Results for “dml-exception”

Detect DCOM-based lateral movement by correlating Sysmon process creation and network connection events, WMI event analysis, and RPC endpoint mapper traffic to identify abuse of MMC20.Application, ShellBrowserWindow, and ShellWindows COM objects.

Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs using pandas for behavioral analytics and statistical baselines.

Deploys endpoint Data Loss Prevention (DLP) controls to detect and prevent sensitive data exfiltration through email, USB, cloud storage, and printing using Microsoft Purview or Symantec DLP.

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

Coerce a domain controller to authenticate to an attacker-controlled host and relay that NTLM authentication to an AD CS web enrollment endpoint to obtain a certificate for the DC machine account, enabling full domain compromise via DCSync.

Detect and exploit XML External Entity (XXE) injection vulnerabilities in XML parsers, including file disclosure, SSRF, and blind out-of-band exfiltration.

Prevents SOQL injection and enforces CRUD, FLS, and sharing in Apex queries, with guidance for building, reviewing, and troubleshooting secure data access.

Extract Active Directory credentials via DCSync attacks and establish domain persistence by dumping KRBTGT, Domain Admin, and service account hashes for Golden Ticket creation.

Exploit Active Directory replication privileges (DS-Replication-Get-Changes) to perform a DCSync attack, allowing an attacker to impersonate a Domain Controller and extract password hashes (like the krbtgt hash for Golden Tickets) without code execution on a DC.

Configures sensitivity labels, DLP policies, and endpoint data protection rules in Microsoft Purview to safeguard sensitive information across Exchange, SharePoint, OneDrive, Teams, and endpoints.

Detect prompt injections and jailbreak attempts in LLM applications using Meta's 86M parameter classifier. Filter user inputs, third-party data, and RAG documents with low latency and multilingual support.

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