Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
20,499 detections
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IDS Classtypes
1
Detects ClickFix-style browser or explorer spawned command execution using clipboard-paste patterns, or subsequent ransomware-related discovery and inhibit-system-recovery commands such as net.exe, nltest.exe, and vssadmin.exe used after initial access.
Detects a multi-stage Telephone-Oriented Attack Delivery (TOAD) campaign. The first stage identifies phishing emails containing billing or security-themed subjects and phone numbers without links or attachments to evade automated analysis. The second stage monitors for the subsequent installation or execution of common remote-support tools (e.g., AnyDesk, TeamViewer, ScreenConnect). A final correlative rule detects these events occurring sequentially on the same host, indicating a high-confidence indicator of a successful callback phishing social-engineering attempt.
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.
Detects anomalous, chained execution patterns initiated by AI agents integrated with Model Context Protocol (MCP) servers. The rule identifies a multi-stage sequence involving the invocation of browser automation tools followed by shell execution and subsequent outbound network activity, potentially indicating an AI-driven attack chaining filesystem, shell, or cloud-API access in an unauthorized manner.
Detects anomalous, rapid execution of a high volume of diverse offensive security tools (scanners, exploitation frameworks, and C2 agents) from a single parent process within a short window, which is characteristic of automated LLM-driven orchestration or scripted attack tool chaining.
Detects endpoint behavior consistent with AI-assisted exploit development, characterized by the execution of reverse engineering and vulnerability research tools (e.g., IDA Pro, Ghidra, AFL) interleaved with frequent outbound network connections to LLM APIs, followed by the local compilation of a new executable or DLL.
Detects anomalous, chained execution patterns initiated by AI agents integrated with Model Context Protocol (MCP) servers. The rule identifies a multi-stage sequence involving the invocation of browser automation tools followed by shell execution and subsequent outbound network activity, potentially indicating an AI-driven attack chaining filesystem, shell, or cloud-API access in an unauthorized manner.
Detects anomalous, chained execution patterns initiated by AI agents integrated with Model Context Protocol (MCP) servers. The rule identifies a multi-stage sequence involving the invocation of browser automation tools followed by shell execution and subsequent outbound network activity, potentially indicating an AI-driven attack chaining filesystem, shell, or cloud-API access in an unauthorized manner.
Detects anomalous outbound network connections initiated by security and developer tooling (Trivy, Checkmarx, LiteLLM, Telnyx). This behavior is consistent with supply-chain attacks, such as those attributed to the UNC6780/TeamPCP threat group, where compromised tooling is leveraged to exfiltrate sensitive cloud credentials or API keys to external, non-vendor infrastructure.
Detects anomalous, rapid execution of a high volume of diverse offensive security tools (scanners, exploitation frameworks, and C2 agents) from a single parent process within a short window, which is characteristic of automated LLM-driven orchestration or scripted attack tool chaining.
Detects anomalous process activity that reads sensitive developer secrets (such as Anthropic, OpenAI, Gemini, or Hugging Face API keys) from common configuration or environment files, followed by an outbound network connection to a non-private IP address. This behavior is consistent with credential harvesting and exfiltration tactics used in supply chain attacks to hijack AI services.
Detects anomalous activity from IDEs or AI coding assistant extension host processes (e.g., VS Code, Cursor, JetBrains). The rule alerts when these processes spawn children that access sensitive local credential files, perform network connections to non-standard/unexpected domains, or modify critical CI/CD build script configuration files, which is indicative of a supply chain compromise within the developer environment.
Detects anomalous child process spawning from common web browsers or productivity applications (e.g., Office, Acrobat) on hosts that have recently communicated with known LLM or code-generation APIs. This behavioral correlation is intended to identify the potential execution of AI-generated exploit code.
Detects the execution of common remote monitoring and management (RMM) software on a host shortly after the associated user account underwent a password or MFA reset, a pattern indicative of help-desk impersonation (vishing) followed by unauthorized remote access setup.
Detects a sequence of suspicious activities on a Windows host aimed at inhibiting system recovery, such as deleting shadow copies, modifying boot recovery configurations, deleting backup catalogs, or stopping security and backup-related services. This rule aggregates distinct categories of these actions by DeviceId and Account to identify potential malicious intent characteristic of ransomware or data destructive attacks.
Detects instances where AI-assisted development tools (such as Claude, Codex, Copilot, or Gemini) initiate command-line processes (e.g., shells, interpreters, or network utilities) with arguments indicative of credential access, reconnaissance, or sensitive file interactions.
Detects an exploitation chain originating from a Chrome browser process, characterized by the execution of an anomalous child process followed by the creation of an executable file named 'chrome_cleanup.exe' within a short time window. This sequence is indicative of multi-stage exploitation, involving remote code execution via browser vulnerability and subsequent privilege escalation.
Detects a suspicious sequence where the 'chrome_cleanup.exe' binary is written to disk, its Mark of the Web (MOTW) identifier is removed, and it is subsequently launched by an unusual process (e.g., explorer, svchost, dllhost, or rundll32) without command-line arguments. This behavior is indicative of a COM-based shell launch, a technique often used by post-exploitation tools to bypass security controls and execute binaries stealthily.
Detects the creation of a process in a suspended state using the CREATE_SUSPENDED flag. This technique is commonly employed during process hollowing, process injection, or by legitimate security/debugging tools to prepare a process for memory modification before execution begins.
Detects Sauron Loader stage-2 payload execution: a randomly-named file dropped in %TEMP% and executed within 5 minutes by a native launcher (rundll32/regsvr32/msiexec/cmd/powershell/wscript). Scoped to devices that already show the confirmed rnpkeys.exe side-load from ProgramData\keyroll, turning a very noisy fleet-wide 'temp file executed by native binary' heuristic (matches countless legitimate installers/updaters) into a targeted next-stage check on hosts with corroborated Sauron Loader activity. Also fixes an unused/dead variable and an ambiguous post-join column reference.
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