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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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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。

Use when testing APIs and web frameworks for mass assignment vulnerabilities where user-controlled request body fields are bound directly to model attributes without a field allowlist. Trigger on: ORM update/create endpoints, REST APIs accepting JSON body, Rails strong parameters, Django model forms, Laravel fillable/guarded, Node.js Mongoose/Sequelize, PUT/PATCH requests, registration endpoints, profile update endpoints, GraphQL mutations. Detects privilege escalation via role/admin/isAdmin fields, plan upgrades via subscription fields, and horizontal access via ownerId/userId injection.

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Detects code quality vulnerabilities in mobile apps (Android/iOS). Trigger on: SQL injection in SQLite, JavaScript injection in WebViews, intent injection, unsafe deserialization, NSKeyedUnarchiver, NSCoding, Java serialization, Parcelable, buffer overflow, JNI native code, PIE disabled, NX disabled, stack canary absent, RELRO, ARC disabled, third-party library CVE, vulnerable dependency, outdated SDK, targetSdkVersion, update enforcement missing, implicit Intent, URL loading in WebView, object persistence, memory corruption, OWASP dependency check. Covers MASVS-CODE-1/2/3/4.

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Detects authentication and biometric bypass vulnerabilities in mobile apps (Android/iOS). Trigger on: BiometricPrompt, LocalAuthentication, LAContext, evaluatePolicy, CryptoObject, Android Keystore, Secure Enclave, kSecAccessControlBiometryCurrentSet, userAuthenticationValidityDurationSeconds, confirmCredentials, biometric fallback, PIN bypass, passive authentication, enrolled biometrics detection, Frida hook auth, jailbreak bypass, TouchID, FaceID, fingerprint. Covers MASVS-AUTH-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。

Identify and exploit logical flaws in OAuth 2.0 and OpenID Connect workflows. Use this skill when testing "Sign in with Google/Facebook/Apple" features, focusing on Authorization Code interception, Implicit flow token leakage, standard CSRF bypassing via missing `state` parameters, and redirect logic 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。

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。

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.

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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。

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。

Linux privilege escalation playbook. Use when you have low-privilege shell access and need to escalate to root via SUID/SGID binaries, capabilities, cron abuse, kernel exploits, misconfigurations, or credential harvesting on Linux systems.

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。

Linux kernel exploitation playbook. Use when exploiting kernel vulnerabilities (UAF, OOB, race condition, type confusion) for privilege escalation via commit_creds, modprobe_path overwrite, or kernel ROP chains in CTF and real-world scenarios.

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 JSON Web Tokens (JWT) by implementing the 'None' algorithm attack. This skill details how to bypass authentication mechanisms when a server improperly accepts JWTs with the `alg` header set to `none`, allowing attackers to forge tokens without a valid signature.

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 Algorithm Confusion vulnerabilities in JSON Web Tokens (JWT). This skill details how to bypass signature verification by changing the signing algorithm from asymmetric (RS256) to symmetric (HS256) and using the public key as the symmetric secret.

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 Java Insecure Deserialization vulnerabilities leading to Remote Code Execution (RCE). Identify serialized Java objects natively passed within HTTP parameters, cookies, or sockets (e.g., `rO0AB...`). Utilize `ysoserial` to meticulously craft malignant payload chains exploiting vulnerable gadget libraries like CommonsCollections inherently present in the application's classpath.

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 Insecure Direct Object Reference (IDOR), or Broken Object Level Authorization (BOLA), vulnerabilities. Manipulate internal identifiers (e.g., user IDs, database primary keys, transaction IDs) within HTTP request parameters or API payloads to unauthorizedly access, modify, or delete data belonging to other users.

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。

Exploit advanced HTTP Request Smuggling combining Transfer-Encoding vulnerabilities (TE.TE). By obscuring the Transfer-Encoding header, an attacker forces desynchronization between a frontend proxy (which processes the request one way) and the backend server (which processes it another way), allowing the smuggling of malicious requests to bypass security controls or poison caches.

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 HTTP Request Smuggling (HTTP Desync) vulnerabilities caused by discrepancies in how front-end proxies (load balancers, CDNs) and back-end servers parse the Content-Length and Transfer-Encoding headers. Use this to bypass security controls, hijack user sessions, and poison web caches.

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。

Heap exploitation playbook. Use when targeting ptmalloc2/glibc heap vulnerabilities including UAF, double free, overflow, off-by-one/null, and leveraging tcache/fastbin/unsortedbin attacks for arbitrary write or code 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。

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。

Identify and exploit Prototype Pollution vulnerabilities in JavaScript applications to achieve client-side Cross-Site Scripting (XSS), bypass authentication, or execute Remote Code Execution (RCE) on Node.js servers by manipulating the core Object prototype.

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。

Use when hunting GitHub Actions cache poisoning vulnerabilities where an attacker can inject malicious content into the CI/CD cache and have it restored by a privileged downstream workflow. Trigger on: "cache poisoning", "actions/cache", "actions/setup-node", "node_modules cache", "GitHub Actions cache", "pnpm cache", "LRU eviction", "10GB limit", "Cacheract", "poisoned cache", "workflow cache attack", supply chain via CI cache, "ng-renovate", "cache stuffing", scheduled workflow cache restore, shared cache key, "hashFiles package.json", cross-workflow cache, PR workflow release workflow same key, "npm install prefer-offline", Cacheract, Gato-X, supply chain npm token.

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 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.

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。