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Container escape is a critical attack technique where an adversary breaks out of container isolation to access the host system or other containers. Detection involves monitoring for escape indicators

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Detect AWS IAM privilege escalation paths using boto3 and Cloudsplaining policy analysis to identify overly permissive policies, dangerous permission combinations, and least-privilege violations

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Detect unauthorized modifications to running containers by monitoring for binary execution drift, file system changes, and configuration deviations from the original container image.

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Detect LSASS credential dumping, SAM database extraction, and NTDS.dit theft using Sysmon Event ID 10, Windows Security logs, and SIEM correlation rules

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Detecting compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible travel patterns, unauthorized resource provisioning, and credential abuse indicators using GuardDuty, Defender for Identity, and SCC Event Threat Detection.

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Detects command-and-control (C2) communications tunneled through DNS protocol including DNS tunneling tools (Iodine, dnscat2, dns2tcp, Cobalt Strike DNS beacon), domain generation algorithms (DGA), encoded payload delivery via TXT/CNAME records, and DNS beaconing patterns. Covers Shannon entropy analysis of query subdomains, statistical anomaly detection, ML-based DGA classification, passive DNS correlation, and Zeek/Suricata signature development. Activates for requests involving DNS-based C2 detection, DNS tunnel identification, suspicious DNS traffic investigation, or DGA domain classification.

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This skill teaches security teams how to deploy and operationalize Amazon GuardDuty for continuous threat detection across AWS accounts and workloads. It covers enabling protection plans for S3, EKS, EC2 runtime monitoring, and Lambda, interpreting finding severity levels, and building automated response workflows using EventBridge and Lambda.

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Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based filters.

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Detect and test for OWASP API3:2023 Broken Object Property Level Authorization vulnerabilities including excessive data exposure and mass assignment attacks.

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Detect container escape attempts in real-time using Falco runtime security rules that monitor syscalls, file access, and privilege escalation.

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Detects and analyzes Bluetooth Low Energy (BLE) security attacks including sniffing, replay attacks, GATT enumeration abuse, and Man-in-the-Middle interception. Uses Ubertooth One and nRF52840 sniffers for packet capture, the bleak Python library for GATT service enumeration, and crackle for BLE encryption cracking. Use when assessing IoT device BLE security, monitoring for BLE-based attacks on wireless infrastructure, or performing authorized BLE penetration testing. Activates for requests involving BLE security assessment, Ubertooth sniffing, GATT enumeration, or BLE replay detection.

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Performs statistical analysis of Zeek conn.log connection intervals to detect C2 beaconing patterns. Uses the ZAT library to load Zeek logs into Pandas DataFrames, calculates inter-arrival time standard deviation, and flags periodic connections with low jitter. Use when hunting for command-and-control callbacks in network data.

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Detect lateral movement in Azure AD/Entra ID environments using Microsoft Graph API audit logs, Azure Sentinel KQL hunting queries, and sign-in anomaly correlation to identify privilege escalation, token theft, and cross-tenant pivoting.

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Automate AWS GuardDuty threat detection findings processing using EventBridge and Lambda to enable real-time incident response, automatic quarantine of compromised resources, and security notification workflows.

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Detecting exposed AWS credentials in source code repositories, CI/CD pipelines, and configuration files using TruffleHog, git-secrets, and AWS-native detection mechanisms to prevent credential theft and unauthorized account access.

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This skill covers detecting cyber attacks targeting Supervisory Control and Data Acquisition (SCADA) systems including man-in-the-middle attacks on industrial protocols, unauthorized command injection into PLCs, HMI compromise, historian data manipulation, and denial-of-service against control system communications. It leverages OT-specific intrusion detection systems, industrial protocol anomaly detection, and process data analytics to identify attacks that traditional IT security tools miss.

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Business Email Compromise (BEC) is a sophisticated fraud scheme where attackers impersonate executives, vendors, or trusted partners to trick employees into transferring funds, sharing sensitive data,

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Detect cyber attacks targeting OT historian servers (OSIsoft PI, Ignition, Wonderware) that sit at the IT/OT boundary and serve as pivot points for lateral movement between enterprise and control networks, including data manipulation, unauthorized queries, and exploitation of historian-specific vulnerabilities.

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Detect and prevent ARP spoofing attacks using ARPWatch, Dynamic ARP Inspection, Wireshark analysis, and custom monitoring scripts to protect against man-in-the-middle interception.

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Detect and prevent API enumeration attacks including BOLA and IDOR exploitation by monitoring sequential identifier access patterns and authorization failures.

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This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.

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Detect malicious email forwarding rules created by adversaries to maintain persistent access to email communications for intelligence collection and BEC attacks.

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Detects prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack classification.

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Plan, run, and measure an adversary engagement operation using the MITRE Engage framework so that deployed deception is driven by strategy instead of deployed ad hoc. Covers the Engage Matrix (Prepare, Expose, Affect, Elicit, Understand), the 10-Step Operational Process, mapping engagement Activities to the ATT&CK techniques they expose, and defining measurable Goals and Operational Objectives. Use when a team has honeypots, honeytokens, or canary tokens but no coordinating strategy, when leadership asks "should we engage attackers and how", when building a deception/denial program, when writing an adversary engagement operation plan, or when deciding which deception Activities to deploy against a specific threat actor. Keywords: MITRE Engage, adversary engagement, cyber deception strategy, denial and deception, Engage Matrix, EAC, EGO, Expose Affect Elicit, deception program, honeypot strategy, engagement operation.

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