'deception' 검색 결과

Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses against observational data, or conducting systematic hypothesis exploration for research discovery in domains like deception detection, AI content detection, mental health analysis, or other empirical research tasks.

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Use when reviewing what was learned after debugging, workaround discovery, or trial-and-error investigation, and capture it as a dated journal entry under ~/.codex/journal

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Use when reviewing what was learned after debugging, workaround discovery, or trial-and-error investigation, and capture it as a dated journal entry under ~/.claude/journal

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Use when the diff reads Host or X-Forwarded-* headers, configures trusted proxies, builds absolute URLs or response headers from input, sets cache headers or CDN/reverse-proxy cache rules, or changes an ingress, auth proxy or custom HTTP parser - host and forwarded-header trust, cache poisoning and deception, header injection, trusted-proxy bypass and request framing

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Hunt web cache poisoning, CDN cache poisoning, and cache deception attacks. Use when target uses a CDN (Cloudflare, Akamai, Fastly, AWS CloudFront) or has Varnish/nginx caches and you want to identify cache key flaws that let attackers serve malicious content to other users.

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Use when evidence needs grading before anyone reasons from it — the user asks "how reliable is this?", "grade this source", "check this report", "can I trust this article?", "is this corroborated?", or hands over a URL, a pasted report or a Liberty91 event ahead of an ACH or assessment. The Quality of Information Check from the CIA Tradecraft Primer, adapted for vendor and press CTI reporting. Resolves each document to its originating source, splits it into claims, and grades every claim on two written elements, access level (how the source knows) and claim support (what backs the statement), with a stated rationale, using a fixed rubric with hard caps. The grade is not an Admiralty rating. Five outlets citing one vendor report count as one source; anything that cannot be traced to a primary is capped, not guessed. Returns a graded claim table, gaps, a deception screen and JSON. /ach, /threat-assessment and /writing-assessments run it first by default.

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Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

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Plant canarytokens and honey credentials and alert on breach.

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Deploys and monitors ransomware canary files across critical directories using Python's watchdog library for real-time filesystem event detection. Places strategically named decoy files that mimic high-value targets (financial records, credentials, database exports) in locations ransomware typically enumerates first. Monitors for any read, modify, rename, or delete operations on canary files and triggers immediate alerts via email, Slack webhook, or syslog when interaction is detected, providing early warning before full encryption begins.

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Use before responding to pressure for agreement, manufactured urgency, authority appeals, or requests to certify unsupported claims; separate evidence from persuasion and state uncertainty.

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在授权渗透测试中挖掘 Web 缓存投毒与缓存欺骗（web cache poisoning / cache deception）。当目标前有缓存层（CDN/反代/Varnish）且缓存键未覆盖某些影响响应的输入（unkeyed header/参数/解析差异）时使用——典型场景：X-Forwarded-Host 反射进响应被缓存、unkeyed 参数、缓存键规范化差异、静态扩展名欺骗缓存私有页。适用目标类型 Web / CDN。触发场景包括用户说"测下缓存投毒""X-Forwarded-Host 能不能毒缓存""缓存欺骗看一下""unkeyed 输入有没有"。输出：投毒/欺骗可行性 + 被缓存证据的 finding（含 killed 记录）。

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Dağıt: and manage network honeypots using OpenCanary, T-Pot, or Cowrie to tespit etme
unauthorized access, lateral movement, and attacker reconnaissance.

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Deploys and monitors ransomware canary files across critical directories using Python's watchdog library for real-time filesystem event detection. Places strategically named decoy files that mimic high-value targets (financial records, credentials, database exports) in locations ransomware typically enumerates first. Monitors for any read, modify, rename, or delete operations on canary files and triggers immediate alerts via email, Slack webhook, or syslog when interaction is detected, providing early warning before full encryption begins.

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Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.

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Deploys and monitors ransomware canary files across critical directories using Python's watchdog library for real-time filesystem event detection. Places strategically named decoy files that mimic high-value targets (financial records, credentials, database exports) in locations ransomware typically enumerates first. Monitors for any read, modify, rename, or delete operations on canary files and triggers immediate alerts via email, Slack webhook, or syslog when interaction is detected, providing early warning before full encryption begins.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.