Results for “amcards”

Perform evidence-backed security audits for the Adobe Dispatcher Apache HTTP Server module and related HTTPD configuration in AMS workflows.

Parses and analyzes the Windows Amcache.hve registry hive to extract evidence of program execution, application installation, and driver loading for digital forensics investigations.

Extract cached credentials, password hashes, Kerberos tickets, and authentication tokens from memory dumps using Volatility and Mimikatz for forensic investigation.

Identify AWS IAM privilege escalation paths by analyzing policies for dangerous permission combinations and least-privilege violations using boto3 and Cloudsplaining-style analysis.

Enumerate and audit Active Directory forest trust relationships using impacket for SID filtering analysis, trust key extraction, cross-forest SID history abuse detection, and inter-realm Kerberos ticket assessment.

Assess Active Directory security posture using PingCastle, BloodHound, and Purple Knight to identify misconfigurations, privilege escalation paths, and attack vectors.

Active Directory security audit using the MITRE ATT&CK framework. Full domain enumeration, trust mapping, GPO analysis, ACL abuse paths, ADCS attacks (ESC1-ESC8), delegation abuse (constrained/unconstrained/RBCD), fine-grained password policies, LAPS deployment, service account security, and Kerberos configuration. Uses enum4linux-ng, netexec, impacket, ldapsearch, certipy-ad, bloodhound-python, and rpcclient. Produces attack path diagrams, prioritized risk register, and PoCs. Chains into /gh-export for issue filing.

Take over Active Directory user and computer accounts by writing alternate certificate keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, and Certipy, then authenticate via PKINIT.

Audit Claude Code configuration files for security vulnerabilities, misconfigurations, and injection risks using AgentShield.

中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.