Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects anomalous file manipulation behavior consistent with the Kothamine agent, characterized by rapid create, write, and delete cycles on a single file. The detection specifically targets processes identified as either explorer.exe with a reflected Kothamine DLL module or the standalone MicrosoftEdgeUpdateCore.exe loader running from an AppData staging path.
Detects anomalous file manipulation behavior consistent with the Kothamine agent, characterized by rapid create, write, and delete cycles on a single file. The detection specifically targets processes identified as either explorer.exe with a reflected Kothamine DLL module or the standalone MicrosoftEdgeUpdateCore.exe loader running from an AppData staging path.
Detects a suspected ransomware double-extortion sequence: the bulk creation of password-protected archives using compression utilities (e.g., 7z, RAR), followed by significant outbound network connections to common cloud storage or anonymization endpoints, and concluding with mass file modifications indicative of encryption activity.
Detects modifications, creations, or deletions of critical CI/CD pipeline configuration files (GitHub Actions, GitLab CI, Jenkins, Azure Pipelines) across cloud service logs. The rule correlates these actions with the account's historical IP baseline to identify potentially unauthorized changes originating from anomalous locations.
Detects browser extension updates that request a combination of high-risk permissions capable of account takeover (e.g., cookies, webRequest, identity) for extensions previously classified as low-risk. This pattern often indicates a supply chain compromise where a benign extension is acquired and subsequently updated with malicious capabilities.
Detects browser extension updates that request a combination of high-risk permissions capable of account takeover (e.g., cookies, webRequest, identity) for extensions previously classified as low-risk. This pattern often indicates a supply chain compromise where a benign extension is acquired and subsequently updated with malicious capabilities.
Detects browser extension updates that request a combination of high-risk permissions capable of account takeover (e.g., cookies, webRequest, identity) for extensions previously classified as low-risk. This pattern often indicates a supply chain compromise where a benign extension is acquired and subsequently updated with malicious capabilities.
Detects the deletion, truncation, or modification of critical PaperCut server logs and application files (server.log, pcxboot_l.txt, .bin files). This activity is associated with attempts to conceal exploitation of PaperCut vulnerabilities (e.g., CVE-2026-82078/81578) by clearing logs or tampering with binaries, typically executed by the PaperCut application processes themselves (pc-app.exe or Java runtime).
Correlates a sequence of suspicious activities on a single host within 90 minutes: LSASS memory access consistent with credential dumping, followed by remote service or WMI-based lateral movement, and finishing with mass file renames or modifications characteristic of ransomware encryption.
Detects the creation of .aspx, .ashx, or .asp files within Exchange OWA/ECP or IIS wwwroot directories by web server processes (w3wp.exe or UMWorkerProcess.exe). This pattern is consistent with web shell deployment following the exploitation of public-facing web applications, a technique often used by threat actors like APT40.
Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Detects a suspicious sequence of events where a common internet-facing server process (e.g., w3wp.exe, nginx.exe) spawns a potentially malicious child process (e.g., cmd.exe, powershell.exe), followed shortly thereafter by mass file modifications on the same host, which is characteristic of ransomware behavior following initial exploitation.
Detects a suspicious sequence of events where a common internet-facing server process (e.g., w3wp.exe, nginx.exe) spawns a potentially malicious child process (e.g., cmd.exe, powershell.exe), followed shortly thereafter by mass file modifications on the same host, which is characteristic of ransomware behavior following initial exploitation.
Detects a suspicious sequence of events where a common internet-facing server process (e.g., w3wp.exe, nginx.exe) spawns a potentially malicious child process (e.g., cmd.exe, powershell.exe), followed shortly thereafter by mass file modifications on the same host, which is characteristic of ransomware behavior following initial exploitation.
Detects UNC6240 PSEMHUB.war web shell/tunnel/trojanized installer artifacts by distinctive content markers (x.jsp, u.jsp, tunnel.jsp/jspx, Ple64.exe)
Detects non-browser processes (e.g., non-chrome.exe/msedge.exe) modifying critical browser configuration files such as 'Secure Preferences' followed by near-simultaneous writes to extension-related directories like 'Extension State' or 'Extensions'. This pattern is indicative of a malicious attempt to bypass browser security integrity checks to silently install or load unauthorized extensions.
Detects unauthorized configuration or content changes (e.g., login or portal pages) performed outside of standard business hours by user accounts that have recently exhibited high-risk or compromised authentication behavior. This pattern aligns with tactics observed in the ShinyHunters playbook where compromised accounts are used to deface public-facing applications.
Detects potential supply chain or local configuration tampering involving the Model Context Protocol (MCP). The rule identifies modification of common MCP configuration files, followed by the execution of a tool runner (node, python, npx) that spawns a scripting shell, which subsequently initiates an unusual outbound network connection.
Detects anomalous AI-assisted activity where multiple processes (specifically AI developer tools) appear to be chaining operations by writing to and reading from shared local directories (e.g., 'handoff', 'artifacts') followed by network requests to known AI service APIs. This pattern may indicate automated task chaining or unauthorized exfiltration of context/code to AI platforms.
This rule detects modifications to Group Policy Objects (GPOs) that are either explicitly marked as malicious based on known indicators (GUIDs or 'PAYLOAD' strings) or involve unauthorized modifications to domain-root or organizational unit GPO links. It monitors Active Directory security event logs for object changes.
This rule detects unauthorized or anomalous file modifications or creations to machine learning training datasets (e.g., fine-tuning data, training corpora) by users not belonging to the authorized data engineering group, occurring within one hour of a scheduled training or fine-tuning job execution. This behavioral pattern is indicative of potential data poisoning, where an adversary attempts to inject malicious data into the training set to bias or compromise the resulting model.
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