Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
日本語の概要は準備中です。原文の説明を表示しています。
Executes authorized phishing simulation campaigns to assess an organization's susceptibility to email-based social engineering attacks. The tester designs realistic phishing scenarios, builds credential harvesting infrastructure, sends targeted phishing emails, and tracks open rates, click-through rates, and credential submission rates to measure human security awareness. Activates for requests involving phishing simulation, social engineering assessment, email security testing, or security awareness measurement.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Do not use without explicit written authorization from the organization's leadership, for actual credential theft beyond the authorized scope, for targeting individuals personally rather than professionally, or for sending phishing emails that could cause psychological harm or legal liability.
Design realistic phishing scenarios based on threats relevant to the target organization:
Configure the phishing infrastructure:
target-corp.com, targetcorp-portal.com, targetsupport.netLaunch the phishing campaign:
Process captured credentials to demonstrate impact (if authorized):
Analyze campaign results and produce the assessment report:
| Term | Definition |
|---|---|
| Pretext | The fabricated scenario and social context used to persuade the target to take a desired action such as clicking a link or entering credentials |
| Credential Harvesting | Collecting usernames and passwords through fake login pages that mimic legitimate services |
| GoPhish | Open-source phishing simulation platform that manages email templates, landing pages, target groups, and campaign tracking |
| Spear Phishing | Targeted phishing directed at specific individuals using personalized information gathered through reconnaissance |
| Typosquatting | Registering domains that are visually similar to legitimate domains through character substitution, addition, or omission |
| Security Awareness | Training programs designed to educate employees about social engineering threats and proper reporting procedures |
| DMARC | Domain-based Message Authentication, Reporting, and Conformance; email authentication protocol that prevents unauthorized use of a domain for sending email |
Context: A 2,000-employee company has never conducted a phishing simulation. The CISO wants to establish a baseline susceptibility rate before deploying a new security awareness training program. The campaign should test all employees using a realistic but not overly sophisticated pretext.
Approach:
m365-targetcorp.com, set up GoPhish, and build a landing page cloning the Microsoft 365 login portalPitfalls:
## Phishing Simulation Campaign Report
**Campaign Name**: Q4 2025 Baseline Phishing Assessment
**Pretext**: Microsoft 365 Password Expiration Notice
**Campaign Duration**: November 15-18, 2025
**Target Population**: 2,000 employees (all departments)
### Campaign Metrics
| Metric | Count | Rate |
|--------|-------|------|
| Emails Sent | 2,000 | 100% |
| Emails Delivered | 1,847 | 92.4% |
| Emails Opened | 1,243 | 67.3% |
| Links Clicked | 487 | 26.4% |
| Credentials Submitted | 312 | 16.9% |
| Reported to IT | 23 | 1.2% |
### Department Breakdown
| Department | Employees | Clicked | Submitted | Reported |
|------------|-----------|---------|-----------|----------|
| Finance | 120 | 38.3% | 28.3% | 0.8% |
| Marketing | 85 | 35.3% | 24.7% | 1.2% |
| Engineering| 300 | 15.0% | 8.3% | 3.7% |
| IT | 45 | 8.9% | 4.4% | 11.1% |
### Key Findings
1. Baseline credential submission rate of 16.9% exceeds industry average (12%)
2. Report rate of 1.2% indicates employees are not trained to report suspicious emails
3. Finance department is the highest-risk group with 28.3% credential submission rate
4. Email security gateway did not flag the phishing domain despite being registered 48 hours prior
### Recommendations
1. Deploy mandatory security awareness training with emphasis on phishing identification
2. Install a phishing report button in email clients and train all employees on its use
3. Implement DMARC enforcement (p=reject) and enhanced email filtering rules
4. Conduct targeted training for Finance and Marketing departments
5. Schedule quarterly phishing simulations to track improvement
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
日本語の概要は準備中です。原文の説明を表示しています。
Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths
日本語の概要は準備中です。原文の説明を表示しています。
Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.
日本語の概要は準備中です。原文の説明を表示しています。
Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze advanced persistent threat (APT) group techniques using MITRE ATT&CK Navigator to create layered heatmaps of adversary TTPs for detection gap analysis and threat-informed defense.
日本語の概要は準備中です。原文の説明を表示しています。
Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or building cloud SIEM detections.
日本語の概要は準備中です。原文の説明を表示しています。