Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
2,132 detections
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Detects unauthenticated guest users abusing the Salesforce Experience Cloud Aura endpoint (/s/sfsites/aura) to perform high-volume metadata and schema enumeration. This activity typically involves tools like AuraInspector to harvest CRM data by repeatedly invoking Aura actions such as describeSObjects, getSObjectMetadata, ObjectInfoController, or RecordUiController.
Detects high-frequency anonymous comment submissions to 'wp-comments-post.php' from a single IP address within a 10-minute window, characterized by minimal variations in User-Agent. This behavior is indicative of an automated script attempting to bypass comment moderation settings, potentially exploiting known vulnerabilities (e.g., CVE-2026-93485) to inject content without administrative approval.
This rule detects potential prompt injection attacks against internal AI-powered assistants, agents, or LLM gateways. It monitors network HTTP requests for common patterns used to override system instructions, bypass safety filters, or force the model to adopt a privileged, unauthorized persona (e.g., administrator or debug mode). These techniques are often used in social engineering to manipulate the AI's output or security posture.
The following analytic detects potential exploitation of the CrushFTP authentication bypass vulnerability (CVE-2025-31161). This detection identifies suspicious command execution patterns associated with exploitation of this vulnerability, such as executing mesch.exe with specific arguments like b64exec or fullinstall. This activity is indicative of an attacker exploiting CVE-2025-31161 to gain unauthorized access to the CrushFTP server and perform post-exploitation activities.
The following analytic detects Log4Shell JNDI payload injections via outbound connections. It identifies suspicious LDAP lookup functions in web logs, such as `${jndi:ldap://PAYLOAD_INJECTED}`, and correlates them with network traffic to known malicious IP addresses. This detection leverages the Web and Network_Traffic data models in Splunk. Monitoring this activity is crucial as it targets vulnerabilities in Java web applications using log4j, potentially leading to remote code execution. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary code, and compromise sensitive data within the affected environment.
This rule detects reconnaissance activity targeting Azure App Service instances, specifically looking for attempts to access web-shell-like files, WordPress administration paths, PHP-CGI endpoints, or LangFlow code validation paths. It identifies potential Storm-3168 actor infrastructure through known IOCs or through patterns of high-volume, path-diverse HTTP requests that deviate from typical noise, helping to distinguish targeted probing from common internet background scanning.
Detects HTTP requests containing malicious JDBC strings targeting PaperCut NG/MF print management software, which are indicative of attempts to exploit authentication bypass and remote code execution vulnerabilities (CVE-2026-81578, CVE-2026-82078).
Detects network activity (TCP, DNS, TLS, HTTP) associated with the UNC6240 threat actor's infrastructure, specifically targeting communications with known malicious IP addresses and domains used in a campaign leveraging PeopleSoft-related lures.
Detects systematic and high-frequency probing of internet-exposed LLM chatbot or API gateway endpoints. The rule monitors for sequences of requests, use of known prompt-infiltration or jailbreak testing toolkits, and varied path access indicative of automated reconnaissance or adversarial prompt engineering attempts.
Detects the execution of suspicious child processes (e.g., cmd.exe, powershell.exe, bash, wget) spawned by processes associated with public-facing appliances like Fortinet SSL-VPN, Veeam Backup & Replication, Citrix ADC, and cPanel/WHM. This behavior is often indicative of exploitation of public-facing applications (T1190) for initial access, frequently associated with ransomware actors targeting unpatched infrastructure.
Detects the execution of suspicious child processes (e.g., cmd.exe, powershell.exe, bash, wget) spawned by processes associated with public-facing appliances like Fortinet SSL-VPN, Veeam Backup & Replication, Citrix ADC, and cPanel/WHM. This behavior is often indicative of exploitation of public-facing applications (T1190) for initial access, frequently associated with ransomware actors targeting unpatched infrastructure.
Detects a multi-stage attack pattern against publicly exposed AI service endpoints. The detection correlates: (1) Enumeration of sensitive AI/MCP-related API endpoints (minimum 3 distinct hits) in a 10-minute window; (2) Subsequent POST requests to AI service endpoints within 15 minutes of the enumeration; and (3) A spike of 5 or more server errors (HTTP 5xx) within 10 minutes following the POST activity. This pattern suggests automated reconnaissance followed by attempted exploitation of exposed AI model interfaces or orchestration services. Covers T1595.002, T1190
Detects rapid, machine-speed, sequential attack-path adaptation by suspected AI agent binaries. The rule monitors for a correlation of four stages occurring within a tight 20-minute window: 1) Failed service connections across three or more distinct protocols (SMB, SSH, HTTP, RDP, WinRM), 2) Credential access attempts (Logon events or execution of known credential-dumping tools), and 3) A lateral movement pivot to a new, previously un-targeted internal destination. The rule specifically keys on the InitiatingProcessId and InitiatingProcessCreationTime to ensure the sequence is attributed to a single process instance, distinguishing it from human manual penetration testing activity. Covers T1190, T1210, T1078
This rule identifies Linux-based devices that are vulnerable to specific security flaws tracked under CVE-2026-64507 and CVE-2026-64508. It correlates vulnerability assessment data with device inventory to highlight exposed systems.
Detects attempts to exploit CVE-2021-33044, an authentication bypass vulnerability in Dahua NetKeyboard hardware. The rule triggers when a POST request containing 'global.login', 'clientType: NetKeyboard', and 'loginType: Direct' is observed, which allows unauthorized access by manipulating authentication parameters.
Detects network traffic attempting to exploit the Dahua CVE-2021-33044 vulnerability, which allows an unauthenticated user to bypass authentication mechanisms on affected NetKeyboard devices.
Detects instances where the Adobe ColdFusion service (jrun.exe or java.exe) spawns common command-line shells, administrative tools, or utility processes. This behavior is indicative of potential exploitation of ColdFusion vulnerabilities, such as CVE-2023-26360 or CVE-2023-29298, where an adversary gains initial access and executes commands on the underlying system.
Detects malicious HTTP POST requests targeting SharePoint servers that attempt to inject 'Register' directives into pages or manipulate WebPartPage ToolPane markup, indicative of attempts to bypass SafeControls or exploit vulnerable SharePoint features for code execution.
Detects a specific SharePoint markup injection technique targeting the EditingPageParser type-check mechanism. An attacker smuggles a 'Register' directive into a crafted WebPartMarkup POST request using the 'publishingribbon' Tagprefix to bypass SafeControl validation.
Detects suspicious activities associated with ysoserial.net gadget chain exploitation targeting SharePoint's w3wp.exe process. The rule correlates the loading of specific .NET assemblies (PresentationFramework, System.Xaml, System.Data.Services) with the subsequent execution of command shells (cmd, powershell) or the presence of common ysoserial.net-related strings (ActivitySurrogateSelector, LosFormatter) within command lines originating from the w3wp.exe process, indicating a potential web shell.
Detects malicious prompt injection payloads targeting AI agent frameworks, specifically designed to bypass Semantic Kernel's filter blocklist. The payload utilizes Python reflection mechanisms by crawling the type hierarchy (starting from a tuple) to locate and invoke 'BuiltinImporter.load_module()', effectively bypassing filters that look for direct references to forbidden modules or functions like '__import__', 'eval', or 'os'. This allows for indirect arbitrary command execution within the agent's application context.
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