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Supports authorized security exercises that test data transfer outside a network. Organizes channel choices, compression, encryption, splitting, and receipt verification, alongside the monitoring signals defenders can use to identify different transfer methods.
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Dense methodology covering DNS exfiltration (dnscat2, iodine, dns2tcp), HTTPS tunneling (domain fronting, CDN abuse, legitimate service channels), ICMP tunneling (icmpsh, ptunnel-ng), cloud storage dead drops (S3 presigned URLs, Azure Blob SAS tokens, GCS signed URLs), email-based exfil (SMTP, EWS, draft method), steganography (image, audio, document metadata), encoding/encryption (base64 chunking, XOR, AES), covert channels (custom protocol tunneling, HTTP header encoding, timing channels), and data staging (compression, splitting, encryption). Tools: dnscat2, iodine, dns2tcp, PacketWhisper, chisel, stunnel, icmpsh, ptunnel-ng, steghide, zsteg, OpenStego. MITRE ATT&CK: T1048 (Exfiltration Over Alternative Protocol), T1041 (Exfiltration Over C2 Channel), T1567 (Exfiltration Over Web Service), T1029 (Scheduled Transfer), T1030 (Data Transfer Size Limits), T1132 (Data Encoding), T1001 (Data Obfuscation). Use when planning or executing data exfiltration during authorized red team engagements or post-exploita...
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Analyze DNS logs to find signs of hidden data transfers. Use query length, character randomness, traffic volume, and response sizes to identify suspicious domains and support monitoring rules with Python, Suricata, and Splunk examples.
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Examine captured network traffic for hidden malware communications in DNS, ICMP, and HTTP. Analyze domain patterns, packet sizes, and traffic frequency to identify suspicious channels used for remote control or data theft.
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Analyze Zeek DNS logs for possible data theft hidden in domain queries. The skill examines unusual character patterns, long labels, distinct subdomains, and query volume, then ranks suspicious domains and produces a JSON evidence report.
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Analyze Zeek and NetFlow records for ransomware-related activity, including regular callbacks to attacker infrastructure, TOR connections, and possible data exfiltration. Combine indicators into risk scores and structured reports with timelines to support threat hunting and incident investigation.
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Test organizational egress controls by executing various data exfiltration techniques during a red team engagement. Use this skill to simulate an adversary attempting to steal sensitive data without triggering DLP (Data Loss Prevention) or network monitoring alerts. Covers exfiltration over DNS, ICMP, HTTP/S, alternative protocols (SSH/FTP), cloud services, and physical methods.
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Detects DNS-based tunneling and data exfiltration in DNS logs: scoring high query volume to single domains, long/high-entropy subdomains, unusual record types, and encoded payload labels. Activates for requests to hunt DNS tunneling, detect DNS exfiltration, or analyze suspicious DNS query patterns.
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Detect data exfiltration through DNS tunneling by analyzing query entropy, subdomain length, query volume, TXT record abuse, and response payload sizes using passive DNS monitoring.
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Detect and analyze covert communication channels used by malware including DNS tunneling, ICMP exfiltration, steganographic HTTP, and protocol abuse for C2 and data exfiltration.
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Detect data exfiltration through DNS tunneling by analyzing query entropy, subdomain length, query volume, TXT record abuse, and response payload sizes using passive DNS monitoring.
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Detect and analyze covert communication channels used by malware including DNS tunneling, ICMP exfiltration, steganographic HTTP, and protocol abuse for C2 and data exfiltration.
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Detect data exfiltration through DNS tunneling by analyzing query entropy, subdomain length, query volume, TXT record abuse, and response payload sizes using passive DNS monitoring.
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Detect and analyze covert communication channels used by malware including DNS tunneling, ICMP exfiltration, steganographic HTTP, and protocol abuse for C2 and data exfiltration.
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Investigate suspected data theft from AWS S3 by comparing access logs, GuardDuty findings, and Macie classifications. Identify unusual bulk downloads and transfers across accounts, assess sensitive data exposure, and build automated detection rules.
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Analyze endpoint, cloud storage, and email logs for signs of insider data theft. Use pandas to compare upload volumes with normal user behavior and examine data loss prevention alerts, unusual file access, and activity outside working hours.
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Analyze DNS query logs for signs of hidden data transfers and attacker command channels. This skill uses unusual name lengths, randomness, and traffic volumes, then correlates findings with endpoint processes and estimates possible data loss.
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Detect data exfiltration via URL path encoding and chained web_fetch navigation. Covers fake trusted UI injection, letter-level URL path exfiltration, and multi-hop navigation hijacking. Use when the agent has web/URL fetch capability and stores user memory or personal context.
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Test Retrieval-Augmented Generation (RAG) systems for data poisoning, prompt injection via retrieved documents, and data exfiltration through manipulated context windows. Use this skill when assessing RAG-based chatbots, knowledge bases, enterprise AI assistants, or any system that augments LLM responses with external document retrieval. Covers document injection, embedding manipulation, knowledge base poisoning, and cross-document inference attacks.
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Hunts for pre-exfiltration data staging and archiving by detecting archive-tool execution (rar, 7z, zip, tar), large compressed files written to staging directories, and collection into a single location before transfer. Activates for requests to hunt data staging, detect pre-exfil archiving, or find collection and compression before exfiltration.
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Analyzes infostealer samples by mapping the browser, credential store, wallet, and application paths they target, and the exfiltration channel used, from static strings and behavior. Activates for requests to analyze an infostealer, identify targeted credential stores, or map stealer collection and exfiltration behavior.
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Detect DNS-based data exfiltration by analyzing Zeek dns.log for high-entropy subdomains and anomalous query patterns
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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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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.
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