Skill UI

Scans your codebase with pattern matching and entropy analysis to detect exposed secrets like API keys, passwords, and private keys, then generates remediation reports so you can fix vulnerabilities before committing or deploying code.

This skill analyzes DNS query logs to detect sophisticated threats such as data exfiltration via DNS tunneling, Domain Generation Algorithm (DGA) usage, and covert Command and Control (C2) channels. It utilizes advanced techniques like entropy analysis, query volume anomaly detection, and subdomain length analysis within SIEM platforms, helping SOC teams identify threats that bypass traditional network security controls.

A dedicated tool for forensic analysis of Windows PowerShell Script Block Logs (Event ID 4104) captured in EVTX files. It reconstructs multi-block scripts, applying advanced detection heuristics such as entropy analysis and pattern matching to identify obfuscated commands, encoded payloads (Base64), download cradles, and AMSI bypass attempts, crucial for threat hunting and incident response.

This skill provides a comprehensive framework for detecting data exfiltration attempts that utilize DNS tunneling. It analyzes DNS logs for anomalies such as high Shannon entropy in subdomains, excessive query volume, abnormal query lengths, and misuse of TXT/CNAME records. It is ideal for SOC analysts and security engineers building advanced threat detection rules and performing threat hunting.

This skill provides a methodology to detect data exfiltration attempts hidden within DNS queries. By analyzing Zeek dns.log files, the agent computes Shannon entropy of subdomains, flags unusually long labels, and monitors unique subdomain counts per parent domain. This technique is crucial for identifying malicious tunneling tools like dnscat2 or iodine that bypass traditional security controls.

This skill detects covert data exfiltration occurring over DNS tunnels. It analyzes DNS query names by computing Shannon entropy, examining query length distributions, and identifying high subdomain cardinality. It is essential for security operations, incident response, and proactive threat hunting when suspicious or encoded DNS activity is suspected.

A comprehensive guide for performing static analysis of Windows Portable Executable (PE) files using tools like PEStudio and scripting libraries. This technique examines file headers, calculates section entropy, identifies suspicious API calls (e.g., process injection, persistence), and extracts obfuscated strings and Indicators of Compromise (IoCs) without executing the sample, making it a crucial step in malware triage and reverse engineering.

This system detects ransomware by combining multiple advanced techniques: real-time entropy analysis, file system I/O monitoring, and behavioral heuristics. It identifies abnormal mass file modification patterns, sudden spikes in data entropy (indicating encryption), and suspicious process activities like shadow copy deletion. Ideal for building advanced EDR detection layers to catch zero-day and unknown ransomware variants.

A comprehensive guide detailing the workflow for firmware reverse engineering. It uses tools like binwalk to identify embedded filesystems (e.g., SquashFS, CramFS), perform entropy analysis to locate hidden data, and extract components. The process covers deep inspection, credential discovery, and reporting for IoT and embedded security assessments.

This skill detects malicious Command and Control (C2) communications tunneled through the DNS protocol. It analyzes DNS query logs for indicators such as DNS tunneling tools (Iodine, dnscat2), Domain Generation Algorithms (DGA), and encoded payloads within TXT/CNAME records. Techniques include Shannon entropy analysis of subdomains, statistical anomaly detection, and machine learning classification. Ideal for network security analysts conducting deep threat hunting and forensic investigations.