Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects non-browser HTTP POST requests containing both 'access_token' and step-style parameters (e.g., 'step', 'stage', 'counter' followed by a number) in proxy or firewall telemetry. This rule specifically targets patterns associated with malware communication, such as SessionGate, Remus, or AnimateClipper, by excluding common browser user agents.
This rule detects VBScript execution (via wscript.exe or cscript.exe) that attempts to load WinHTTP objects (WinHttpRequest.5.1, MSXML2.XMLHTTP) or uses generic object creation functions (CreateObject, GetObject). This behavior is often associated with VBScripts establishing command and control (C2) communication or performing data exfiltration.
Detects curl.exe where system metadata is concatenated into User-Agent/Referer fields with specific delimiters.
This rule correlates multiple low-to-medium confidence signals across file, process, network, and registry events to detect potential ransomware activity. The rule identifies suspicious behaviors including mass file renames, creation of known ransom note file types in multiple directories, shadow copy deletion via system utilities, disabling of antivirus and security monitoring, large archive staging, unauthorized outbound network connections, and suspicious process injection. Alerts are generated based on the aggregation and frequency of these signals, indicating a high probability of ransomware-related malicious activity.
This rule detects suspicious activity related to high-volume Server Message Block (SMB) share enumeration and the execution of a specific Python script named 'backup_dfs.py'. It combines three detection logics: identifying processes executing 'backup_dfs.py', detecting an unusual number of SMB network connections to common administrative shares (SYSVOL, NETLOGON, DFS), and flagging high-volume SMB-related identity directory events targeting SYSVOL or NETLOGON. This behavior could indicate an adversary performing discovery of network shares, collecting data, or preparing for data exfiltration.
This rule detects potential ransomware activity by identifying a combination of three distinct behaviors occurring on a single endpoint within a short window: high-volume file modifications (indicative of file encryption), deletion of system recovery resources (e.g., volume shadow copies, backup catalogs), and high-frequency network activity (indicative of data exfiltration). The rule uses cross-event correlation to reduce false positives by requiring all three signals to be present.
This rule detects validly signed executables making HTTP POST connections, which could indicate data exfiltration or command and control (C2) communication by a signed dropper. It correlates network events with image load events to identify processes with valid signatures performing HTTP POST requests.
Detects scripts launched from developer directories (e.g., GitHub, GitLab, Bitbucket, or directories containing 'interview' in their path) that attempt to exfiltrate data by making network connections to Discord or Telegram API webhooks. This rule specifically looks for network events where the remote URL matches Discord or Telegram webhook patterns, and correlates them with process events where the initiating process's folder path suggests a developer-related or interview-related context.
This rule monitors for large outbound network data transfers (greater than 10MB) over standard web ports (80/443) initiated by processes typically associated with office productivity applications (e.g., WinWord, Excel, Outlook) or credential dumping tools (lsass.exe). It further correlates this activity with preceding network connections to common directory service ports (LDAP/Global Catalog) on the same host, indicating potential credential collection followed by data exfiltration.
Detects unauthorized processes (excluding known web browsers and system processes) attempting to access sensitive browser credential files (e.g., Login Data, key4.db) followed by a network connection to external IP addresses. This behavior is highly indicative of credential theft and subsequent exfiltration.
This rule monitors GitHub repository activity (git.push, git.clone, repo.contents) to identify specific activity patterns associated with known GitHub usernames, email addresses, or specific repository paths (e.g., tools/setup, .github/scripts/precheck) and commit messages. It flags potentially significant actions such as forced pushes to default branches for review.
This rule detects a process identified as a Go-compiled binary (via command-line arguments like 'goroutine' or 'runtime.main') that exhibits rapid, high-volume file interactions (more than 100 files accessed or created in a 60-second window) alongside outbound network connectivity. This behavior is indicative of malicious automated activity such as file encryption for ransomware or rapid data staging and exfiltration.
Detects network connections to Filen.io cloud storage initiated by processes other than common web browsers. This behavior is often associated with the use of command-line tools or scripts for unauthorized file exfiltration or data staging.
This rule monitors for outbound network connections to 'graph.microsoft.com' initiated by processes that are either unsigned or not digitally signed by Microsoft. It further correlates these connections with proxy logs to identify processes utilizing non-standard or unexpected User-Agents. This behavior is indicative of potentially malicious activity attempting to masquerade as legitimate Microsoft services or using the Microsoft Graph API for command and control or data exfiltration.
This rule detects a process identified as a Go-compiled binary (via command-line arguments like 'goroutine' or 'runtime.main') that exhibits rapid, high-volume file interactions (more than 100 files accessed or created in a 60-second window) alongside outbound network connectivity. This behavior is indicative of malicious automated activity such as file encryption for ransomware or rapid data staging and exfiltration.
This rule monitors for indicators of unauthorized Tor usage or anonymization activity, including non-standard Tor executable paths, suspicious library loads, the creation of Tor configuration files (torrc), non-browser processes communicating over known Tor ports, and DNS queries for .onion domains. It also specifically monitors for potential data exfiltration via the temp.sh file-sharing service from non-browser applications. The rule aggregates these suspicious signals over a 24-hour period to calculate a risk severity score based on the frequency and variety of indicators detected.
Detects high volume HTTPS outbound traffic (greater than 50MB within a 5-minute window) from a host that does not show evidence of preceding file encryption or volume shadow copy deletion activity. This rule is designed to identify potential unauthorized data exfiltration that deviates from typical ransomware-associated behavior patterns.
Detects the deletion of a storage bucket immediately followed by the creation of a new bucket with the same name across different cloud providers (GCP, AWS S3, Azure). This rule specifically flags scenarios where the deleting actor or project differs from the creating actor or project, indicating a potential cross-account hijack or data exfiltration attempt.
This rule detects a potential Azure storage account name hijacking scenario. It identifies instances where a storage account is deleted and then recreated with the exact same name in a different subscription within the same Azure tenant, all within a 60-minute window. This activity could indicate an attempt to exfiltrate data via Azure Monitor diagnostic settings by taking over a previously used storage account name.
Detects a potential S3 bucket hijacking attempt where an S3 bucket is deleted and shortly after, an AWS Firehose delivery stream is created or updated to target a bucket with the same name, but under a different AWS account. This could indicate an adversary attempting to exfiltrate data by redirecting data intended for the original bucket to their own controlled bucket.
Detects instances where a process not associated with standard SSH operations (e.g., ssh, scp, git) accesses SSH private key files, followed by an outbound network connection to a non-private IP address within a 5-minute window. This behavior is indicative of credential theft and subsequent exfiltration of sensitive SSH keys, often associated with infostealer malware.
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