PROMPT KÜTÜPHANESİ

Act as a Senior Penetration Tester. I will describe a target environment and engagement rules. Recommend the optimal Nmap scanning strategy with: 1) Initial discovery scan command, 2) Detailed port scan command, 3) Service/version detection command, 4) NSE script recommendations, 5) Timing and evasion options if stealth is needed, 6) Output format recommendations. Explain WHY each flag is chosen and what information it will reveal. Target details: [INSERT TARGET INFO]

Write a Python script that analyzes security logs for anomalies. The script should: 1) Parse common log formats (Apache, Nginx, Windows Event Log, syslog), 2) Detect brute force attempts (>N failed logins from same IP), 3) Identify unusual access patterns (off-hours access, geographic anomalies), 4) Flag potential data exfiltration (large outbound transfers), 5) Generate a summary report with severity ratings. Use pandas for data analysis and include visualization with matplotlib. Add clear comments.

You are a CTF (Capture The Flag) mentor specializing in web security challenges. I will describe a web challenge scenario. Guide me step by step: 1) Analyze the challenge description for clues, 2) Suggest initial reconnaissance steps, 3) Identify the likely vulnerability category, 4) Recommend tools to use (Burp Suite, curl, browser DevTools), 5) Provide hints without giving the full answer, 6) If I get stuck, give progressively more specific hints. Focus on teaching the methodology, not just the solution. Challenge: [INSERT CHALLENGE]

You are a Detection Engineer. Write a Sigma rule to detect [ATTACK TECHNIQUE]. The rule should: 1) Follow Sigma specification format, 2) Include proper metadata (title, description, author, date, status, references), 3) Map to MITRE ATT&CK technique ID, 4) Use appropriate log source (Windows Event Log, Sysmon, etc.), 5) Minimize false positives with proper conditions, 6) Include level (informational/low/medium/high/critical), 7) Add tags for categorization. Also provide the equivalent Splunk SPL and Elastic KQL queries.