Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects potential browser-based credential and session theft by monitoring for a process that accesses multiple sensitive web browser artifacts (cookies, local state, session storage) across several user profiles in a short timeframe. It correlates this activity with the subsequent creation of a compressed archive in a temporary directory followed by an outbound network connection, indicating a multi-stage exfiltration workflow.
Detects two distinct suspicious patterns: first, the deletion of executable files residing in common user-writable temporary directories (e.g., Temp, Downloads, AppData) using command-line tools like 'del' or 'erase', which is often indicative of anti-forensic activity after malware execution. Second, it monitors for network traffic involving HTTP POST requests or specific SOAP headers related to 'tempuri.org', which are commonly associated with default .NET WCF service scaffolding and may be used by adversaries for C2 communication or exfiltration.
Detects two distinct suspicious patterns: first, the deletion of executable files residing in common user-writable temporary directories (e.g., Temp, Downloads, AppData) using command-line tools like 'del' or 'erase', which is often indicative of anti-forensic activity after malware execution. Second, it monitors for network traffic involving HTTP POST requests or specific SOAP headers related to 'tempuri.org', which are commonly associated with default .NET WCF service scaffolding and may be used by adversaries for C2 communication or exfiltration.
Detects a suspected infostealer activity chain involving the unauthorized access of credential and session cookie files, followed by the archival of these files into a temporary or staging directory, and concluding with an outbound network connection to an uncommon destination from the archiving process.
Detects the first-ever observed network connection from a device to WhatsApp Web or Telegram Web within a 30-day lookback period. This behavior is used to identify potentially unauthorized companion-device linking to a user's messenger account, which could indicate credential theft or unauthorized access.
This rule monitors for network connections to known malicious domains and IP addresses, as well as the presence or execution of files with specific SHA256 hashes known to be associated with threat activity. The indicators focus on Vercel-hosted domains and specific file hashes linked to recent campaign activity.
Detects network activity associated with the 'ClickFix' social engineering campaign, which commonly targets users with fake error messages to trick them into executing malicious commands. The rule identifies communication with known malicious infrastructure (domains and IP addresses) found in CommonSecurityLog events, while implementing filters to exclude common security scanners and deduplicating per-host alerts.
Detects network activity associated with the 'ClickFix' social engineering campaign, which commonly targets users with fake error messages to trick them into executing malicious commands. The rule identifies communication with known malicious infrastructure (domains and IP addresses) found in CommonSecurityLog events, while implementing filters to exclude common security scanners and deduplicating per-host alerts.
Detects network activity associated with the 'ClickFix' social engineering campaign, which commonly targets users with fake error messages to trick them into executing malicious commands. The rule identifies communication with known malicious infrastructure (domains and IP addresses) found in CommonSecurityLog events, while implementing filters to exclude common security scanners and deduplicating per-host alerts.
This rule detects various malicious indicators including known file hashes, IP addresses, domains, and specific URLs associated with threat activity. It consolidates hits from process, file, and network telemetry to alert on potential compromise or communication with identified command-and-control (C2) infrastructure.
Detects the initialization and persistence of a 'tailcat.exe' process operating from a non-standard, user-profile directory, typically mimicking legitimate tools. The rule monitors for the specific 'forward' command-line argument containing port mapping strings, an immediate loopback connection to that port, and subsequent periodic external network keep-alive traffic originating from the same process or parent lineage, indicating a C2 tunnel setup.
Hunts telemetry for published NeedyMantis indicators: three known SHA-256 hashes (WinSparkle.dll first-stage loader and its encrypted archive, plus the older libcurl archive), the C2 domain corp.tripswithengine[.]com, and the C2 URL path /library/zip/ on that domain. Domain/URL matching parses the actual host out of RemoteUrl and requires an EXACT match (not substring 'has/contains'), so a different domain that merely contains 'corp.tripswithengine.com' as a suffix of a longer label (e.g. 'notcorp.tripswithengine.com') or as a prefix of an unrelated domain (e.g. 'corp.tripswithengine.com.evil.net') cannot match. No malicious IP address has been published for this campaign, so IP-based matching is intentionally not included.
This rule detects a multi-stage phishing redirection chain where a user visits a known survey platform (t.maze.co) followed immediately by a request to a Cloudflare Workers landing page (.workers.dev), a common pattern used by adversaries to obfuscate malicious delivery endpoints.
Detects anomalous outbound lateral movement attempts where a single source host initiates WinRM connections (ports 5985/5986) to an unusually high number of distinct destination hosts within a short timeframe, which is often indicative of automated credential propagation or internal scanning by an adversary.
Detects high-volume outbound data transfers (exceeding 100MB) directed towards common cloud storage providers, file sharing services, and AI model endpoints. This behavior is indicative of potential data exfiltration by an adversary using legitimate web services to bypass traditional network security controls.
Detects large outbound HTTP POST requests to known public AI-inference APIs and paste services. This pattern is potentially indicative of data exfiltration or sensitive information exposure where attackers leverage third-party services to bypass traditional outbound egress filtering.
Detects outbound HTTP requests traversing a proxy where the User-Agent string contains structurally consistent but algorithmically randomized components. This pattern is characteristic of AI-generated C2 client code attempting to create plausible yet non-standard browser signatures for each beacon. Detection relies on identifying User-Agent strings that follow a standard browser template (e.g., Chrome/Safari) but include anomalous hex/alphanumeric tokens in the version fields.
Detects unauthorized processes attempting to access the cloud instance metadata service (169.254.169.254). Adversaries frequently target this endpoint to extract sensitive instance information, such as IAM credentials or configuration data, typically as part of post-exploitation discovery or SSRF-based attacks.
Detects suspicious outbound HTTPS POST traffic to major generative AI API endpoints (OpenAI, Anthropic, Google) originating from scripting hosts like powershell.exe or python.exe. The detection is triggered by either unusually large outbound payload sizes or the use of unauthorized scripting tools, which is indicative of data exfiltration using AI infrastructure as a relay.
Detects scripting interpreters (wscript.exe, cscript.exe, powershell.exe, python.exe) that perform an outbound network connection to known public Generative AI API endpoints and subsequently modify their own script file on disk. This behavior is indicative of sophisticated malware attempting to rewrite its own source code at runtime to evade signature-based detection.
This rule detects potential DNS tunneling activities often associated with Command and Control (C2) communication. It monitors for anomalous patterns including excessively long DNS query labels, the use of uncommon DNS record types (TXT or NULL), and high volumes of DNS traffic directed towards specific domains. These behaviors are common indicators of data exfiltration or covert beaconing attempts designed to bypass traditional network security controls.
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