Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
714 detections
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Detects suspicious cross-process memory operations where a source process requests process creation/write permissions on a target process (e.g., browsers, explorer.exe, or svchost.exe), followed immediately by the execution of a remote thread injection technique such as CreateRemoteThread, QueueUserAPC, or NtCreateThreadEx within the same process pair.
Detects the execution of the Microsoft HTML Application host (mshta.exe) when it is used to launch scripts from remote URLs (http/https/ftp) or when it spawns common command-line shells and scripting engines, which is a common indicator of living-off-the-land techniques used to execute malicious payloads.
Detects the UAT-11587 TestAssembly.dll downloader by its shared AssemblyAttribute GUID b2b3adb0-1669-4b94-86cb-6dd682ddbea3 embedded in .NET metadata across all campaign builds
Detects the execution of the Graphalgo RAT second-stage payload. The rule identifies the spawning of a detached 'go run .' process, which performs an ephemeral key exchange using a hardcoded public key before C2 establishment. It also correlates this activity with parent processes common in software build environments (npm, terraform, go) to differentiate malicious staging from developer activity.
Detects potential SaaS supply-chain attacks where a third-party application or connector triggers an OAuth permission scope escalation or publishes a new version, followed by the associated service account initiating outbound network connections to rare or newly-registered domains within a 24-hour window.
Detects command-line execution patterns that exhibit hallmark traits of LLM-generated code, such as overly verbose and explanatory comments, appearing alongside common obfuscation or decoding primitives (e.g., base64, iex, eval). This combination suggests the use of AI to assist in creating decoy logic or burying malicious payloads within legitimate-appearing scripts to evade detection.
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 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 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 a specific pattern of Windows API calls consistent with PIVOTPIPE C2 activity, involving process token impersonation (OpenProcessToken, DuplicateTokenEx, ImpersonateLoggedOnUser, or SetThreadToken) followed by a RevertToSelf call. The rule correlates these events within a single process, identifies artifacts related to PIVOTPIPE loader/payloads, and ensures the sequence completes within a short time window.
Detects modifications to the .zshrc configuration file followed by shell commands interacting with the file, or the execution of commands that source the .repair-run script within a shell environment. This pattern is indicative of potential persistence mechanisms where an adversary modifies startup scripts to maintain access or execute malicious code upon shell initialization.
This rule detects the execution of common remote access tools (Quick Assist, AnyDesk) on a host that does not have a prior history of using those specific tools. This is intended to identify potential hands-on-keyboard activity by adversaries following initial access via social engineering, such as vishing or email-bombing, where they attempt to establish a persistent remote access foothold.
This rule detects Terraform CLI execution of the 'init' command correlated with network access to HashiCorp AWS registry domains, or the presence of Terraform lock files in specific sensitive or project-related directory paths. This combination often indicates infrastructure-as-code management activities or potential misuse of Terraform in a production environment.
Detects anomalous, high-frequency access to clipboard APIs (GetClipboardData/SetClipboardData) by non-standard, unrecognized processes. The rule specifically monitors for patterns consistent with clipper malware, which polls the clipboard for cryptocurrency address formats and replaces them with attacker-controlled addresses.
Detects process command lines containing LLM tool-calling syntax (e.g., 'tool_call', 'function_call') in conjunction with keywords associated with offensive security capabilities (e.g., 'exploit', 'mimikatz', 'exfiltrate'). This pattern is consistent with the behavior of malicious LLM-based agents attempting to autonomously invoke and execute offensive tasks.
Detects process command lines containing LLM tool-calling syntax (e.g., 'tool_call', 'function_call') in conjunction with keywords associated with offensive security capabilities (e.g., 'exploit', 'mimikatz', 'exfiltrate'). This pattern is consistent with the behavior of malicious LLM-based agents attempting to autonomously invoke and execute offensive tasks.
Detects process command lines containing LLM tool-calling syntax (e.g., 'tool_call', 'function_call') in conjunction with keywords associated with offensive security capabilities (e.g., 'exploit', 'mimikatz', 'exfiltrate'). This pattern is consistent with the behavior of malicious LLM-based agents attempting to autonomously invoke and execute offensive tasks.
Detects instances where a Claude process accesses a 'SKILL.md' file, followed closely by a suspicious command execution on the same device. The rule specifically targets command-line activity that involves encoding, download-and-execute patterns, staging in sensitive directories, or communication with suspicious domains/IPs, which is indicative of a supply-chain or poisoned agent-skill file attack.
Detects instances where a Claude process accesses a 'SKILL.md' file, followed closely by a suspicious command execution on the same device. The rule specifically targets command-line activity that involves encoding, download-and-execute patterns, staging in sensitive directories, or communication with suspicious domains/IPs, which is indicative of a supply-chain or poisoned agent-skill file attack.
Detects command-line execution patterns that exhibit hallmark traits of LLM-generated code, such as overly verbose and explanatory comments, appearing alongside common obfuscation or decoding primitives (e.g., base64, iex, eval). This combination suggests the use of AI to assist in creating decoy logic or burying malicious payloads within legitimate-appearing scripts to evade detection.
Detects command-line execution patterns that exhibit hallmark traits of LLM-generated code, such as overly verbose and explanatory comments, appearing alongside common obfuscation or decoding primitives (e.g., base64, iex, eval). This combination suggests the use of AI to assist in creating decoy logic or burying malicious payloads within legitimate-appearing scripts to evade detection.
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