127 件 ・ 関連度順
概要と使いどころ
Analyzes DNS query logs to detect data exfiltration via DNS tunneling, DGA domain communication, and covert C2 channels using entropy analysis, query volume anomalies, and subdomain length detection in SIEM platforms. Use when SOC teams need to identify DNS-based threats that bypass traditional network security controls.
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。
Tespit etme data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty Bul:ings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.
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。
Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs. Uses pandas for behavioral analytics and statistical baselines. Use when investigating insider threats or building user behavior analytics for data loss prevention.
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 DNS-based data exfiltration by analyzing Zeek dns.log for high-entropy subdomains and anomalous query patterns
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。
Detecting data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty findings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.
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。
Analyzes DNS query logs to detect data exfiltration via DNS tunneling, DGA domain communication, and covert C2 channels using entropy analysis, query volume anomalies, and subdomain length detection in SIEM platforms. Use when SOC teams need to identify DNS-based threats that bypass traditional network security controls.
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 DNS-based data exfiltration by analyzing Zeek dns.log for high-entropy subdomains and anomalous query patterns
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。
Detecting data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty findings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.
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。
Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs. Uses pandas for behavioral analytics and statistical baselines. Use when investigating insider threats or building user behavior analytics for data loss prevention.
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。
Analyzes DNS query logs to detect data exfiltration via DNS tunneling, DGA domain communication, and covert C2 channels using entropy analysis, query volume anomalies, and subdomain length detection in SIEM platforms. Use when SOC teams need to identify DNS-based threats that bypass traditional network security controls.
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。
Triage npm packages and lockfiles for install-script malware, credential exfiltration, and worming behavior using GuardDog, manual tarball inspection, and dynamic detonation with network/filesystem monitoring. Use when vetting a new dependency, reviewing a package.json/package-lock.json during code review, checking lockfiles against a supply-chain advisory's known-bad versions, or investigating a host suspected of installing a trojanized package.
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。
Correlate Windows registry keys (USBSTOR, MountedDevices), Event Logs, and setupapi.dev.log to reconstruct USB device connection history, first/last-plugged timestamps, and drive letter mappings. Use when investigating removable media usage, tracking device provenance, or building a timeline for suspected data exfiltration.
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 abnormal access in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics for after-hours bulk downloads, new-IP access, and API-call spikes (e.g. GetObject) via statistical baselines and time-series anomaly detection. Use when investigating suspected cloud data exfiltration or building related detection rules.
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。
Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic analysis, packet capture investigation, PCAP analysis, network forensics, C2 traffic detection, or exfiltration detection.
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。
Hunt client-side DOM vulnerabilities — DOM Clobbering (overwrite JS globals via HTML injection), PostMessage hijacking (missing origin check), Service Worker abuse (intercept requests from same-origin script), CSS Injection/Exfiltration (attribute selectors → token char-by-char via OOB), client-side template injection, dangerouslySetInnerHTML. Grounded in named public research: Gareth Heyes / PortSwigger DOM-clobbering + DOM-Invader, Michał Bentkowski DOMPurify clobbering bypasses, jQuery htmlPrefilter XSS (CVE-2020-11022 / CVE-2020-11023), d0nut CSS-exfil research. Use when hunting DOM-XSS, client-side auth bypass, or token exfiltration without server-side 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。
Hunt LLM/AI feature bugs — prompt injection, indirect injection, exfiltration via tool-use/markdown, ASCII smuggling, agentic AI security (OWASP Agentic Apps 2026, ASI01-ASI10). Patterns: direct injection ('ignore previous instructions'), indirect injection via documents/web pages/email the model reads, ASCII smuggling (Unicode Tags block U+E0000-U+E007F, invisible to humans, decoded by the model), tool-use exfiltration (model has fetch/browse tool, attacker injects OOB URL, model exfils chat history/secrets), markdown-image zero-click exfil, system-prompt extraction, IDOR-via-AI (cross-tenant data). Targets: chatbots, RAG, summarizers, agentic copilots, MCP tools. Detection: any LLM-backed endpoint, doc upload triggering AI processing, autonomous agent with tools. Validate: OOB/Collaborator callback for exfil, verbatim-reproducible system-prompt leak (run twice), verifiable cross-tenant leak or RCE. Confabulation is NOT a finding. Use when hunting AI features, chatbots, RAG, agentic systems, MCP.
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。
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.
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。
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.
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。
Triage npm packages for install-script malware, exfiltration, and worming behavior.
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。
Investigate USB device connection history from Windows registry, event logs, and setupapi logs to track removable media usage and potential data exfiltration.
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。
Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic analysis, packet capture investigation, PCAP analysis, network forensics, C2 traffic detection, or exfiltration detection.
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 ransomware network indicators including C2 beaconing patterns, TOR exit node connections, data exfiltration flows, and encryption key exchange via Zeek conn.log and NetFlow analysis
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 abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly detection.
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 ransomware network indicators including C2 beaconing patterns, TOR exit node connections, data exfiltration flows, and encryption key exchange via Zeek conn.log and NetFlow analysis
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。