Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This query parses the complex JSON parameters of the New-TransportRule and Set-TransportRule commands to find instances where email is being secretly copied externally or security is being lowered.
Detects the use of lifecycle hooks in `.devcontainer/devcontainer.json` files to execute potentially malicious commands. Adversaries can abuse hooks like `postCreateCommand` or `postStartCommand` in development environments like GitHub Codespaces to automatically run scripts upon container creation. This can be used to exfiltrate secrets, such as GitHub tokens, or establish further access.
Detects network connections or DNS queries to known malicious domains identified as being used for Cobalt Strike C2, data exfiltration, and secondary payload delivery during the Notepad++ supply chain attack. This rule specifically targets the domains 'cdncheck.it.com', 'safe-dns.it.com', 'self-dns.it.com', 'api.wiresguard.com', 'api.skycloudcenter.com', and 'temp.sh'.
This rule detects large volumes of outbound network traffic originating from Azure Kubernetes Service (AKS) clusters to external destinations over non-standard ports. It specifically looks for traffic allowed by Palo Alto firewalls, excluding common ports like 80 and 443, and flags transfers exceeding a defined data threshold (e.g., 100MB). This behavior can indicate data exfiltration attempts where adversaries use alternative protocols to bypass standard monitoring.
Detects the use of lifecycle hooks in `.devcontainer/devcontainer.json` files to execute potentially malicious commands. Adversaries can abuse hooks like `postCreateCommand` or `postStartCommand` in development environments like GitHub Codespaces to automatically run scripts upon container creation. This can be used to exfiltrate secrets, such as GitHub tokens, or establish further access.
This rule detects chat or call activity by external users in Microsoft Teams whose source tenant is not present in a pre-defined whitelist. This could indicate unauthorized external communication or potential data exfiltration attempts.
Detects the use of curl.exe to upload files to the temp.sh service while simultaneously passing the resulting temp.sh URL within the User-Agent string to a C2 server. This specific exfiltration and beaconing TTP was observed in the Notepad++ supply chain attack.
Detects the use of curl.exe to upload data to the anonymous file-sharing service temp.sh. This technique has been observed being used by threat actors for reconnaissance data exfiltration, notably after gaining initial access through a compromised Notepad++ updater.
This rule detects instances where a SharePoint or OneDrive file/folder is shared with a guest user whose email address is identified as a threat intelligence indicator. This could indicate an attempt to exfiltrate data to a known malicious entity or an account compromise being used for data staging.
Detects a behavioral sequence where an AI agent runtime (e.g., Node.js, Python) spawns a shell to execute a web request utility like curl or wget. This pattern is a strong indicator of automated data exfiltration, often resulting from a successful prompt injection attack that instructs the agent to steal and send data to an attacker-controlled server.
Detects instances where an AI agent process (e.g., Moltbot, ClawdBot) performs a web search or HTTP request containing suspicious internal identifiers (like API keys or project names) as URL parameters. This behavior is a hallmark of 'search-and-leak' exfiltration tactics, often triggered by indirect prompt injection.
This query is used to identify the usage of RMM tools.
This detection identifies when a user or service principal executes a command inside an AKS pod ('kubectl exec') and then, using the same identity, accesses data in a sensitive Azure Blob Storage container within a short time frame. This pattern can indicate an attacker, having gained initial access to the Kubernetes API, is using a pod as a pivot point to discover and exfiltrate sensitive data.
Detects when an Azure Kubernetes Service (AKS) worker node initiates an outbound connection to a public IP address over a non-standard or unexpected port. This could indicate data exfiltration using custom tools or protocols.
Detects techniques used by the INC Ransomware Group, including the execution of their RainINC payload from C:\PerfLogs and the abuse of a renamed Restic binary for data exfiltration.
Detects the execution of the native Windows tar.exe utility with flags that redirect the output to standard output (stdout) instead of a file. This behavior can indicate data being staged for exfiltration or piped to another tool for in-memory execution, bypassing file-based detections.
This rule detects when a system configuration file is downloaded from a FortiGate device within 60 seconds of a successful SSO login from the same user and source IP. This rapid sequence of events is indicative of automated scripts used by attackers to exfiltrate device configurations immediately after gaining initial access.
This rule detects suspicious activity related to mailbox access within Office 365. It has two main parts: 1) Identifies users accessing a high number of unique mailboxes belonging to other users. 2) Identifies users accessing a high number of unique folders within another user's mailbox. Both scenarios can indicate unauthorized access, data exfiltration attempts, or compromised accounts.
This rule detects when a user from a specified watchlist sends an email with a large attachment (over 5MB).
Detects processes, other than legitimate web browsers or the official Telegram client, making DNS queries for the Telegram Bot API. This behavior is indicative of malware using Telegram for command-and-control (C2) or data exfiltration, as seen in a campaign leveraging Amnesia RAT.
This rule detects potential network beaconing activity by analyzing network connection events. It identifies repetitive outbound connections from a device to a remote IP and port with a consistent time interval (low jitter). The rule filters out known legitimate processes and domains to reduce false positives. It calculates the average time delta between connections, standard deviation, and interquartile range to identify beacon-like patterns. The rule also considers the number of unique devices connecting to the same remote endpoint to identify potentially compromised infrastructure.
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