Results for “maldoc”

Escalate from standard domain user to Domain Admin by exploiting the noPac vulnerability chain (CVE-2021-42278 sAMAccountName spoofing and CVE-2021-42287 KDC PAC confusion) in Active Directory environments.

Classify medical devices under EU MDR 2017/745, build technical documentation, plan clinical evaluations, and manage post-market surveillance and EUDAMED integration.

Guides authorized macOS and Mach-O reverse engineering covering codesign analysis, Objective-C/Swift symbol recovery, endpoint security surfaces, and Apple platform malware analysis.

Active Directory and network lateral movement assessment. Pass-the-hash, pass-the-ticket, Kerberoasting, AS-REP roasting, NTLM relay, SMB relay, WMI/WinRM/PSRemoting abuse, constrained/unconstrained delegation, RBCD, cross-trust exploitation, and pivoting. Uses impacket, netexec, enum4linux-ng, smbmap, smbclient, bloodhound-python, Responder, ntlmrelayx, mitm6, and ldap-utils. Chains from /post-exploit or /credential-audit.

Get best practices for developing applications with Helidon 4 (SE and MP). Use when working with Helidon SE or Helidon MP, HttpService routing, Helidon DB Client, MicroProfile Config, Helidon Security, or Helidon testing in Java 21+ projects.

Query the Malpedia API to research malware family relationships, track variant evolution, link families to threat actors, and integrate YARA rules for detection across malware lineages.

Analyzes malicious PDF files using PDFiD, pdf-parser, and peepdf to identify embedded JavaScript, shellcode, exploits, and suspicious objects without opening the document. Determines the attack vector and extracts embedded payloads for further analysis.

Analyze suspected malware through static, dynamic, and behavioral techniques, including IOC extraction, YARA or Sigma rules, sandboxing, and anti-analysis behavior detection.

Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.