Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects installation of known malicious npm packages associated with the PhantomSub campaign, which abuse WhatsApp spam channels. It also identifies network connections to known remote C2 channel-list URLs or domains used by these packages to automate channel joining.
This rule detects installation of known malicious npm packages associated with the PhantomSub campaign, which abuse WhatsApp spam channels. It also identifies network connections to known remote C2 channel-list URLs or domains used by these packages to automate channel joining.
This rule detects installation of known malicious npm packages associated with the PhantomSub campaign, which abuse WhatsApp spam channels. It also identifies network connections to known remote C2 channel-list URLs or domains used by these packages to automate channel joining.
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 outbound HTTP GET requests for common AI model file formats (.pkl, .pt, .bin) from non-authorized or untrusted external domains. This activity is indicative of downloading potentially malicious pre-trained AI models, which can execute arbitrary code during the unpickling process (Supply Chain Compromise).
Detects the execution of a specific, tampered version (12.5.1) of the DAEMON Tools Lite installer, which has been identified as a distribution vector for malicious activity linked to Storm-3069.
Detects the use of package installation commands (pip, npm, npx) where the package name resembles popular AI/ML libraries (tensorflow, torch, openai, langchain) but is not an exact match for known legitimate versions. This is a common indicator of a typosquatting supply chain attack, where attackers attempt to trick users into installing malicious packages with similar names to trusted software.
Detects activity associated with the MALFEX npm supply-chain campaign (also known as Overlord/movinlike). This rule performs a sweep for known malicious file hashes, suspicious process command lines, outbound network connections to malicious domains or IPs, and email communications from identified adversary-controlled accounts.
Detects the installation or execution of known MALFEX malicious packages via npm or Node.js, which are used as entry points for polyglot or disguised payload delivery.
Detects the installation or execution of known MALFEX malicious packages via npm or Node.js, which are used as entry points for polyglot or disguised payload delivery.
Detects an abnormally high volume of POST requests to the RubyGems API endpoint used for publishing packages. A sudden burst of these requests from a single source may indicate a mass upload campaign, commonly associated with software supply chain poisoning attempts.
Detects HTTP traffic attempting to download specific known malicious RubyGems packages from rubygems.org associated with the GemStuffer campaign. This indicates a supply chain compromise attempt where attackers distribute malicious code via public package repositories.
This rule detects the installation of common AI agent and orchestration frameworks (e.g., LangChain, AutoGen, CrewAI) using command-line package managers (pip, npm, conda) that point to non-standard or unverified package indexes. This behavior is a common indicator of a potential software supply-chain compromise, where an adversary attempts to inject malicious code by forcing the installation of packages from an attacker-controlled source.
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 the use of package installation commands (pip, npm, npx) where the package name resembles popular AI/ML libraries (tensorflow, torch, openai, langchain) but is not an exact match for known legitimate versions. This is a common indicator of a typosquatting supply chain attack, where attackers attempt to trick users into installing malicious packages with similar names to trusted software.
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
This rule detects scenarios where a node-based package manager (npm or npx) executes an 'install' command, which is immediately followed by the spawning of unexpected child processes such as interpreters (python, powershell, cmd, wscript, cscript, mshta) or network tools (curl). This behavior is characteristic of malicious npm packages used in supply chain attacks to deliver secondary payloads like BeaverTail, InvisibleFerret, or similar threats.
Detects the creation of potential hook or startup scripts within hidden AI coding assistant directories (.claude, .vscode, .cursor) followed by the execution of a process from those same directories. This pattern is consistent with supply chain compromises where trojanized extensions or MCP servers are leveraged for code execution.
Detects anomalous GitHub audit log activity indicative of a potential supply-chain compromise. The rule identifies workflows or automated entities (Bot/App) that access sensitive Action secrets followed by the publishing of packages to public registries like PyPI or npm. This activity can represent the precursor steps of token-theft-driven malicious package injection.
Detects the spawning of suspicious child processes (powershell, cmd, node, wscript, cscript) by dependency management utilities (npm, go, terraform). Attackers often leverage malicious packages or configurations during the build or install phase to execute arbitrary code. The rule also monitors for suspicious command-line execution patterns, such as detached 'go run' operations, which may indicate malicious activity.
Detects the use of package installation commands (pip, npm, npx) where the package name resembles popular AI/ML libraries (tensorflow, torch, openai, langchain) but is not an exact match for known legitimate versions. This is a common indicator of a typosquatting supply chain attack, where attackers attempt to trick users into installing malicious packages with similar names to trusted software.
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