「leakage」の検索結果

Foundations of digital signal processing — sampling, aliasing, the Nyquist criterion, quantization, convolution, the discrete Fourier transform, FFT, FIR and IIR filter design, windowing, spectral leakage, and practical issues in fixed-point versus floating-point implementation. Use when designing filters, analyzing spectra, choosing sample rates, reasoning about aliasing, or implementing DSP algorithms on a microcontroller, DSP chip, or host CPU.

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Use when discovering niche signals, auditing ICP or won/lost evidence, rescoring accounts, or building account and lead scoring Plays. Triggers on fit scoring, engagement scoring, external proxies, and scoring leakage. Skip pure outreach copy or contributor skill installation tasks.

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Review user-facing docs, CLI reference text, examples, and scaffolds for truthfulness, prose quality, and RFC leakage. Use when a broad review needs a dedicated docs/claims pass or when the user asks for docs-truth review specifically.

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Use when writing or modifying code for s&box, sbox, the Facepunch sandbox engine, or any Source 2 game project in C#. Triggers on mentions of s&box, sbox, sandbox game, Facepunch engine, Source 2 game, `.sbproj`, `.razor` with `PanelComponent`, `@inherits PanelComponent`, `Sandbox.Component`, `GameObject` + `Components.Get<T>`, `Scene.Trace`, `CharacterController` + `.Move()`, `SkinnedModelRenderer`, `NavMeshAgent`, `[Sync]`, `[Rpc.Broadcast]`, `[Property]`, `INetworkListener`, `ISceneEvent`, `PlayerController` in C#. Also triggers on any file with `using Sandbox;` or `using Sandbox.UI;` that isn't Unity/Godot/Unreal. Writes idiomatic s&box C# components, Razor UI, and networking code; prevents Unity-pattern leakage.

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Evaluates alternative trading systems with fill rate analysis, information leakage assessment, and venue toxicity measurement. Use when analyzing dark pools, evaluating ATS venues, or assessing execution venue quality.

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Hunt cloud misconfigurations exposed externally — open S3/GCS buckets, exposed IAM-tied EC2 metadata, Cognito misconfig, GitHub Actions secret leakage, exposed Kubernetes API, public Elastic / Kibana / Mongo, Firebase RTDB rules, weak AWS access keys discovered in JS/mobile. Use when leveraging leaked credentials or hunting cloud surface in scope.

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Hunt GraphQL-specific vulns — introspection abuse, field-level authorization (IDOR), batched queries for brute force, alias-based rate limit bypass, deep query DoS, mutation injection, schema disclosure, suggestion leakage, CSRF via GET, and CSRF via POST without preflight. Use when a GraphQL endpoint is present.

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REQUIRED before writing code for any feature touching auth, payments, credentials, tokens, sessions, file upload, user data, untrusted input, deserialization, network endpoints, or data deletion. Scope by that surface, not the task label — a research/experimental/local-only feature with none of it doesn't need this. Invoke it, record answers inline in the design doc or PR description, then write the code. Skipping this is how auth bypass, token leakage, and delete-the-wrong-record bugs ship. If the feature touches any of those surfaces and this hasn't run, the design is incomplete — do not proceed to implementation.

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Intercepts and analyzes HTTP/HTTPS traffic from mobile applications using Burp Suite proxy to identify insecure API communications, authentication flaws, data leakage, and server-side vulnerabilities. Use when performing mobile application penetration testing, assessing API security, or evaluating client-server communication patterns. Activates for requests involving mobile traffic interception, Burp Suite mobile proxy, API security testing, or mobile HTTPS analysis.

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Comprehensive LLM security testing prompts for bias detection, data leakage, alignment testing, and adversarial prompt resistance.

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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.

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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.

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Deep OAuth 2.0 / OpenID Connect security assessment. Covers the full attack surface: redirect_uri validation bypass (path traversal, open redirect chains, subdomain confusion, URL parsing tricks, parameter pollution, response mode switching), missing/broken state parameter CSRF, PKCE downgrade and absent-challenge attacks, implicit grant token leakage (Referer, browser history, XSS fragment theft), authorization code injection, scope escalation, client confusion attacks, mutable-claims account takeover (iss+sub confusion, email-keyed identity merging), pre-account takeover via unverified registration, OpenID Connect dynamic client registration SSRF (logo_uri, jwks_uri, sector_identifier_uri, request_uri), nonce replay, ID token validation failures, Device Code phishing flow, mobile custom URI scheme hijacking, refresh token persistence, token introspection enumeration, consent screen clickjacking, host header injection, and cross-IdP mix-up attacks. Uses KOAuth, jwt_tool, nuclei oauth templates, and manual http(action="request", ...) payloads. Every technique includes actual request payloads and verification logic. Chains from /pentester or /api-security when OAuth endpoints are discovered. Chains into /web-exploit for open redirect, XSS, and SSRF chaining. Chains into /post-exploit when full account takeover is achieved. Chains into /credential-audit when access tokens or refresh tokens are recovered.

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Execute sophisticated Data Extraction and Privacy Leakage attacks explicitly against Large Language Models (LLMs) to natively force the neural network entirely into organically regurgitating exact, verbatim strings of Highly Confidential Personally Identifiable Information (PII), proprietary source code, or copyrighted material categorically memorized intrinsically during its foundational pre-training phase.

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Advanced Content Security Policy bypass techniques. Use when XSS or data exfiltration is blocked by CSP and you need to find policy weaknesses, trusted endpoint abuse, nonce leakage, or exfiltration channels that CSP cannot block.

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Audit and attack session cookies via missing Secure/HttpOnly/SameSite attributes, overly broad Domain/Path scope, non-expiring persistent cookies, absent __Host- and __Secure- prefixes, browser cache leakage (Cache-Control: no-store missing), session token predictability via Burp Sequencer analysis, server-side session not invalidated on logout, and SSO single-logout bypass. Tools: Burp Suite Repeater/Sequencer, OWASP ZAP, EditThisCookie, Tamper Data, Cookiebro.

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Test APIs for authentication and authorization bypass vulnerabilities including JWT manipulation, OAuth2 flaws, API key leakage, broken authentication, and token forgery. Use this skill when assessing REST/GraphQL APIs for access control weaknesses, session management flaws, or credential handling issues. Covers JWT algorithm confusion, OAuth redirect manipulation, and rate limit bypass.

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AI/LLM red-team assessment using the OWASP LLM Top 10 (2025) + OWASP AI Testing Guide (AITG v1, Nov 2025) frameworks, plus OWASP MCP Top 10 runtime testing for agentic/MCP targets. Tests prompt injection, jailbreaks, system prompt leakage, sensitive data extraction, excessive agency, improper output handling, model extraction, content bias, evasion, membership inference, MCP token exposure, MCP command injection, and more. Uses four tools in combination: FuzzyAI (single-turn jailbreak fuzzing), PyRIT (multi-turn orchestrated attacks), Garak (probe-based vulnerability scanning), and promptfoo (plugin-based red-team evaluation). Each tool covers different OWASP categories; running them together gives systematic coverage. Includes a conditional MCP reconnaissance phase and a post-access AI infrastructure phase (chained from /post-exploit). Produces: OWASP LLM Top 10 + AITG + MCP coverage matrix, findings per category, architecture diagram of the AI system, PoCs for confirmed exploits. Chains into /gh-export for issue filing.

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Backend code review patterns. Use when reviewing API routes, database operations, auth middleware, and server utilities. Covers injection, boundary validation, authorization coverage, secret/PII exposure, error leakage, and query patterns.

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Run this skill for Phase 3 of the evidence verification pipeline — the adversarial audit step. Use when you need to check the evidence data lake for bad data before ingestion: dead links, wrong URL formats (tree/ vs blob/), subjective wording ("elite", "high-quality"), stale migration notes, benchmark catalog misuse/vendor-claim leakage, or skills whose star evidence conflicts with classified evidence level. Triggers on phrases like: "audit the data lake", "adversarial check", "ev-adversarial-audit", "check for noise in evidence", "flag bad evidence", "run the audit phase", "quality check the by-type files", or any reference to Phase 3 of the pipeline.

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Use when a task needs the judgment of an Energy Auditor — running a blower-door/duct-leakage diagnostic sequence on a home or light-commercial building, testing combustion appliances for worst-case backdraft risk before recommending air sealing, reconciling utility billing history against walkthrough findings, ranking retrofit measures by savings-to-investment ratio (SIR) for a weatherization or utility rebate program, or writing the prioritized scope of work an install crew executes without the auditor on site. Distinct from an energy-engineer (industrial/commercial capital planning, IPMVP measurement and verification, calibrated simulation) — this role owns the field diagnostics and the residential/light-commercial audit-to-scope handoff.

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Use when a task needs the judgment of a Control and Valve Installer/Repairer — sizing a control valve's Cv against actual process flow, diagnosing a limit-cycling control loop as valve stiction versus a bad PID tune, running an FCI 70-2 seat-leakage or API 598 pressure test, calibrating a positioner/actuator after a rebuild, or scheduling PHMSA-mandated regulator/relief-valve testing on a gas distribution system.

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Build QA quality dashboards, calculate defect metrics, analyze escape rates, and produce quality trend reports. Use this skill whenever a QA lead or manager needs to measure and communicate quality outcomes. Trigger on phrases like "quality metrics", "defect density", "escape rate", "bug trends", "quality dashboard", "sprint quality report", "test coverage metrics", "QA KPIs", "how is quality trending", "defect leakage", "production bugs this quarter", "mean time to detect", "quality scorecard", or any request to quantify, visualize, or report on the quality of a product or testing process. Also trigger when a manager asks "how is QA performing?" or an engineering lead asks "what does our defect data tell us?".

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Redact PII in your own code before it reaches Agentforce prompts, models, and logs. Trigger keywords: agentforce pii, pii redaction, data masking llm, prompt pii filter, audit pii leakage. NOT for turning on the platform's own masking, zero-retention and audit-trail controls — use agentforce/einstein-trust-layer. NOT for masking PII in a refreshed sandbox — use security/sandbox-data-masking.

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。