「attacks」の検索結果

Detect RDP brute force attacks by analyzing Windows Security Event Logs for failed authentication patterns (Event ID 4625), successful logons after failures (Event ID 4624), NLA failures, and source IP frequency 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 Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests targeting service accounts with SPNs for offline password cracking.

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 Pass-the-Hash attacks by analyzing NTLM authentication patterns, identifying Type 3 logons with NTLM where Kerberos is expected, and correlating with credential dumping.

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 Golden Ticket attacks in Active Directory by analyzing Kerberos TGT anomalies including mismatched encryption types, impossible ticket lifetimes, non-existent accounts, and forged PAC signatures in domain controller event logs.

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This skill covers detecting sophisticated cyber-physical attacks that follow the Stuxnet attack pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation from operators. It addresses PLC logic integrity monitoring, physics-based process anomaly detection, engineering workstation compromise indicators, USB-borne attack vectors, and multi-stage attack chain detection spanning IT-to-OT lateral movement through to process 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。

Detect DLL side-loading attacks where adversaries place malicious DLLs alongside legitimate applications to hijack execution flow for defense evasion.

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Detect and prevent API enumeration attacks including BOLA and IDOR exploitation by monitoring sequential identifier access patterns and authorization failures.

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Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.

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Detect cyber attacks targeting OT historian servers (OSIsoft PI, Ignition, Wonderware) that sit at the IT/OT boundary and serve as pivot points for lateral movement between enterprise and control networks, including data manipulation, unauthorized queries, and exploitation of historian-specific vulnerabilities.

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Detects prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack classification.

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Conducts authorized wireless network penetration tests to assess the security of WiFi infrastructure by testing for weak encryption protocols, captive portal bypasses, evil twin attacks, WPA2/WPA3 handshake capture, rogue access point detection, and client-side attacks. The tester evaluates wireless authentication, network segmentation, and the effectiveness of wireless intrusion detection systems. Activates for requests involving wireless pentest, WiFi security assessment, WPA2/WPA3 testing, or rogue access point detection.

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Provide comprehensive techniques for attacking Microsoft Active Directory environments. Covers reconnaissance, credential harvesting, Kerberos attacks, lateral movement, privilege escalation, and domain dominance for red team operations and penetration testing.

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How to identify and test for account takeover vulnerabilities in web applications. Use this skill whenever the user mentions account takeover, authentication bypass, password reset attacks, email verification bypass, session hijacking, or any technique to compromise user accounts. This includes testing authorization issues, unicode normalization attacks, reset token reuse, CORS/CSRF/XSS exploitation, cookie manipulation, and OAuth vulnerabilities.

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Provide comprehensive techniques for attacking Microsoft Active Directory enprojectnments. Covers reconnaissance, credential harvesting, Kerberos attacks, lateral movement, privilege escalation, and domain dominance for red team operations and penetration testing.

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Content sanitization and hidden-content detection for agent skill security. Scans markdown, HTML, and text for visually hidden but agent-readable attacks: CSS-hidden text (display:none, color:white, font-size:0, opacity:0), HTML comments with instructions, collapsible details sections, zero-width unicode, homoglyphs, misleading links, and inline HTML in markdown. Enforces mandatory sanitization before external content enters agent context. Load as part of the secure-* sequence during any repo scan or skill audit. Also load for sanitize content, check hidden text, scan markdown attacks, strip HTML, detect invisible instructions, check zero-width chars. Core principle: visibility does not equal influence — hidden content is more dangerous than visible content because agents process it but humans cannot see it.

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。

Provide comprehensive techniques for attacking Microsoft Active Directory environments. Covers reconnaissance, credential harvesting, Kerberos attacks, lateral movement, privilege escalation, and domain dominance for red team operations and penetration 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。

Provide comprehensive techniques for attacking Microsoft Active Directory environments. Covers reconnaissance, credential harvesting, Kerberos attacks, lateral movement, privilege escalation, and domain dominance for red team operations and penetration 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。

Scans GitHub Actions workflows and CI/CD pipeline configurations for supply chain attack vectors including unpinned actions, script injection via expressions, dependency confusion, and secrets exposure. Uses PyGithub and YAML parsing for automated audit. Use when hardening CI/CD pipelines or investigating compromised build 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。

This skill covers detecting sophisticated cyber-physical attacks that follow the Stuxnet attack pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation from operators. It addresses PLC logic integrity monitoring, physics-based process anomaly detection, engineering workstation compromise indicators, USB-borne attack vectors, and multi-stage attack chain detection spanning IT-to-OT lateral movement through to process 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。

Detect Kerberos Pass-the-Ticket (PtT) attacks by analyzing Windows Event IDs 4768, 4769, and 4771 for anomalous ticket usage patterns in Splunk and Elastic SIEM

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 command injection attacks against Modbus TCP/RTU protocol in ICS environments by monitoring for unauthorized write operations, anomalous function codes, malformed frames, and deviations from established communication baselines using ICS-aware IDS and protocol deep packet inspection.

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 Pass-the-Hash attacks by analyzing NTLM authentication patterns, identifying Type 3 logons with NTLM where Kerberos is expected, and correlating with credential dumping.

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 AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.

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 NTLM relay attacks through Windows Security Event correlation by analyzing Event 4624 LogonType 3 for IP-to-hostname mismatches, identifying Responder/LLMNR poisoning artifacts, auditing SMB and LDAP signing enforcement across the domain, and detecting NTLM downgrade attacks from NTLMv2 to NTLMv1 using event log 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。