools and architectures can be used for competitors. Prometheus in the wild Title Prometheus collects and stores its metrics as time series data, i.e. metrics information is stored with the timestamp at which it was recorded, alongside optional key-value pairs called labels. Prometheus allows (and recommends) using basic authentication, but not enabled by default: https://prometheus.io/docs/operating/security/ Exposing open Prometheus endpoints to the Internet is a bad idea... and as every bad idea, it's highly adopted: What will we us to fingerprint Kubernetes? Title Two of the most widely used exporters offer most of the information that we need: Node ExporterKube State Metrics ● ●● ● ● ● ● ● Physical infrastructure Network interfaces Host OS & kernel Kubernetes components Hostnames and network topology Logical hierarchy Secrets location Applications (and versions) deployed Fingerprinting Physical Infrastructure Title Node Exporter: node_dmi_info bios_vendor: ● SeaBIOS ● Amazon EC2 bios_version: ● seabios-1.9.1-qemu-project.org ● 8f19b21 ● 1.0 bios_release: ● 1.0 bios_date: ● 10/16/2017 ● 04/01/2014 chassis_asset_tag: ● Amazon EC2 chassis_vendor: ● Amazon EC2 ● Alibaba Cloud system_vendor: ● Tencent Cloud ● Amazon EC2 ● Alibaba Cloud product_name: ● m5.xlarge ● Alibaba Cloud ECS product_version: ● pc-i440fx-2.1 board_vendor: ● Amazon EC2 board_asset_tag: ● i-00280f617XXXXX board_vendor: ● Smdbmds ● Amazon EC2 Fingerprinting network interfaces Title Node Exporter: node_network_info{device=~'eth.+'} { address="06:d5:XX:XX:XX:XX", broadcast="ff:ff:ff:ff:ff:ff", device="eth0", instance="172.31.XX.XX:9100", instance_az="us-west-2a", instance_id="i-XXXXX", instance_name="XXX-XXX", instance_type="c5.xlarge", instance_vpc="vpc-XXXXXXX", job="ec2_instances", operstate="up" } Fingerprinting network topology Title KSM: kube_node_info kube_service_info * on (service) group_left group by (service,type)(kube_service_spec_type{type="LoadBalancer"}) kube_ingress_info Node hostname Services in the cluster (specially load-balancers) ● namespace ● cluster IP ● node ● (application behind the service can be guessed by name of service/namespace) Ingresses in the cluster

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[cb22] What I learned from the direct confrontation with the adversaries who ...
[cb22] What I learned from the direct confrontation with the adversaries who ...[cb22] What I learned from the direct confrontation with the adversaries who ...
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In November 2019, I started monitoring the Bitcoin operation by the adversaries who hid IP addresses of their C&C server in the blockchain. In June 2020, I started collaborating with Professor Christian Doerr of the Hasso Plattner Institute based on the idea of redirecting C&C server communication to a sinkhole server (called takeover), and we successfully achieved this in August. However, the adversaries quickly took evasive action, where they managed to implement an evasion mechanism in only two weeks and restarted their attack. Although we could not conduct our takeover, our monitoring system could worked well. The end of their attack was brought upon by the surge in Bitcoin prices. Due to the fees for the Bitcoin miners, a transaction had reduced the adversaries' profits, and we confirmed the last C&C update was in January 2021 and the abandonment of the attack infrastructure came in March. Since then, no similar attacks have been observed by my monitoring system. Although this attack has already concluded and is unlikely to restart unless the value of Bitcoin declines, I would like to share the know-how I have learned through the direct confrontation with the adversaries. That is, at the time of the confrontation with them, this attack was highly novel, and the adversaries themselves did not fully understand the best solution for its' operation. They needed to evolve their tactics, techniques, and procedures (TTPs) while operating the system. We carefully analyzed their TTPs and tried to catch them off their guard. Even more troublesome was the need to understand as quickly as possible what they intended to do each time they were affected by the Bitcoin halving or making a simple operational error. This presentation is a culmination my insights learned from interactions with these adversaries and I am looking forward to sharing this information with everyone.

© 2022 JPCERT/CC
Get C2 server form VT
Download Cobalt Strike beacon
Analysis Cobalt Strike beacon
Publish configuration
32
Flow of Cobalt Strike Beacon Hunting
Automatically execute from discovering C2 servers to
hunting Cobalt Strike beacon.
© 2022 JPCERT/CC
33
C2 Server Data
https://www.virustotal.com/api/v3/intelligence/search?query=entity%3Aip%20c
omment%3A%23cobaltstrike&limit=300&descriptors_only=true
VT API
Extract a list of IP addresses of Cobalt Strike's servers from
VirusTotal.
© 2022 JPCERT/CC
32bit
/aaa9
/aab8
64bit
/aab9
/aac8
34
Default URL Path for Cobalt Strike Beacon
Many C2 servers do not change the default URL path.
Default URL path for Cobalt Strike beacon
© 2022 JPCERT/CC
35
Configuration Data
Encode
configuration data
in Cobalt Strike
beacon
XOR 0x2E

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FIDO Masterclass
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© 2022 JPCERT/CC
set Description Remarks
0x01 BeaconType
0=HTTP, 1=Hybrid HTTP and
DNS, 8=HTTPS
0x02 Port number
0x03 Polling time
0x04 Unknown
0x05 Jitter
Ratio of jitter in polling time (0-
99%)
0x06 Maxdns
Maximum length of host name
when using DNS (0-255)
0x07 Unknown
0x08 Destination host
0x09 User agent
0x0a
Path when communicating
HTTP_Header2
0x0b Unknown
0x0c HTTP_Header1
0x0d HTTP_Header2
0x0e Injection process
0x0f Pipe name
0x10 Year
Stops operating after the
specified date by Year, Month,
Day
36
Configuration Index
set Description Remarks
0x11 Month
0x12 Day
0x13 DNS_idle
0x14 DNS_Sleep
0x1a HTTP_Method1
0x1b HTTP_Method2
0x1c Unknown
0x1d
Process to inject arbitrary
shellcode (32bit)
0x1e
Process to inject arbitrary
shellcode (64bit)
0x1f Unknown
0x20 Proxy server name
0x21 Proxy user name
0x22 Proxy password
0x23 AccessType
1 = Do not use proxy server
2 = Use IE configuration in the
registry
4 = Connect via proxy server
0x24 create_remote_thread
Flag whether to allow creating
threads in other processes
0x25 Not in use
© 2022 JPCERT/CC
37
Cobalt Strike Beacon Hunting System
Cobalt Strike C2
Lambda
EventBridge GitHub Actions Private
Public
AWS Cloud GitHub
Git push
Git push
Event
Get beacon
S3 output bucket
Put Config Get Config
Git checkout
Put Config
VirusTotal
Get server list
API Gateway
WAF
Run
REST API
Client
© 2022 JPCERT/CC
38
Published Configuration Repository
https://github.com/JPCERTCC/CobaltStrike-Config
© 2022 JPCERT/CC
39
Cobalt Strike Beacon Hunting System
Cobalt Strike C2
Lambda
EventBridge GitHub Actions Private
Public
AWS Cloud GitHub
Git push
Git push
Event
Get beacon
S3 output bucket
Put Config Get Config
Git checkout
Put Config
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API Gateway
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Client

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devopsiacsecurity
© 2022 JPCERT/CC
40
REST API
https://[mask]/cs-scan-api?scan=http://185.38.142.75:80/aaa9
You do not need to download Cobalt Strike Beacon to analyze.
© 2022 JPCERT/CC
41
Changes in Cobalt Strike C2 (Daily)
0
20
40
60
80
100
120
140
160
180
200
2/17/2022 3/17/2022 4/17/2022 5/17/2022 6/17/2022 7/17/2022 8/17/2022
(Count)
(Date)
© 2022 JPCERT/CC
42
1 Malware C2 Monitoring
2 Malware Hunting using Cloud
3 YARA CI/CD system
4 Surface Analysis System on Cloud
5 Memory Forensic on Cloud
© 2022 JPCERT/CC
Since creating YARA rules cannot be automated, malware
analysts create them manually, and they spend too much time
on it.
Some characteristic malware can be created automatically.
43
YARA CI/CD System
Creation of YARA rules must be done manually by the
malware analyst, and automation of YARA rule creation is a
challenge.

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© 2022 JPCERT/CC
44
Case3: HUI Loader Analysis System
Legitimate HUI Loader Encoded Malware
DLL Data
HUI Loader used in APT10, Blue Termite,
A41APT and DEV-0401.
for i in range(len(enc_data)):
data = ord(enc_data[i]) ^ 0x20 ^ ord(key[i % len(key)])
dec_data.append(data)
Decode code
© 2022 JPCERT/CC
2015
2016
2017
2018
2019
2020
2021
2022
45
Overview of HUI Loader Timeline
2015 Jan
APT10 started using HUI loader
2015 Apr
Blue Termite started using HUI loader
2020 Jun
A41APT started using HUI loader
2021 Aug
DEV-0401 started using HUI loader
2016 Jul
 Password randomized
 DLL injection
2021 Dec
 Security features bypassed
 "HUI…" strings deleted
© 2022 JPCERT/CC
46
Common Problems with Loader
Legitimate HUI Loader Encoded Malware
DLL Data
Even if the Loader is found, the function of the malware is
unknown because the encoded malware cannot be found.
Found it!
Not found!
© 2022 JPCERT/CC
Get HUI Loader form VT
Analysis HUI Loader
Create YARA rule
Push YARA rule to VT
47
Flow of HUI Loader Analysis System
Automatically execute from getting HUI Loader to creating
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HUI Loader Analysis System
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Push YARA rule
© 2022 JPCERT/CC
49
Created YARA rule
rule malware_HUILoader_data_2317d3e14a {
meta:
description = "HUI Loader loading data"
author = "JPCERT/CC Incident Response Group"
HUI_loader_hash = "2317d3e14ab214f06ae38a729524646971e21b…"
condition:
uint32(0) == 0x7A8F473A or
vt.metadata.file_name iequals "vlc.cnf"
}
The file name and encoding key are unique to each loader, and thus rules
must be created for each it.
YARA rule
© 2022 JPCERT/CC
50
Published Analysis Results Repository
https://github.com/JPCERTCC/HUILoader-research
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Surface Analysis System on Cloud
Confusing: each type of malware is called different names
by different vendors.
© 2022 JPCERT/CC
53
Surface Analysis System on Cloud
Malware names are different for each security vendor and
get confusing when looking at each reports.
What is this
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I want to scan
using my YARA
rule…
© 2022 JPCERT/CC
54
Surface Analysis System on Cloud
Same issues on twitter…
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Surface Analysis System on Cloud
Same issues on twitter…
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© 2022 JPCERT/CC
56
Can sandbox alone solve all problems?
© 2022 JPCERT/CC
57
Can sandbox alone solve all problems?
No.
Sandbox analysis results
need to be analyzed further,
which is time consuming.
© 2022 JPCERT/CC
Simple
Fewer steps to execute
Execute multiple analyses all at once
Easy to customize
Store analysis results
58
Our Requirements for Malware Analysis Tools
© 2022 JPCERT/CC
YARA
scan
floss capa
olevba Sandbox
My
toolset
59
For Example
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[cb22]  「協調された脆弱性開示の現在と未来」国際的なパネルディスカッション(2)by Allan Friedman [cb22]  「協調された脆弱性開示の現在と未来」国際的なパネルディスカッション(2)by Allan Friedman
[cb22] 「協調された脆弱性開示の現在と未来」国際的なパネルディスカッション(2)by Allan Friedman

ハッカーたちの間では、セキュリティ向上のために研究を共有することの重要性が何年も前から知られていた。一方、協調して脆弱性を開示することの重要性も、世界中の政府によってますます認識されるようになってきた。情報開示とセキュリティ研究者の保護という原則は国境を越えて共通であるものの、国によって重要な違いがある。本パネルでは、重要な公共政策や企業の行動に影響を与える可能性のあるグローバルな視点を提示する。 ENISAは、2022年4月に「EUにおける脆弱性開示政策の調整」を発表した。本報告書では、EU加盟国における脆弱性開示の協調政策の現状を客観的に紹介するだけでなく、中国、日本、米国における脆弱性開示の運用を紹介している。それらを踏まえて、協調的な脆弱性開示プロセスに望ましい要素やベストプラクティスの要素を検討し、その後、課題や問題点について議論する予定。 本報告書の内容を共有し、日本における運用の課題と今後の方向性、米国における国家安全保障と脆弱性対応の課題を、各法域の代表者とのパネルディスカッションで明らかにすることを目的としています。 パネリストは、日本では早期警戒��ートナーシップ通知機関の実務に携わる方々、欧州では上記報告書の執筆者、米国では上記報告書の寄稿者 日本では、脆弱性対応における体制意識、インセンティブ、未処理案件の増加、いわゆるトリアージなどの課題が紹介される予定 米国からは、国家安全保障のための脆弱性情報の開示方針(Vulnerabilities Equities Process)、脆弱性研究の不起訴方針の公表などを紹介するとともに、この問題の歴史的背景を紹介する。 パネルディスカッションを通じて、脆弱性開示政策を取り巻く国際情勢や今後の動向、特にサイバーセキュリティにおける脆弱性の重要な役割とそれを取り巻く社会が抱える課題について参加者に理解していただくことを目的とする。

[cb22] "The Present and Future of Coordinated Vulnerability Disclosure" Inter...
[cb22] "The Present and Future of Coordinated Vulnerability Disclosure" Inter...[cb22] "The Present and Future of Coordinated Vulnerability Disclosure" Inter...
[cb22] "The Present and Future of Coordinated Vulnerability Disclosure" Inter...

While hackers have known the importance of sharing research to improve security for years, the importance of coordinated vulnerability disclosure is increasingly recognized by governments around the world. The principals of disclosure an protecting security researchers are common across borders, but different countries have some key differences. This panel will present a global perspective that may in turn inform key public policy and company behavior. ENISA has published 'Coordinated Vulnerability Disclosure policies in the EU' in April 2022 . This report not only provides an objective introduction to the current state of coordinated vulnerability disclosure policies in the Member States of the European Union, but also introduces the operation of vulnerability disclosure in China, Japan and the USA. Based on these findings, the desirable and good practice elements of a coordinated vulnerability disclosure process are examined, followed by a discussion of the challenges and issues. This session aims to share the contents of this report and clarify the challenges and future direction of operations in Japan, as well as national security and vulnerability handling issues in the US, in a panel discussion with representatives from various jurisdictions. The panelists are involved in the practice of early warning partnership notified bodies in Japan, the authors of the above report in Europe and the contributors to the above report in the US. In Japan, the issues of system awareness, incentives, increase in the number of outstanding cases in handling and so-called triage in handling vulnerabilities will be introduced. From the United States, the Vulnerabilities Equities Process for National Security and the publication of a non-prosecution policy for vulnerability research will be introduced, as well as a historical background on the issue. The aim is that the panel discussion will enable the audience to understand the international situation surrounding CVD, as well as future trends, in particular the important role of vulnerability in cybersecurity and the challenges faced by society around it.

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[cb22] 「協調された脆弱性開示の現在と未来」国際的なパネルディスカッション (1)by 高橋 郁夫
[cb22] 「協調された脆弱性開示の現在と未来」国際的なパネルディスカッション (1)by  高橋 郁夫[cb22] 「協調された脆弱性開示の現在と未来」国際的なパネルディスカッション (1)by  高橋 郁夫
[cb22] 「協調された脆弱性開示の現在と未来」国際的なパネルディスカッション (1)by 高橋 郁夫

ハッカーたちの間では、セキュリティ向上のために研究を共有することの重要性が何年も前から知られていた。一方、協調して脆弱性を開示することの重要性も、世界中の政府によってますます認識されるようになってきた。情報開示とセキュリティ研究者の保護という原則は国境を越えて共通であるものの、国によって重要な違いがある。本パネルでは、重要な公共政策や企業の行動に影響を与える可能性のあるグローバルな視点を提示する。 ENISAは、2022年4月に「EUにおける脆弱性開示政策の調整」を発表した。本報告書では、EU加盟国における脆弱性開示の協調政策の現状を客観的に紹介するだけでなく、中国、日本、米国における脆弱性開示の運用を紹介している。それらを踏まえて、協調的な脆弱性開示プロセスに望ましい要素やベストプラクティスの要素を検討し、その後、課題や問題点について議論する予定。 本報告書の内容を共有し、日本における運用の課題と今後の方向性、米国における国家安全保障と脆弱性対応の課題を、各法域の代表者とのパネルディスカッションで明らかにすることを目的としています。 パネリストは、日本では早期警戒パートナーシップ通知機関の実務に携わる方々、欧州では上記報告書の執筆者、米国では上記報告書の寄稿者 日本では、脆弱性対応における体制意識、インセンティブ、未処理案件の増加、いわゆるトリアージなどの課題が紹介される予定 米国からは、国家安全保障のための脆弱性情報の開示方針(Vulnerabilities Equities Process)、脆弱性研究の不起訴方針の公表などを紹介するとともに、この問題の歴史的背景を紹介する。 パネルディスカッションを通じて、脆弱性開示政策を取り巻く国際情勢や今後の動向、特にサイバーセキュリティにおける脆弱性の重要な役割とそれを取り巻く社会が抱える課題について参加者に理解していただくことを目的とする。

[cb22] Are Embedded Devices Ready for ROP Attacks? -ROP verification for low-...
[cb22] Are Embedded Devices Ready for ROP Attacks? -ROP verification for low-...[cb22] Are Embedded Devices Ready for ROP Attacks? -ROP verification for low-...
[cb22] Are Embedded Devices Ready for ROP Attacks? -ROP verification for low-...

Yuuma Taki is enrolled in the Hokkaido Information University Information Media Faculty of Information Media (4th year). At university he is focusing on learning about security for lower-level components, such OS and CPU. In his third year of undergraduate school, he worked on trying to implement the OS security mechanism "KASLR", at Sechack365. Currently, he is learning about ROP derivative technology and embedded equipment security.

[cb22] Wslinkのマルチレイヤーな仮想環境について by Vladislav Hrčka
[cb22] Wslinkのマルチレイヤーな仮想環境について by Vladislav Hrčka [cb22] Wslinkのマルチレイヤーな仮想環境について by Vladislav Hrčka
[cb22] Wslinkのマルチレイヤーな仮想環境について by Vladislav Hrčka

2021年10月、Lazarusグループに関連する可能性が高いユニークなローダーであるWSLinkの最初の分析を公開。ほとんどのサンプルは難読化され、高度な仮想マシン(VM)難読化機能で保護されている。サンプルには明確なアーティファクトが含まれておらず、当初は難読化を公的に知られているVMと関連付けなかったが、後にそれをCodevirtualizerに接続することに成功。このVMは、ジャンクコードの挿入、仮想オペランドの暗号化、仮想オペコードの重複、難読化手法仮想命令のマージ、ネストされたVMなど、いくつかの追加の難読化技術を導入する。 本発表では、VMの内部を分析し、合理的な時間で難読化技術を「見抜く」ための半自動化されたアプローチについて説明する。また、難読化されたバイトコードと難読化されていないバイトコードを比較し、本手法の有効性を紹介する。われわれの手法は、仮想オペコードのセマンティクスを抽出する既知の難読化解除手法に基づいており、単純化規則によるシンボリック実行を使用。さらに、バイトコードチャンクとVMの内部構成を記号ではなく、具体的な値として扱い、既知の難読化手法で追加の難読化技術を自動的に処理できるようにする。

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[cb22] Under the hood of Wslink’s multilayered virtual machine en by Vladisla...
[cb22] Under the hood of Wslink’s multilayered virtual machine en by Vladisla...[cb22] Under the hood of Wslink’s multilayered virtual machine en by Vladisla...
[cb22] Under the hood of Wslink’s multilayered virtual machine en by Vladisla...

In October 2021, we published the first analysis of Wslink – a unique loader likely linked to the Lazarus group. Most samples are packed and protected with an advanced virtual machine (VM) obfuscator; the samples contain no clear artifacts and we initially did not associate the obfuscation with a publicly known VM, but we later managed to connect it to CodeVirtualizer. This VM introduces several additional obfuscation techniques such as insertion of junk code, encoding of virtual operands, duplication of virtual opcodes, opaque predicates, merging of virtual instructions, and a nested VM. Our presentation analyzes the internals of the VM and describes our semi automated approach to “see through” the obfuscation techniques in reasonable time. We demonstrate the approach on some bytecode from a protected sample and compare the results with a non-obfuscated sample, found subsequent to starting our analysis, confirming the method’s validity. Our solution is based on a known deobfuscation method that extracts the semantics of the virtual opcodes, using symbolic execution with simplifying rules. We further treat the bytecode chunks and some internal constructs of the VM as concrete values instead of as symbolic ones, enabling the known deobfuscation method to deal with the additional obfuscation techniques automatically.

[cb22] CloudDragon’s Credential Factory is Powering Up Its Espionage Activiti...
[cb22] CloudDragon’s Credential Factory is Powering Up Its Espionage Activiti...[cb22] CloudDragon’s Credential Factory is Powering Up Its Espionage Activiti...
[cb22] CloudDragon’s Credential Factory is Powering Up Its Espionage Activiti...

Kimsuky is a North Korean APT possibly controlled by North Korea's Reconnaissance General Bureau. Based on reports from the Korea Internet & Security Agency (KISA) and other vendors, TeamT5 identified that Kimsuky's most active group, CloudDragon, built a workflow functioning as a "Credential Factory," collecting and exploiting these massive credentials. The credential factory powers CloudDragon to start its espionage campaigns. CloudDragon's campaigns have aligned with DPRK's interests, targeting the organizations and key figures playing a role in the DPRK relationship. Our database suggested that CloudDragon has possibly infiltrated targets in South Korea, Japan, and the United States. Victims include think tanks, NGOs, media agencies, educational institutes, and many individuals. CloudDragon's "Credential Factory" can be divided into three small cycles, "Daily Cycle," "Campaign Cycle," and "Post-exploit Cycle." The"Daily Cycle" can collect massive credentials and use the stolen credentials to accelerate its APT life cycle. In the "Campaign Cycle," CloudDragon develops many new malware. While we responded to CloudDragon's incidents, we found that the actor still relied on BabyShark malware. CloudDragon once used BabyShark to deploy a new browser extension malware targeting victims' browsers. Moreover, CloudDragon is also developing a shellcode-based malware, Dust. In the "Post-exploit Cycle," the actor relied on hacking tools rather than malicious backdoors. We also identified that the actor used remote desktop software to prevent detection. In this presentation, we will go through some of the most significant operations conducted by CloudDragon, and more importantly, we will provide possible scenarios of future invasions for defense and detection.

[cb22] From Parroting to Echoing: The Evolution of China’s Bots-Driven Info...
[cb22]  From Parroting to Echoing:  The Evolution of China’s Bots-Driven Info...[cb22]  From Parroting to Echoing:  The Evolution of China’s Bots-Driven Info...
[cb22] From Parroting to Echoing: The Evolution of China’s Bots-Driven Info...

Social media is no doubt a critical battlefield for threat actors to launch InfoOps, especially in a critical moment such as wartime or the election season. We have seen Bot-Driven Information Operations (InfoOps, aka influence campaign) have attempted to spread disinformation, incite protests in the physical world, and doxxing against journalists. China's Bots-Driven InfoOps, despite operating on a massive scale, are often considered to have low impact and very little organic engagement. In this talk, we will share our observations on these persistent Bots-Driven InfoOps and dissect their harmful disinformation campaigns circulated in cyberspace. In the past, most bots-driven operations simply parroted narratives of the Chinese propaganda machine, mechanically disseminating the same propaganda and disinformation artifacts made by Chinese state media. However, recently, we saw the newly created bots turn to post artifacts in a livelier manner. They utilized various tactics, including reposting screenshots of forum posts and disguised as members of “Milk Tea Alliance,” to create a false appearance that such content is being echoed across cyberspace. We particularly focus on an ongoing China's bots-driven InfoOps targeting Taiwan, which we dub "Operation ChinaRoot." Starting in mid-2021, the bots have been disseminating manipulated information about Taiwan's local politics and Covid-19 measures. Our further investigation has also identified the linkage between Operation ChinaRoot and other Chinese state-linked networks such as DRAGONBRIDGE and Spamouflage.

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Malwares written in Go is increasing every year. Go's cross-platform nature makes it an opportune language for attackers who wish to target multiple platforms. On the other hand, the statically linked libraries make it difficult to distinguish between user functions and libraries, making it difficult for analysts to analyze. This situation has increased the demand for Go malware classification and exploration. In this talk, we will demonstrate the feasibility of computing similarity and classification of Go malware using a newly proposed method called gimpfuzzy. We have implemented "gimpfuzzy", which incorporates Fuzzy Hashing into the existing gimphash method. In this talk, we will verify the discrimination rate of the classification using the proposed method and confirm the validity of the proposed method by discussing some examples from the classified results. We will also discuss issues in Go-malware classification.

[cb22] Mal-gopherとは?Go系マルウェアの分類のためのgimpfuzzy実装と評価 by 澤部 祐太, 甘粕 伸幸, 野村 和也
[cb22] Mal-gopherとは?Go系マルウェアの分類のためのgimpfuzzy実装と評価 by 澤部 祐太, 甘粕 伸幸, 野村 和也[cb22] Mal-gopherとは?Go系マルウェアの分類のためのgimpfuzzy実装と評価 by 澤部 祐太, 甘粕 伸幸, 野村 和也
[cb22] Mal-gopherとは?Go系マルウェアの分類のためのgimpfuzzy実装と評価 by 澤部 祐太, 甘粕 伸幸, 野村 和也

Goで書かれたマルウェアは年々増加している。Goはクロスプラットフォームの性質を持っており、複数のプラットフォームを標的にしたい攻撃者にとって好都合な言語である。その一方で、ライブラリが静的にリンクされていることからユーザ関数とライブラリの区別が難しく、アナリストにとって解析が困難である。そうした状況で、Goマルウェアの分類や探索の需要が高まっている。 本講演ではgimpfuzzyという新たな提案手法を用いてGoマルウェアに対し類似性の計算や分類が可能であることを検証する。われわれは既存手法であるgimphashにFuzzy Hashingを組み込んだ「gimpfuzzy」を新たに実装した。講演では提案手法を利用し���分類の判別率を検証し、分類された結果の中からいくつかの事例を取り上げその妥当性について確認する。また、Goマルウェアの分類における課題についても検討を行う予定である。

[cb22] ブロックチェーンにC&Cサーバー情報を隠ぺいした攻撃者との直接対峙により得られたもの by 谷口 剛
[cb22]  ブロックチェーンにC&Cサーバー情報を隠ぺいした攻撃者との直接対峙により得られたもの by 谷口 剛[cb22]  ブロックチェーンにC&Cサーバー情報を隠ぺいした攻撃者との直接対峙により得られたもの by 谷口 剛
[cb22] ブロックチェーンにC&Cサーバー情報を隠ぺいした攻撃者との直接対峙により得られたもの by 谷口 剛

本研究では 2019 年 11 月から C&C サーバーの IP アドレスをブロックチェーンに隠ぺいした攻撃者のビットコイン運用監視を開始した。2020 年 6 月に C&C サーバ通信をシンクホールサーバへ直接誘導する (テイクオーバーと呼ぶ) アイデアによる国際協業を Hasso Plattner Institute の Christian Doerr 教授と開始し、8 月にテイクオーバーに成功した。攻撃者のテイクオーバー回避は早く、約 2 週間で回避メカニズムを実装し攻撃を再開した。テイクオーバーは回避されてしまったが、ビットコイン運用監視は機能し続けた。この攻撃の終息はビットコイン高騰がきっかけとなった。ビットコイン取引における採掘者への手数料が利益を圧迫する要因となり、2021 年 1 月に最後の C&C 情報の更新、3 月に攻撃インフラ放棄を確認した。その後、本研究の監視範囲において同種の攻撃は観察されていない。 この攻撃はすでに終息し、ビットコインの価値が下がらない限り再開される可能性は低いが、本講演では攻撃者との直接対峙により得られたノウハウを共有したい。つまり、攻撃者と対峙していた当時、この攻撃は新規性が高く、攻撃者自身も最適な運用方法を理解できていなかった。運用しながら攻撃手法を進化させる必要があり、われわれも攻撃手法を慎重に分析しながら隙を狙っていた。さらに厄介なのが、攻撃者がビットコイン半減期の影響を受けたり、単純な運用ミスをしたりして、そのたびに、われわれも攻撃者の意図を可能な限り早く理解しなければならなかったという点だ。この対峙により得られた知見は、本講演者による CODE BLUE 講演でも活かしており、本質的なノウハウとして共有する。

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[cb22] SMARTIAN: Enhancing Smart Contract Fuzzing with Static and Dynamic Da...
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Smartian is a tool that enhances smart contract fuzzing with static and dynamic data-flow analyses. It integrates static analysis to identify promising sequences of function calls for generating initial fuzzing seeds. It then uses dynamic analysis to mutate function arguments to realize expected data flows across functions. Smartian implements bug oracles for 13 classes of smart contract bugs. Evaluation shows Smartian outperforms other fuzzers and symbolic executors on benchmarks with known bugs, demonstrating the effectiveness of integrating static and dynamic analyses for smart contract fuzzing.

[cb22] Scaling the Security Researcher to Eliminate OSS Vulnerabilities Once ...
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[cb22] Scaling the Security Researcher to Eliminate OSS Vulnerabilities Once ...

Imagine a world where a security researcher becomes aware of a security vulnerability, impacting thousands of Open Source Software (OSS) projects, and is enabled to both identify and fix them all at once. Now imagine a world where a vulnerability is introduced into your production code and a few moments later you receive an automated pull request to fix it. Hundreds of thousands of human hours are invested every year in finding common security vulnerabilities with relatively simple fixes. These vulnerabilities aren't sexy, cool, or new, we've known about them for years, but they're everywhere! The scale of GitHub and tools like CodeQL (GitHub's code query language) enable one to scan for vulnerabilities across hundreds of thousands of OSS projects, but the challenge is how to scale the triaging, reporting, and fixing. Simply automating the creation of thousands of bug reports by itself isn't useful, and would be even more of a burden on volunteer maintainers of OSS projects. Ideally, the maintainers would be provided with not only information about the vulnerability, but also a fix in the form of an easily actionable pull request. When facing a problem of this scale, what is the most efficient way to leverage researcher knowledge to fix the most vulnerabilities across OSS? This talk will cover a highly scalable solution - automated bulk pull request generation. We'll discuss the practical applications of this technique on real world OSS projects. We'll also cover technologies like CodeQL and OpenRewrite (a style-preserving refactoring tool created at Netflix and now developed by Moderne). Let's not just talk about vulnerabilities, let's actually fix them at scale. This work is sponsored by the new Dan Kaminsky Fellowship; a fellowship created to celebrate Dan's memory and legacy by funding open-source work that makes the world a better (and more secure) place.

[cb22] Lets Dance in the Cache Destabilizing Hash Table on Microsoft IIS by O...
[cb22] Lets Dance in the Cache Destabilizing Hash Table on Microsoft IIS by O...[cb22] Lets Dance in the Cache Destabilizing Hash Table on Microsoft IIS by O...
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Hash Table, as the most fundamental Data Structure in Computer Science, is extensively applied in Software Architecture to store data in an associative manner. However, its architecture makes it prone to Collision Attacks. To deal with this problem, 25 years ago, Microsoft designed its own Dynamic Hashing algorithm and applied it everywhere in IIS, the Web Server from Microsoft, to serve various data from HTTP Stack. As Hash Table is everywhere, isn't the design from Microsoft worth scrutinizing? We dive into IIS internals through months of Reverse-Engineering efforts to examine both the Hash Table implementation and the use of Hash Table algorithms. Several types of attacks are proposed and uncovered in our research, including (1) A specially designed Zero-Hash Flooding Attack against Microsoft's self-implemented algorithm. (2) A Cache Poisoning Attack based on the inconsistency between Hash-Keys. (3) An unusual Authentication Bypass based on a hash collision. By understanding this talk, the audience won't be surprised why we can destabilize the Hash Table easily. The audience will also learn how we explore the IIS internals and will be surprised by our results. These results could not only make a default installed IIS Server hang with 100% CPU but also modify arbitrary HTTP responses through crafted HTTP request. Moreover, we'll demonstrate how we bypass the authentication requirement with a single, crafted password by colliding the identity cache!

© 2022 JPCERT/CC
76
Analysis Results
Demo
© 2022 JPCERT/CC
78
Memory Forensic on Cloud with IaC
GitHub Actions
GitHub
Run
commit
Client
AWS Cloud
S3 output bucket
Submit job
Lambda Batch
CodeCommit ECR
Run
Clone
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Image pull
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EventBridge
SNS
CloudWatch
apply
© 2022 JPCERT/CC
79
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limit Lambda
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runtime 900s - 540s 3600s
memory 10G 30G 16G 32G
data 10G 200G 16G 32G
81
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and data size.
Check the limits and select a serverless service.
© 2022 JPCERT/CC
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https://jsac.jpcert.or.jp/archive/2021/pdf/JSAC2021_201_haruyama_jp.pdf
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  • 1. © 2022 JPCERT/CC 0 Fight Against Malware Development Life Cycle CODE BLUE 2022
  • 2. © 2022 JPCERT/CC 1 Problem of Incident Response Attacker vs Defender
  • 3. © 2022 JPCERT/CC 2 Problem of Incident Response Attacker vs Defender We are in the Malware Development Life Cycle…
  • 4. © 2022 JPCERT/CC Surface Analysis Runtime Analysis Static Analysis Create YARA Rule Finished analysis … 3 Malware Analysis Operations (MAOps)
  • 5. © 2022 JPCERT/CC Surface Analysis Runtime Analysis Static Analysis Create YARA Rule Automation! 4 All roads lead to AUTOMATION!
  • 6. © 2022 JPCERT/CC 5 Problem of MAOps Attacker vs Malware Analyst
  • 7. © 2022 JPCERT/CC 6 Problem of MAOps Attacker vs Malware Analyst Changed C2 server Changed encrypt key Added new function
  • 8. © 2022 JPCERT/CC 7 Problem of MAOps Attacker vs Malware Analyst STOP
  • 9. © 2022 JPCERT/CC Maintenance of malware analysis tools is hard work. Creating a lot of malware analysis tools requires the maintenance of many tools. 8 Problem of MAOps Need the methods to counter the attacker's development lifecycle
  • 10. © 2022 JPCERT/CC 9 Goal of This Presentation This presentation shares methods for building analytical tools to counter the attacker's lifecycle.
  • 11. © 2022 JPCERT/CC Approach Technologies such as CI/CD and serverless are methods that make system maintenance and release more efficient. Cloud services can be a solution to the problems of malware analysts. We managed the Malware Analysis System on Cloud. 10 Use cloud services CI/CD, serverless, and Infrastructure as Code (IaC) to fight the malware development life cycle.
  • 12. © 2022 JPCERT/CC IP addresses can be changed dynamically Easy to update analysis tools Easy to build scale-out systems No server provisioning or maintenance required Low maintenance costs 11 Advantages of Using the Cloud for Malware Analysis
  • 13. © 2022 JPCERT/CC 12 Malware Analysis System on Cloud Scanner Memory Forensic Surface Analysis Sandbox YARA Log Analysis Thread Research
  • 14. © 2022 JPCERT/CC Presentation Topics 13 1 Malware C2 Monitoring 2 Malware Hunting using Cloud 3 YARA CI/CD system 4 Surface Analysis System on Cloud 5 Memory Forensic on Cloud
  • 15. © 2022 JPCERT/CC 14 1 Malware C2 Monitoring 2 Malware Hunting using Cloud 3 YARA CI/CD system 4 Surface Analysis System on Cloud 5 Memory Forensic on Cloud
  • 16. © 2022 JPCERT/CC Blocked access to the C2 server. Targeted by an attacker. Cloud services solve these problems. 15 Malware C2 Monitoring Monitoring the attacker's C2 server is important to understand their activity. Notes
  • 17. © 2022 JPCERT/CC 16 Case1: Monitoring the Lucky Visitor Scam C2
  • 18. © 2022 JPCERT/CC 17 Lucky Visitor Scam Infrastructure C2 Server Compromised Server Scam Server Website access #1 Send client information #2 Reply scam server URL #3 Send scam server URL #4 Access scam website #5
  • 19. © 2022 JPCERT/CC 18 PHP Backdoor Installed in Compromised Server C2 Server
  • 20. © 2022 JPCERT/CC $ curl -i -X POST http[:]//144.76.47[.]168/app/assets/api2?action=redir -d "ip=153.213.10.51&qs=dfaljfadfa.com%2Findex.php%3Ffc858f%3DiPhone-Apple-Windows&ua=Mozilla%2F5.0 (...) &conn=close&uid=fb06bc98-576a-d5df-2195-a4b0a64bec44" -H "User-Agent: " -H "Accept: " HTTP/1.1 200 OK Server: nginx/1.14.2 Date: Tue, 23 Feb 2021 23:46:35 GMT Content-Type: text/html; charset=utf-8 Content-Length: 590 Connection: keep-alive X-Frame-Options: DENY X-Content-Type-Options: nosniff Referrer-Policy: same-origin a:2:{s:4:"type";s:5:"redir";s:4:"data";a:1:{s:4:"code";s:524:"<html> <head> <META http-equiv="refresh" content="1;URL=https[:]//finishsandperson- 9[.]life/?utm_campaign=pEv9cTd8QNHYzqqr5UNFx2COHvnp_JE3r8uVIhm3Qww1&t=main7d"> <script> window.location = "https[:]//finishsandperson- 9[.]life/?utm_campaign=pEv9cTd8QNHYzqqr5UNFx2COHvnp_JE3r8uVIhm3Qww1&t=main7d"; </script> </head> <body> To the new location please <a href="https[:]//finishsandperson- 9[.]life/?utm_campaign=pEv9cTd8QNHYzqqr5UNFx2COHvnp_JE3r8uVIhm3Qww1&t=main7d"><b>click here.</b></a> </body> </html>";}} 19 Request to C2 for Redirect URL
  • 21. © 2022 JPCERT/CC Check  Displays the number of files stored on the server templates • Creates a .html file for templates keywords • Creates a .lst file for keywords update_sitemap • Updates sitemap.xml 20 PHP Backdoor Installed in Compromised Server pages  Creates a new page ping  Sends a sitemap.xml URL to google and bing robots  Creates robots.txt eval  Runs PHP code PHP Backdoor Features
  • 22. © 2022 JPCERT/CC IP Address Status 144.76.47.168 〇 144.76.51.144 × 178.63.30.186 〇 178.63.30.30 〇 178.63.34.6 × 5.9.146.0 ー 5.9.235.245 〇 5.9.239.221 × 5.9.34.13 〇 5.9.37.245 × 94.130.71.28 × 21 Lucky Visitor Scam C2 Attackers use different C2 servers over time.
  • 23. © 2022 JPCERT/CC Get PHP content form VT Check C2 Get redirect URL form C2 Report to Google safe browsing Blocked by web browser 22 Flow of Lucky Visitor Scam Countermeasures Automatically execute the below process, from discovering C2 servers to blocking redirects in web browsers.
  • 24. © 2022 JPCERT/CC 23 Monitoring the Lucky Visitor Scam C2 System (AWS) C2 AWS Lambda Amazon EventBridge GitHub Actions Private Public AWS GitHub Git push Git push Event Get HTML content Amazon S3 output bucket Put IoC Get IoC Git checkout Google Safe Browsing Report VirusTotal
  • 25. © 2022 JPCERT/CC 24 Monitoring the Lucky Visitor Scam C2 System https://github.com/JPCERTCC/Lucky-Visitor-Scam-IoC
  • 26. © 2022 JPCERT/CC 25 Monitoring the Lucky Visitor Scam C2 System (GCP) C2 Cloud Functions Cloud Scheduler GitHub Actions Private Public GitHub Git push Git push Event Get HTML content Cloud Storage Put IoC Get IoC Git checkout Google Safe Browsing Report
  • 27. © 2022 JPCERT/CC 26 Access Control for C2 Server C2 C2 server is inaccessible from Japan.
  • 28. © 2022 JPCERT/CC 27 C2 Activity 0 50 100 150 200 250 300 350 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 (Count) (Time UTC+9) Investigation of the attacker’s redirect URL delivery cycle.
  • 29. © 2022 JPCERT/CC 28 1 Malware C2 Monitoring 2 Malware Hunting using Cloud 3 YARA CI/CD system 4 Surface Analysis System on Cloud 5 Memory Forensic on Cloud
  • 30. © 2022 JPCERT/CC Get IoCs Download malware Analyze malware Hunt and detect incident 29 Malware Hunting using Cloud Malware hunting gets unknown IoCs and can be used for incident detection.
  • 31. © 2022 JPCERT/CC Get IoCs Download malware Analyze malware Hunt and detect incident 30 Malware Hunting using Cloud Malware hunting gets unknown IoCs and can be used for incident detection.
  • 32. © 2022 JPCERT/CC 31 Case2: Cobalt Strike Beacon Hunting Cobalt Strike is a popular penetration tool among attackers. [1] Cobalt Strike's C2s are found in large numbers and cannot all be analyzed manually.
  • 33. © 2022 JPCERT/CC Get C2 server form VT Download Cobalt Strike beacon Analysis Cobalt Strike beacon Publish configuration 32 Flow of Cobalt Strike Beacon Hunting Automatically execute from discovering C2 servers to hunting Cobalt Strike beacon.
  • 34. © 2022 JPCERT/CC 33 C2 Server Data https://www.virustotal.com/api/v3/intelligence/search?query=entity%3Aip%20c omment%3A%23cobaltstrike&limit=300&descriptors_only=true VT API Extract a list of IP addresses of Cobalt Strike's servers from VirusTotal.
  • 35. © 2022 JPCERT/CC 32bit /aaa9 /aab8 64bit /aab9 /aac8 34 Default URL Path for Cobalt Strike Beacon Many C2 servers do not change the default URL path. Default URL path for Cobalt Strike beacon
  • 36. © 2022 JPCERT/CC 35 Configuration Data Encode configuration data in Cobalt Strike beacon XOR 0x2E
  • 37. © 2022 JPCERT/CC set Description Remarks 0x01 BeaconType 0=HTTP, 1=Hybrid HTTP and DNS, 8=HTTPS 0x02 Port number 0x03 Polling time 0x04 Unknown 0x05 Jitter Ratio of jitter in polling time (0- 99%) 0x06 Maxdns Maximum length of host name when using DNS (0-255) 0x07 Unknown 0x08 Destination host 0x09 User agent 0x0a Path when communicating HTTP_Header2 0x0b Unknown 0x0c HTTP_Header1 0x0d HTTP_Header2 0x0e Injection process 0x0f Pipe name 0x10 Year Stops operating after the specified date by Year, Month, Day 36 Configuration Index set Description Remarks 0x11 Month 0x12 Day 0x13 DNS_idle 0x14 DNS_Sleep 0x1a HTTP_Method1 0x1b HTTP_Method2 0x1c Unknown 0x1d Process to inject arbitrary shellcode (32bit) 0x1e Process to inject arbitrary shellcode (64bit) 0x1f Unknown 0x20 Proxy server name 0x21 Proxy user name 0x22 Proxy password 0x23 AccessType 1 = Do not use proxy server 2 = Use IE configuration in the registry 4 = Connect via proxy server 0x24 create_remote_thread Flag whether to allow creating threads in other processes 0x25 Not in use
  • 38. © 2022 JPCERT/CC 37 Cobalt Strike Beacon Hunting System Cobalt Strike C2 Lambda EventBridge GitHub Actions Private Public AWS Cloud GitHub Git push Git push Event Get beacon S3 output bucket Put Config Get Config Git checkout Put Config VirusTotal Get server list API Gateway WAF Run REST API Client
  • 39. © 2022 JPCERT/CC 38 Published Configuration Repository https://github.com/JPCERTCC/CobaltStrike-Config
  • 40. © 2022 JPCERT/CC 39 Cobalt Strike Beacon Hunting System Cobalt Strike C2 Lambda EventBridge GitHub Actions Private Public AWS Cloud GitHub Git push Git push Event Get beacon S3 output bucket Put Config Get Config Git checkout Put Config VirusTotal Get server list API Gateway WAF Run REST API Client
  • 41. © 2022 JPCERT/CC 40 REST API https://[mask]/cs-scan-api?scan=http://185.38.142.75:80/aaa9 You do not need to download Cobalt Strike Beacon to analyze.
  • 42. © 2022 JPCERT/CC 41 Changes in Cobalt Strike C2 (Daily) 0 20 40 60 80 100 120 140 160 180 200 2/17/2022 3/17/2022 4/17/2022 5/17/2022 6/17/2022 7/17/2022 8/17/2022 (Count) (Date)
  • 43. © 2022 JPCERT/CC 42 1 Malware C2 Monitoring 2 Malware Hunting using Cloud 3 YARA CI/CD system 4 Surface Analysis System on Cloud 5 Memory Forensic on Cloud
  • 44. © 2022 JPCERT/CC Since creating YARA rules cannot be automated, malware analysts create them manually, and they spend too much time on it. Some characteristic malware can be created automatically. 43 YARA CI/CD System Creation of YARA rules must be done manually by the malware analyst, and automation of YARA rule creation is a challenge.
  • 45. © 2022 JPCERT/CC 44 Case3: HUI Loader Analysis System Legitimate HUI Loader Encoded Malware DLL Data HUI Loader used in APT10, Blue Termite, A41APT and DEV-0401. for i in range(len(enc_data)): data = ord(enc_data[i]) ^ 0x20 ^ ord(key[i % len(key)]) dec_data.append(data) Decode code
  • 46. © 2022 JPCERT/CC 2015 2016 2017 2018 2019 2020 2021 2022 45 Overview of HUI Loader Timeline 2015 Jan APT10 started using HUI loader 2015 Apr Blue Termite started using HUI loader 2020 Jun A41APT started using HUI loader 2021 Aug DEV-0401 started using HUI loader 2016 Jul  Password randomized  DLL injection 2021 Dec  Security features bypassed  "HUI…" strings deleted
  • 47. © 2022 JPCERT/CC 46 Common Problems with Loader Legitimate HUI Loader Encoded Malware DLL Data Even if the Loader is found, the function of the malware is unknown because the encoded malware cannot be found. Found it! Not found!
  • 48. © 2022 JPCERT/CC Get HUI Loader form VT Analysis HUI Loader Create YARA rule Push YARA rule to VT 47 Flow of HUI Loader Analysis System Automatically execute from getting HUI Loader to creating the YARA rule.
  • 49. © 2022 JPCERT/CC 48 HUI Loader Analysis System Lambda EventBridge Github Action Private Public AWS Cloud Github Git push Git push Event S3 output bucket Put Config Get Result Git checkout Put Config VirusTotal Get HUI Loader API Gateway WAF Run REST API Client Push YARA rule
  • 50. © 2022 JPCERT/CC 49 Created YARA rule rule malware_HUILoader_data_2317d3e14a { meta: description = "HUI Loader loading data" author = "JPCERT/CC Incident Response Group" HUI_loader_hash = "2317d3e14ab214f06ae38a729524646971e21b…" condition: uint32(0) == 0x7A8F473A or vt.metadata.file_name iequals "vlc.cnf" } The file name and encoding key are unique to each loader, and thus rules must be created for each it. YARA rule
  • 51. © 2022 JPCERT/CC 50 Published Analysis Results Repository https://github.com/JPCERTCC/HUILoader-research
  • 52. © 2022 JPCERT/CC 51 1 Malware C2 Monitoring 2 Malware Hunting using Cloud 3 YARA CI/CD system 4 Surface Analysis System on Cloud 5 Memory Forensic on Cloud
  • 53. © 2022 JPCERT/CC 52 Surface Analysis System on Cloud Confusing: each type of malware is called different names by different vendors.
  • 54. © 2022 JPCERT/CC 53 Surface Analysis System on Cloud Malware names are different for each security vendor and get confusing when looking at each reports. What is this malware? I want to scan using my YARA rule…
  • 55. © 2022 JPCERT/CC 54 Surface Analysis System on Cloud Same issues on twitter…
  • 56. © 2022 JPCERT/CC 55 Surface Analysis System on Cloud Same issues on twitter… What is this malware? I want to scan using my YARA rule…
  • 57. © 2022 JPCERT/CC 56 Can sandbox alone solve all problems?
  • 58. © 2022 JPCERT/CC 57 Can sandbox alone solve all problems? No. Sandbox analysis results need to be analyzed further, which is time consuming.
  • 59. © 2022 JPCERT/CC Simple Fewer steps to execute Execute multiple analyses all at once Easy to customize Store analysis results 58 Our Requirements for Malware Analysis Tools
  • 60. © 2022 JPCERT/CC YARA scan floss capa olevba Sandbox My toolset 59 For Example These analyses are executed in a single step.
  • 61. © 2022 JPCERT/CC Send analysis job from web browser Get malware form VT Analyze malware for selected tools Save analysis results 60 Flow of Surface Analysis System on Cloud Analysis results in a single action for multiple malware.
  • 64. © 2022 JPCERT/CC 63 Surface Analysis System on Cloud AWS Cloud S3 REST VirusTotal API Gateway WAF Client Submit job Lambda Batch CodeCommit ECR Run Clone Get malware Push results Image pull Lambda EventBridge EventBridge SNS CloudWatch Twitter Get twitter data Status notification Blog Get contents Submit job
  • 65. Demo
  • 66. © 2022 JPCERT/CC 65 Infrastructure as Code (IaC) Code and manage the building of the Surface Analysis System on cloud. Heavy use of IaC increases the cost of managing the code. Limit the cases in which IaC is used. Simple systems that do not need to use IaC. Problem
  • 67. © 2022 JPCERT/CC 66 IaC using Terraform Building analysis system using Terraform
  • 68. © 2022 JPCERT/CC 67 Surface Analysis System on Cloud with IaC GitHub Actions Private Github run apply commit AWS Cloud Client S3 API Gateway WAF Lambda Batch CodeCommit ECR Lambda EventBridge EventBridge SNS CloudWatch
  • 70. © 2022 JPCERT/CC 69 Surface Analysis System on Cloud
  • 71. © 2022 JPCERT/CC 70 1 Malware C2 Monitoring 2 Malware Hunting using Cloud 3 YARA CI/CD system 4 Surface Analysis System on Cloud 5 Memory Forensic on Cloud
  • 72. © 2022 JPCERT/CC Investigating multiple hosts simultaneously is time-consuming. Memory forensic system must scale out. Build a memory forensic system that scale out on cloud service. 71 Memory Forensic on Cloud Increased memory size has become a bottleneck for memory forensic investigations.
  • 73. © 2022 JPCERT/CC Get memory images Send memory images to cloud Run Volatility 3 analysis system (docker image) Save analysis results 72 Flow of Memory Forensic on Cloud Build a memory forensic system that scale out on cloud service.
  • 74. © 2022 JPCERT/CC 73 Memory Forensic on Cloud AWS Cloud S3 output bucket Client Submit job Lambda Batch CodeCommit ECR Run Clone Push results Image pull Memory Image S3 EventBridge Analysis results REST WAF API Gateway Run EventBridge SNS CloudWatch Status notification
  • 75. © 2022 JPCERT/CC 74 Memory Forensic on Cloud AWS Cloud S3 output bucket Client Submit job Lambda Batch CodeCommit ECR Run Clone Push results Image pull Memory Image S3 EventBridge Analysis results REST WAF API Gateway Run EventBridge SNS CloudWatch Status notification Scale out
  • 78. Demo
  • 79. © 2022 JPCERT/CC 78 Memory Forensic on Cloud with IaC GitHub Actions GitHub Run commit Client AWS Cloud S3 output bucket Submit job Lambda Batch CodeCommit ECR Run Clone Push results Image pull S3 EventBridge Analysis results WAF API Gateway Run EventBridge SNS CloudWatch apply
  • 80. © 2022 JPCERT/CC 79 Surface Analysis System on Cloud
  • 81. Tips
  • 82. © 2022 JPCERT/CC limit Lambda Batch (Fargate) Cloud Functions Cloud Run runtime 900s - 540s 3600s memory 10G 30G 16G 32G data 10G 200G 16G 32G 81 Limited of Serverless Service Serverless service have limited runtime, memory and data size. Check the limits and select a serverless service.
  • 83. © 2022 JPCERT/CC API Gateway • 10MB presigned url s3 • 5GB AWS CLI • 160GB 82 Transfer of Large Data Limited transfer of large data, such as memory images, log file etc. CUI is required for large data transfers. For Example (AWS)
  • 84. © 2022 JPCERT/CC 83 Use as a Serverless System for GitHub Actions GitHub Actions can also be used as a serverless system. (free: 3000min/month)
  • 85. © 2022 JPCERT/CC 84 Monitoring the Lucky Visitor Scam C2 System (GitHub) C2 AWS Lambda Amazon EventBridge GitHub Actions Private Public AWS Cloud GitHub Git push Git push Event Get HTML content Amazon S3 output bucket Put IoC Git checkout Google Safe Browsing Report Get IoC
  • 86. © 2022 JPCERT/CC Share MAOps (Malware Analysis Operations) that fight the malware development life cycle. Introduce the case study on how to build a malware analysis system with low maintenance costs. Learn how to build the Malware Analysis System on Cloud. 85 Takeaways (repost)
  • 87. Thank you! @jpcert_en ir-info@jpcert.or.jp PGP https://www.jpcert.or.jp/english/pgp/ @jpcert_ac
  • 88. © 2022 JPCERT/CC [1] JSAC2021: Knock, knock, Neo. - Active C2 Discovery Using Protocol Emulation https://jsac.jpcert.or.jp/archive/2021/pdf/JSAC2021_201_haruyama_jp.pdf 87 Reference