Arnold Chan
@slazTrusted contributorCompletionist
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7,555 detections
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Detects a sequence of activity characteristic of lateral movement and credential theft: the deployment or execution of PsExec service followed by the use of Windows utilities (vssadmin, ntdsutil, esentutl) to perform Volume Shadow Copy-based extraction of the NTDS.dit database or SYSTEM hive on the same host.
Detects potential credential reuse attacks such as Pass-the-Hash, Pass-the-Ticket, or Overpass-the-Hash. The rule identifies a 'NewCredentials' logon (e.g., runas /netonly) followed by a network or remote interactive logon to a target system (such as a domain controller) by the same user, without an interactive logon session occurring between the events, which is indicative of using stolen credentials to move laterally.
Detects phishing messages that deliberately mix writing systems/scripts/homoglyphs in the Subject line as ONE corroborating signal within a broader, multi-signal phishing pattern. Hardened against false positives: brand/product tokens (e.g. Microsoft, PayPal) are stripped before the Latin-script check so a lone brand mention in non-Latin correspondence doesn't count as mixed-script; the suspicious-URL signal requires a high-confidence credential/urgency term (not generic business vocabulary); sender risk requires the domain to be new to both the recipient and the organization; and at least three independent signal categories (not two) must corroborate the language-confusion signal before the rule fires.
Correlates a successful OAuth device-code sign-in with a subsequent Device Registration Service event for the same user within 30 minutes, excluding known legitimate device-code applications (Azure CLI, Azure PowerShell, Visual Studio, VS Code) and requiring both events to have succeeded. The registration match is scoped strictly to the Device Registration Service resource (not the broader Microsoft Authentication Broker app, which fires on routine token refresh/broker activity unrelated to registering a new device). The correlation is further restricted to cases where the device-registration event originates from a different IP address or city than the original device-code sign-in -- the pattern expected when an attacker uses a stolen token to register their own device and mint a Primary Refresh Token, versus a legitimate user completing device enrollment from their own network shortly after a CLI/developer login.
Detects AI coding assistants and LLM agent frameworks attempting to access, read, or export sensitive credential files, registry hives, or application secrets. The rule monitors both file system operations on known secret locations and process execution patterns indicative of credential dumping or API token retrieval by these tools.
Detects rapid, multi-category system and network enumeration performed by AI-assisted development tools (e.g., Cursor, Claude, ChatGPT). The rule triggers when an AI agent process performs a comprehensive scan of the host environment, specifically covering machine/OS identity, network configuration, process listings, installed software, and access to sensitive credential files within a 10-minute window. It requires at least 5 distinct discovery categories and 6 total events to reduce noise from routine debugging. Covers T1082, T1016, T1057, T1518
Detects potential multimodal prompt injection attacks where an AI agent process ingests a file (PDF, image, etc.) from a browser or mail client and subsequently performs suspicious downstream activity, such as spawning a shell with execution primitives or making network connections to rare, non-reputable external domains. Covers T1204, T1059, T1105
Detects AI coding assistants and LLM agent frameworks spawning shell processes or interpreters to execute potentially malicious commands. The rule monitors for suspicious activity often associated with download cradles, credential access, reconnaissance, or persistence, while excluding standard interactive terminal usage.
Detects potentially malicious OAuth device code sign-ins by identifying sign-in events that exhibit anomalous characteristics, such as atypical geographic locations, rare client-application combinations, unmanaged/noncompliant device status, or elevated risk signals associated with the account. This detection helps identify account compromise or malicious OAuth consent activity often associated with 'EvilTokens' or device-code phishing attacks.
Detects a specific adversarial pattern used to bypass authentication protections, where a browser process first navigates to an attacker-controlled identity verification or CAPTCHA challenge page, followed by an immediate navigation to legitimate Microsoft device code authentication endpoints within the same process session.
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