Parse and analyze Cobalt Strike Malleable C2 profiles using dissect.cobaltstrike and pyMalleableC2 to extract C2 indicators, detect evasion techniques, and generate network detection signatures.
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Parse and analyze Cobalt Strike Malleable C2 profiles using dissect.cobaltstrike and pyMalleableC2 to extract C2 indicators, detect evasion techniques, and generate network detection signatures.
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。
Uses the Linux Audit framework (auditd) with ausearch and aureport utilities to detect intrusion attempts, unauthorized access, privilege escalation, and suspicious system activity. Covers audit rule configuration, log querying, timeline reconstruction, and integration with SIEM platforms. Activates for requests involving auditd analysis, Linux audit log investigation, ausearch queries, aureport summaries, or host-based intrusion detection on Linux.
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。
IDS/IPS detection content for a CVE — Snort/Suricata-compatible rules, YARA signatures, ProjectDiscovery Nuclei templates, traffic-filter rules. Capability-aware: skips families when the binary is not installed (no Snort = no Snort output). Use when deploying defences for a CVE without a patch, augmenting SAST with active detection, or feeding the SOC engineering pipeline.
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Detects the mobile stack of the current project (Flutter, Android, iOS, React Native, Kotlin Multiplatform), its languages and target platforms, using the reis-mobile detector. Use when you need to know which mobile stack a project uses before reviewing, debugging or changing it, or when the user asks "what stack is this project?".
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Use when working with Crowdstrike — crowdStrike Falcon endpoint detection and response, threat intelligence, host management, and incident investigation. Covers detection queries, host inventory, IOC searches, real-time response sessions, and vulnerability assessment. Use when investigating security incidents, reviewing endpoint health, analyzing threat detections, or managing CrowdStrike Falcon sensors.
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Use when working with Chronicle — google Chronicle SIEM security operations, threat detection, asset investigation, and log analysis. Covers UDM search queries, detection rule management, asset and IOC lookups, reference list management, and retrohunt execution. Use when investigating security events, reviewing detection rules, searching for IOCs, or analyzing Chronicle data ingestion health.
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Use when working with Trivy — trivy comprehensive security scanner. Covers container image scanning, filesystem scanning, Kubernetes cluster scanning, SBOM generation, secret detection, license scanning, and misconfiguration detection. Use when scanning containers for vulnerabilities, generating SBOMs, scanning K8s clusters, or detecting misconfigurations.
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。
PVDetector, a training-free framework that detects PI attacks during LLM inference by measuring hidd GAP: no mappable capability in latest signal (our
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。
Expert en détection de contenu IA (watermarking, outils de détection, originalité, intégrité académique)
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Anomaly Detection Specialist IA — Expert en détection d'anomalies (statistique, ML, séries temporelles, alerting temps réel)
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Hunt for threats in AWS environments using Detective behavior graphs, entity investigation timelines, GuardDuty Bul:ing correlation, and automated entity profiling across IAM users, EC2 instances, and IP addresses.
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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.
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Detect privilege escalation attempts including token manipulation, UAC bypass, unquoted service paths, kernel exploits, and sudo/doas abuse across Windows and Linux.
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Detect malicious email forwarding rules created by adversaries to maintain persistent access to email communications for intelligence collection and BEC attacks.
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Detect and prevent privilege escalation in Kubernetes pods by monitoring security contexts, capabilities, and syscall patterns with Falco and OPA policies.
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Detect abuse of service accounts through anomalous interactive logons, privilege escalation, lateral movement, and unauthorized access patterns.
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Detects and prevents code injection attacks targeting serverless functions (AWS Lambda, Azure Functions, Google Cloud Functions) through event source poisoning, malicious layer injection, runtime command execution, and IAM privilege escalation via function modification. The analyst combines static analysis of function code, CloudTrail event correlation, runtime behavior monitoring, and IAM policy auditing to identify injection vectors across the expanded serverless attack surface including API Gateway, S3, SQS, DynamoDB Streams, and CloudWatch event triggers. Activates for requests involving Lambda security assessment, serverless injection detection, function event poisoning analysis, or serverless privilege escalation investigation.
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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.
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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.
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Analyze WAF (ModSecurity/AWS WAF/Cloudflare) logs to detect SQL injection attack campaigns. Parses ModSecurity audit logs and JSON WAF event logs to identify SQLi patterns (UNION SELECT, OR 1=1, SLEEP(), BENCHMARK()), tracks attack sources, correlates multi-stage injection attempts, and generates incident reports with OWASP classification.
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Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests targeting service accounts with SPNs for offline password cracking.
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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.
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Detect process hollowing (T1055.012) by analyzing memory-mapped sections, hollowed process indicators, and parent-child process anomalies in EDR telemetry.
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Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access 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。