Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects high-frequency access requests directed at authentication or API endpoints originating from known Tor exit-node IP addresses. This activity is indicative of credential stuffing or automated scanning patterns that attempt to evade IP-based reputation or rate-limiting filters.
This rule correlates endpoint-based alerts related to credential theft or infostealer malware with subsequent successful cloud identity authentication events. It identifies situations where a user, who has recently triggered a credential theft alert on an endpoint, logs into a cloud environment from a country or IP address that has not been historically associated with that user within a 24-hour window.
This rule detects potentially malicious POST requests to SharePoint endpoints (such as AddGallery.aspx) that include indicators of WebPartMarkup or ToolPane payloads. This activity attempts to bypass the SPUtility.EnsureAuthentication() check that is typically enforced on the ToolPane.aspx page, potentially allowing unauthorized code execution or configuration manipulation.
This rule detects security alerts (IDS, WAF, or infrastructure logs) indicating exploitation attempts against JFrog Artifactory instances related to CVE-2026-66384, specifically involving cache poisoning techniques observed in recent threat campaigns.
This rule monitors for network communication with a specific known malicious IP address and URL path, as well as the execution or presence of associated malicious file hashes. Additionally, it flags emails involving specific indicators identified as potentially associated with malicious activity.
This rule monitors for network communication with a specific known malicious IP address and URL path, as well as the execution or presence of associated malicious file hashes. Additionally, it flags emails involving specific indicators identified as potentially associated with malicious activity.
This rule monitors for network communication with a specific known malicious IP address and URL path, as well as the execution or presence of associated malicious file hashes. Additionally, it flags emails involving specific indicators identified as potentially associated with malicious activity.
Detects an suspicious progression of activities originating from identified AI assistant/agent processes. The rule monitors for a chain of behaviors occurring within a 15-minute window: execution of an AI agent/tool, followed by host discovery (network/system enumeration), access to sensitive local credential files, archival of data, and outbound network communication to common file-sharing/exfiltration platforms.
Detects instances where attrib.exe is executed with minimal or no command-line arguments, launched by processes other than standard Windows shell processes like cmd.exe, explorer.exe, or powershell.exe. This pattern is often indicative of potential masquerading, where malicious binaries are renamed to mimic legitimate Windows system utilities to evade detection.
Detects anomalous authentication behavior originating from a single source IP targeting an SSL VPN gateway. The rule identifies a high volume of unique usernames failing authentication within a short time window, a pattern indicative of password spraying and often associated with initial access attempts on environments lacking multi-factor authentication (MFA), such as those leveraged in Akira ransomware operations.
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Detects high-frequency authentication attempts originating from user agents associated with automation frameworks (Selenium, Playwright, Puppeteer, etc.). This pattern is often indicative of automated credential stuffing, brute force attacks, or CAPTCHA/2FA bypass attempts orchestrated by headless browsers.
This rule performs a point-in-time IOC hunt within CommonSecurityLog data to detect indicators associated with NeedyMantis. It monitors for specific C2 infrastructure domains ('tripswithengine.com') and a hardcoded user-agent string ('Firefox/21.0') in proxy, firewall, or network logs, which are characteristic of this threat actor's activity.
Detects HTTP proxy requests for ZIP archives where the Referer header indicates a navigation originated from a GitHub Pages (github.io) site, but the destination is not a GitHub domain. This behavior is indicative of a multi-hop redirect chain used in campaigns to deliver malicious payloads, such as infostealers, by abusing GitHub Pages to redirect users to external hosting for ZIP archive downloads.
This rule detects specific JavaScript helper functions and state machine constants commonly used in 'ClickFix' phishing campaigns (e.g., Exvicy and ErrTraffic). These scripts typically prompt users to copy and paste malicious code snippets, often disguised as error fixes or CAPTCHA resolution, to facilitate initial access.
Detects the presence of a specific, hardcoded Ed25519 public key associated with malicious cross-platform payloads (npm, Go modules, Terraform providers) that use Slack for C2 communication.
Detects high-volume outbound transfers of sensitive machine learning artifacts, such as model files, training datasets, and model registries, to unauthorized external destinations like personal email or unmanaged cloud storage. This behavior is indicative of potential intellectual property theft.
This rule detects a multi-stage attack correlation where a user receives multiple inbound emails flagged for high likelihood of being LLM-generated (and containing urgency/credential-harvesting language) and subsequently performs a voice or video authentication attempt that is flagged by deepfake detection systems with a low confidence score, indicating a potential impersonation attempt.
Detects suspicious mobile-device HTTP traffic patterns indicative of 'quishing' (QR code phishing). The rule identifies mobile HTTP GET requests (iPhone, Android, Mobile Safari, iPad) that lack a Referer header, followed by HTTP POST requests to common credential entry URI patterns (e.g., /login, /signin) over the same protocol, which is a behavioral indicator of a user being redirected from a scanned QR code to a malicious phishing landing page.
Detects successful administrative logins to VMware ESXi, vCenter, or related VDI infrastructure (Horizon) by users who have recently performed a password or MFA reset. This pattern is indicative of a helpdesk-based social engineering attack (vishing) or credential compromise where the attacker regains account access and immediately pivots to critical virtualization management infrastructure to facilitate ransomware deployment.
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
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