450 件 ・ 関連度順
概要と使いどころ
Use when the user asks about carding, BIN attacks, payment-card breach markets, fullz/CVV2 trade, autoshops (BidenCash, Brian's Club, Russianmarket, B1ack's Stash), or financial-fraud TTPs. Self-updating knowledge cell.
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。
Monitors dark web forums, marketplaces, paste sites, and ransomware leak sites for mentions of organizational assets, leaked credentials, threatened attacks, and threat actor communications to provide early warning intelligence. Use when establishing dark web monitoring coverage, investigating specific data breach claims, or enriching incident investigations with dark web context. Activates for requests involving dark web OSINT, leak site monitoring, credential exposure, Recorded Future dark web, or Tor hidden service intelligence.
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.
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 attack simulations against Active Directory environments to identify misconfigurations, weak credentials, dangerous privilege paths, and exploitable trust relationships that could lead to domain compromise. The tester uses BloodHound for attack path analysis, Mimikatz for credential extraction, and Impacket for protocol-level attacks including Kerberoasting, AS-REP Roasting, and delegation abuse. Activates for requests involving Active Directory pentest, AD attack simulation, domain compromise testing, or Kerberos attack assessment.
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。
Deploy an Evil Twin (Rogue Access Point) to clone a legitimate Wi-Fi network's SSID and MAC address. By combining this with targeted deauthentication attacks, an attacker aggressively forces nearby victims to silently connect to the malicious AP, enabling pervasive Man-in-the-Middle (MitM), captive portal phishing, and credentials interception.
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。
SAML SSO assertion attack playbook. Use when testing signature validation, assertion wrapping, audience restrictions, ACS handling, XML trust boundaries, and enterprise SSO flaws.
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。
TLS/SSL configuration audit. Tests protocol versions (SSLv2/3, TLS 1.0/1.1/1.2/1.3), cipher strength and ordering, certificate chain validation (intermediates, CT logs, OCSP stapling, pinning), known vulnerabilities (POODLE, BEAST, CRIME, Heartbleed, ROBOT, DROWN, Ticketbleed, GOLDENDOODLE), session management (ticket reuse, resumption, fixation), renegotiation attacks, TLS 1.3-specific issues (0-RTT replay, PSK modes, downgrade), HSTS deep analysis (preload, subdomains, bypass), and certificate revocation (CRL, OCSP responder, stapled response freshness). Compliance-mapped to PCI DSS 4.0 (full Section 4), NIST SP 800-52r2, and FedRAMP. Uses testssl.sh, sslscan, sslyze, openssl, nmap NSE scripts, and nuclei SSL templates. Multi-port scanning across 20+ TLS-bearing services.
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。
Test Retrieval-Augmented Generation (RAG) systems for data poisoning, prompt injection via retrieved documents, and data exfiltration through manipulated context windows. Use this skill when assessing RAG-based chatbots, knowledge bases, enterprise AI assistants, or any system that augments LLM responses with external document retrieval. Covers document injection, embedding manipulation, knowledge base poisoning, and cross-document inference attacks.
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。
Race condition and TOCTOU testing for web apps. Use when testing one-time operations, concurrent HTTP abuse, rate-limit bypass, Turbo Intruder gates, HTTP/2 single-packet attacks, and CWE-362-style synchronization gaps.
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。
Extract sensitive training data (PII, API keys, intellectual property, or code) directly from a deployed Large Language Model (LLM). This AI Red Teaming skill focuses on forcing models to regurgitate memorized, unredacted data from their massive internet-scraped datasets through repetition attacks, prefix continuation, and context window manipulation.
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。
Identify Open Redirect vulnerabilities where applications route users to arbitrary external URLs based on unvalidated input. Prove maximum impact by chaining Open Redirects with OAuth token theft, SSRF, or Phishing, graduating this typically low-risk finding into a high-severity report.
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。
Execute advanced Man-in-the-Middle (MITM) NTLM Relay attacks specifically targeting environments where SMB Signing is purportedly enabled but improperly configured (e.g., exclusively required on Domain Controllers but NOT enforced universally on standard Workstations or File Servers). Utilize powerful tools like Responder and Impacket's NTLMRelayX to coercively capture NTLMv2 authentications (via LLMNR, WPAD, or Coercion methods like PetitPotam) and seamlessly relay them across the network identically yielding immediate Local Administrator command execution.
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。
Extract sensitive training data and internal capabilities from AI models through repeated programmatic querying. Use this skill when testing LLMs and ML models for data privacy leaks, intellectual property exposure, or unintended memorization. Covers membership inference, prompt extraction, training data reconstruction, and API boundary testing.
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。
Plan and execute authorized phishing and social engineering campaigns for red team engagements. Use this skill when conducting simulated social engineering attacks including email phishing, spear phishing, vishing, pretexting, and credential harvesting. Covers GoPhish setup, pretext development, payload delivery, and measuring human vulnerability. Requires explicit written authorization before any testing.
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。
Test Model Context Protocol (MCP) servers and tool-calling systems for security vulnerabilities including tool injection, parameter manipulation, privilege escalation, and data exfiltration through AI agent tool interfaces. Use this skill when assessing MCP server implementations, AI agent tool integrations, or any system that exposes tools to language models. Covers tool confusion attacks, cross-tool exploitation, and MCP server hardening assessment.
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。
Exploit insecure handling of the HTTP Host header to poison password resets, generate cache poisoning vectors, or bypass internal routing restrictions. Use this skill when web applications dynamically generate URLs, links, or redirects based on the arbitrary Host header value supplied by the client rather than relying on a static, trusted server configuration.
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。
HTTP request smuggling exploits disagreements between a front-end proxy and back-end server on where one HTTP request ends and the next begins, using conflicting `Content-Length` and `Transfer-Encoding: chunked` headers (CL.TE, TE.CL, TE.TE variants). Enables bypassing access controls, cache poisoning, session hijacking, and capturing other users' requests. Detect via timing attacks, differential responses, and tools like Burp's HTTP Request Smuggler extension.
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。
Lattice-based cryptanalysis playbook. Use when attacking RSA via Coppersmith small roots, recovering DSA/ECDSA nonces from bias, solving knapsack problems, or applying LLL/BKZ reduction to cryptographic constructions.
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。
Exploit implementations of JSON Web Tokens (JWT) through algorithmic confusion (e.g., RS256 to HS256), "none" algorithm attacks, and signature stripping. Use this skill to forge administration tokens and achieve unauthenticated Account Takeover (ATO) on REST APIs and modern web applications.
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 and exploit Insecure Direct Object Reference (IDOR) vulnerabilities in web applications and APIs. Use this skill when testing for unauthorized access to resources by manipulating object identifiers like user IDs, order numbers, file references, or API endpoints. Covers parameter tampering, UUID prediction, hash manipulation, and chained IDOR attacks for maximum impact in bug bounty programs.
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。
Execute Indirect Prompt Injection attacks against Large Language Models (LLMs) by subtly embedding malicious instructions within external data sources (e.g., websites, documents, databases) that the LLM autonomously ingests. This forces the model to execute attacker-controlled commands under the guise of processing legitimate user requests.
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。
JWT and OAuth token attack playbook. Use when validating token trust, signing algorithms, key handling, claim abuse, bearer flows, and OAuth account-binding weaknesses.
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。
Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques
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。
Test Large Language Models for direct prompt injection vulnerabilities where user input overrides system instructions, extracts system prompts, bypasses safety filters, or causes unauthorized actions. Use this skill when assessing chatbots, AI assistants, LLM-powered tools, or any application that processes natural language input through an LLM. Covers role-playing attacks, instruction hierarchy exploitation, multi-turn manipulation, and context window abuse for comprehensive AI security testing.
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。