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概要と使いどころ
Produce the monthly "Stablecoin Pulse" 2-page PDF for a partner team (default Polaris) from Pharos API data plus X and web research, explaining why supply, chains, and pegs moved. Use at month end or when asked for a monthly stablecoin trends/pulse report; not for the daily digest or Telegram pulse.
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Draft a short, prioritized questionnaire for a collaborating stablecoin team when public evidence cannot resolve important Pharos data or confidence 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。
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.
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SQL injection occurs when untrusted user input is interpolated directly into database queries, allowing attackers to alter query logic. Detect via single-quote errors, boolean-based blind responses (AND 1=1 vs AND 1=2), time-delay payloads (SLEEP, WAITFOR), UNION column enumeration, and error messages from MySQL, Oracle, MSSQL, PostgreSQL. Tools: sqlmap, sqlbftools, Burp Suite, wfuzz with SQLi fuzz strings.
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Extract the hidden foundational System Prompts, internal instructions, backend APIs, and confidential contextual data powering Large Language Model (LLM) applications using targeted prompt injection manipulation and adversarial psychological engineering.
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。
SQL injection playbook. Use when input reaches SQL queries, authentication logic, sorting, filtering, reporting, or DB-specific blind and out-of-band execution paths.
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。
NoSQL injection playbook. Use when MongoDB-style operators, JSON query objects, flexible search filters, or backend query DSLs may allow data or logic abuse.
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。
NTLM relay and authentication coercion playbook. Use when capturing and relaying NTLM authentication to escalate privileges via SMB, LDAP, HTTP, or MSSQL relay targets, combined with PetitPotam, PrinterBug, and other coercion methods.
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 and exploit vulnerabilities in the AI Supply Chain by injecting malicious models, datasets, or dependencies. Use this skill to simulate advanced persistent threats (APTs) compromising Hugging Face repositories, manipulating pre-trained weights (Model Poisoning), and exploiting insecure deserialization during model loading (e.g., Pickle files).
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Exploit an application's absolute trust in its underlying LLM (Overreliance). Use this skill to induce critical "hallucinations" (confident falsehoods) that cause downstream logical systems or automated agents tracking the LLM's output to make destructive actions or grant unauthorized access.
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Detects insecure platform interaction in mobile apps (Android/iOS). Trigger on: exported Activity, exported Service, exported BroadcastReceiver, Content Provider, Intent injection, deep link hijacking, WebView JavaScript enabled, JavascriptInterface, addJavascriptInterface, setJavaScriptEnabled, intent:// scheme, file:// scheme, WKWebView, WKScriptMessageHandler, UIPasteboard, URL scheme hijacking, Universal Links, PendingIntent, FLAG_IMMUTABLE, overlay attack, tapjacking, screenshot prevention, FLAG_SECURE, Broadcast sniffing, IPC data exposure. Covers MASVS-PLATFORM-1/2/3.
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。
LLM prompt injection playbook. Use when testing AI/LLM applications for direct injection, indirect injection via RAG/browsing, tool abuse, data exfiltration, MCP security risks, and defense bypass techniques.
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Exploit AI applications using Indirect Prompt Injection. This skill focuses on hiding malicious instructions within data sources (web pages, documents, emails) that the LLM processes, causing the AI to execute unintended actions or leak data without direct user interaction.
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 for indirect prompt injection vulnerabilities where malicious instructions are injected through external data sources (websites, emails, documents, database records) that the LLM processes. Use this skill when assessing LLM-integrated applications that process user-generated content, retrieve web pages, parse emails, or read documents. Covers injection via web content, email bodies, user profiles, and database records that are fed to LLM context.
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。
Systematically bypass LLM safety filters and content moderation systems using advanced jailbreaking techniques. Use this skill when testing AI systems for safety alignment robustness, evaluating content moderation effectiveness, or conducting authorized AI red team assessments. Covers role-play attacks, few-shot manipulation, encoding bypass, multi-modal exploitation, and automated jailbreak discovery methods.
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。
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。
Execute dynamic instrumentation utilizing Frida to inject custom JavaScript into running iOS applications (IPAs) on jailbroken devices. Hook native functions, bypass SSL Pinning, bypass Jailbreak Detection, and manipulate in-memory data at runtime.
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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.
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Execute advanced LLM Jailbreaking techniques using roleplay, nested environments (virtual machines), and complex personas to completely bypass safety constraints and ethical alignments embedded in AI models.
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Java "Ghost Bits" / Cast Attack playbook (Black Hat Asia 2026). Use when attacking Java services where 16-bit char is silently narrowed to 8-bit byte to bypass WAF/IDS for SQL injection, deserialization RCE, file upload (Webshell), path traversal, CRLF injection, request smuggling, and SMTP injection. Affects Tomcat, Spring, Jetty, Undertow, Vert.x, Jackson, Fastjson, Apache Commons BCEL, Apache HttpClient, Angus Mail, JDK HttpServer, Lettuce, Jodd, XMLWriter and re-enables many "patched" CVEs through WAF bypass.
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Identify and exploit SQL Injection vulnerabilities in Django applications, specifically focusing on edge cases involving raw querysets (`RawSQL`), improper use of `.extra()`, and poorly sanitized filters where Django's typical ORM protections are bypassed.
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Simulate supply chain and adversarial machine learning attacks by injecting poisoned data or targeted backdoors into training and fine-tuning datasets. Use this skill when assessing the integrity controls of MLOps pipelines or evaluating the resilience of AI models against highly targeted, stealthy manipulation intended to alter model behavior on specific triggers.
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。