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Hayabusa is a Sigma-based tool for fast digital forensics and incident response. It analyzes multiple Windows EVTX event logs, applying a comprehensive library of Sigma rules to detect suspicious activity. The output is a single, chronologically ordered timeline, complete with severity levels, MITRE ATT&CK mapping, and evidence summaries, streamlining the triage process for security analysts.

This skill is designed for proactive firmware forensics and advanced threat hunting. It analyzes the EFI System Partition (ESP) to detect sophisticated, pre-OS persistence mechanisms, such as UEFI bootkits (e.g., BlackLotus, ESPecter). Detection methods include verifying file hashes against a trusted golden baseline, checking Secure Boot signatures, scanning with YARA, and flagging structural anomalies within the ESP.

This skill enables comprehensive security graph mapping by ingesting both on-premises Active Directory data (via SharpHound) and cloud Entra ID data (via AzureHound) into BloodHound CE. It analyzes complex, hybrid attack paths, allowing security teams to identify critical privilege escalation chains from low-privileged accounts to Tier-0 assets, mapping findings to MITRE ATT&CK.

This skill guides the secure migration from classical cryptography (RSA, ECC) to quantum-resistant algorithms (PQC). It covers building a Cryptographic Bill of Materials (CBOM), identifying data at risk (HNDL), and deploying hybrid key exchange protocols like X25519MLKEM768 to ensure long-term data security against quantum threats.

This skill guides the deployment and operation of the Havoc C2 framework, enabling advanced adversary emulation. It utilizes Yaotl profiles to generate highly evasive agents (Demon) that employ sophisticated techniques like indirect syscalls and sleep obfuscation. The scope covers the full red-teaming lifecycle, including C2 setup, payload generation, post-exploitation, and lateral movement, making it ideal for authorized security testing and validating EDR detection capabilities.

This skill guides the full lifecycle of threat intelligence processing, transforming raw indicators of compromise (IOCs) from MISP feeds into actionable security detections. It covers feed curation, false positive reduction using warninglists, and automating the generation of high-fidelity rules for Suricata, Sigma, and Wazuh. Use this to mature your MISP instance from a repository into a proactive detection engine.

This skill utilizes NVIDIA's open-source garak framework to conduct comprehensive red-teaming assessments on Large Language Models (LLMs). It tests for critical vulnerabilities such as prompt injection, jailbreaks, data leakage, and toxic content generation by sending thousands of adversarial probes. Ideal for pre-deployment security validation, API guardrail testing, and generating defensible evidence for AI risk assessments.

Trivy is an open-source, comprehensive scanner used to find vulnerabilities (CVEs), misconfigurations, secrets, and license issues across multiple targets. It is essential for implementing 'shift-left' security gates in CI/CD pipelines, scanning container images, Infrastructure-as-Code (IaC) like Terraform/Kubernetes, and generating/re-scanning SBOMs for supply chain transparency.

This skill provides a comprehensive defense-in-depth framework for securing autonomous AI agents. It implements least-privilege tool allowlisting, identity binding, and human-in-the-loop controls to mitigate risks like prompt injection and tool poisoning. Use this when building agents that interact with sensitive systems (e.g., payments, file writes) to ensure governance, auditability, and compliance.

This guide provides a structured approach to red-teaming Large Language Models (LLMs) to detect sensitive data leakage from system prompts. It simulates advanced attacks—including prompt injection, instruction override, and encoding tricks—to identify embedded secrets, API keys, and proprietary business logic. Essential for validating adherence to OWASP LLM07 and securing AI applications against data exfiltration.

A comprehensive red-teaming skill for assessing the security posture of Retrieval-Augmented Generation (RAG) pipelines. It systematically probes two critical injection surfaces: poisoned retrieved context (indirect prompt injection) and embedding manipulation. Use this skill to validate retrieval guardrails, ensure data leak prevention, and demonstrate vulnerabilities in LLM-powered knowledge assistants.

This skill guides the verification of software build provenance and signatures using industry standards like SLSA and Sigstore. It leverages tools like cosign and slsa-verifier to enforce keyless OIDC identity and confirm that artifacts were built by trusted sources without tampering. It is essential for hardening CI/CD pipelines and securing the entire software supply chain against advanced attacks.