What it does. Investigate AWS account compromise by querying CloudTrail with boto3's LookupEvents Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).

Install

• npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-cloud-forensics-with-aws-cloudtrail, or copy the skill folder into ~/.claude/skills/performing-cloud-forensics-with-aws-cloudtrail/.
• Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/performing-cloud-forensics-with-aws-cloudtrail/SKILL.md

SKILL.md (verbatim)

name: performing-cloud-forensics-with-aws-cloudtrail
description: Investigate AWS account compromise by querying CloudTrail with boto3's LookupEvents
  or AWS Athena SQL over S3-delivered logs, filtering on suspicious user agents, source IPs, and
  event names to reconstruct an attacker timeline. Use when tracing unauthorized API calls, S3
  data exfiltration, IAM privilege escalation, or credential exposure, and building a forensic
  report of findings and remediation steps.
domain: cybersecurity
subdomain: cloud-security
tags:
- cloud-security
- aws
- cloudtrail
- forensics
- incident-response
- dfir
- boto3
- s3
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- PR.IR-01
- ID.AM-08
- GV.SC-06
- DE.CM-01
mitre_attack:
- T1078.004
- T1530
- T1537
- T1580
- T1003

Performing Cloud Forensics with AWS CloudTrail

When to Use

• When investigating suspected AWS account compromise
• After detecting unauthorized API calls or credential exposure
• During incident response involving cloud infrastructure
• When analyzing S3 data exfiltration or IAM privilege escalation
• For post-incident forensic timeline reconstruction

Prerequisites

• AWS account with CloudTrail enabled (management and data events)
• IAM permissions for cloudtrail:LookupEvents, s3:GetObject, athena:StartQueryExecution
• boto3 Python SDK installed
• CloudTrail logs delivered to S3 with optional Athena table configured
• AWS CLI configured with appropriate credentials

Workflow

1. Scope Investigation: Identify timeframe, affected accounts, and compromised credentials.
2. Query CloudTrail: Use boto3 lookup_events or Athena to retrieve relevant API events.
3. Filter by Indicators: Search for suspicious user agents, source IPs, and event names.
4. Reconstruct Timeline: Build chronological sequence of attacker actions from API calls.
5. Analyze Access Patterns: Identify data access, IAM changes, and resource modifications.
6. Identify Persistence: Check for new IAM users, access keys, roles, or Lambda functions.
7. Generate Report: Produce forensic timeline with findings and remediation steps.

Key Concepts

Tools & Systems

Output Format

Forensic Report: AWS-IR-[DATE]-[SEQ]
Account: [AWS Account ID]
Timeframe: [Start] to [End]
Compromised Credentials: [Access Key IDs]
Suspicious Events: [Count]
Source IPs: [List of attacker IPs]
Actions Taken: [API calls by attacker]
Data Accessed: [S3 objects, secrets, etc.]
Persistence Mechanisms: [New users, keys, roles]

Other files in this skill

• LICENSE
• references/api-reference.md
• scripts/agent.py

references/api-reference.md (verbatim)

AWS CloudTrail Forensics API Reference

boto3 CloudTrail Client

import boto3
client = boto3.client("cloudtrail", region_name="us-east-1")

lookup_events

response = client.lookup_events(
    LookupAttributes=[
        {"AttributeKey": "Username", "AttributeValue": "compromised-user"},
    ],
    StartTime=datetime(2025, 1, 1),
    EndTime=datetime(2025, 1, 2),
    MaxResults=50,
)

LookupAttributes Keys

Response Structure

{
    "Events": [
        {
            "EventId": "abc123",
            "EventName": "CreateUser",
            "EventTime": "2025-01-01T12:00:00Z",
            "Username": "attacker",
            "CloudTrailEvent": "{\"sourceIPAddress\":\"1.2.3.4\",\"userAgent\":\"aws-cli/2.0\",...}"
        }
    ],
    "NextToken": "..."
}

Paginator Usage

paginator = client.get_paginator("lookup_events")
for page in paginator.paginate(
    LookupAttributes=[{"AttributeKey": "AccessKeyId", "AttributeValue": "AKIA..."}],
    StartTime=start, EndTime=end
):
    for event in page["Events"]:
        ct = json.loads(event["CloudTrailEvent"])
        print(ct["sourceIPAddress"], ct["eventName"])

AWS CLI Equivalents

# Lookup events by username
aws cloudtrail lookup-events \
  --lookup-attributes AttributeKey=Username,AttributeValue=compromised-user \
  --start-time 2025-01-01T00:00:00Z \
  --output json

# Search by access key
aws cloudtrail lookup-events \
  --lookup-attributes AttributeKey=AccessKeyId,AttributeValue=AKIAEXAMPLE \
  --max-results 50

Athena Query for S3 CloudTrail Logs

SELECT eventtime, eventname, useridentity.arn, sourceipaddress, useragent,
       requestparameters, responseelements, errorcode
FROM cloudtrail_logs
WHERE eventtime BETWEEN '2025-01-01' AND '2025-01-02'
  AND useridentity.accesskeyid = 'AKIAEXAMPLE'
ORDER BY eventtime;

Key Forensic Event Names

Suspicious User Agents

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