Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
259 detections
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Detects the presence of specific Go build path metadata associated with the sckit Go implant, including modules and internal package structures, within an executable or file.
Detects the creation of a malicious GitHub Actions workflow file named 'runtime-update.yml', a known tactic used by the Sckit supply-chain worm. The worm injects this workflow to automate persistence and further propagation of the Go-based implant within CI/CD pipelines.
Detects a Go toolchain process (go.exe) spawning potentially suspicious child processes such as secondary 'go run' commands or Node.js execution. This behavior is indicative of a malicious Go module executing embedded or decrypted code during the build process, a tactic seen in supply chain attacks targeting development environments.
Detects the installation of machine learning or AI software dependencies (pip, conda, npm, poetry) that either pull from unauthorized registries or match known malicious/typosquatting naming patterns (e.g., LiteLLM supply-chain compromise). The rule also monitors for newly published packages that have been flagged as suspicious.
Detects the use of 'terraform init' where the command line arguments reference known typosquatted HashiCorp registry domains. This behavior is indicative of a supply chain attack attempting to pull malicious providers or modules from an adversary-controlled source.
This rule monitors application, build system, and dependency scanning logs for references to known-malicious versions of the @memtensor/memos-cloud-openclaw-plugin (npm) and MemoryOS (PyPI) packages. These versions contain the sckit Go worm and are associated with automated supply chain attacks involving the reuse of stolen credentials, indicated by the 'exact-ref-one-use-NPM_TOKEN' marker.
Detects the installation of a specific known malicious npm package named 'tw-pkgprobe-7731' via the command line, which may indicate a supply chain compromise or an attempt to execute malicious code within a development environment.
Detects instances where Microsoft Visual Studio Code (Code.exe) spawns common interpreters or command-line tools (such as node, python, cmd, or powershell) that are not associated with known internal VS Code process behaviors like renderer tasks or tunnel operations. This activity may indicate malicious use of the integrated terminal or extension execution contexts to execute unauthorized code.
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.
This rule detects Terraform CLI activity interacting with suspicious domains or modules, specifically targeting known repositories or namespaces (e.g., gocommunity.io, gogets.dev). It monitors for Terraform initialization or application patterns referencing these domains in process command lines, as well as the creation of Terraform provider files within these specific namespaces. This behavior is indicative of potential supply chain compromise involving malicious Terraform modules or dependencies.
Detects the GitHub Actions runner executing the Bun runtime to run a hidden index.js payload, specifically associated with the compromised 'actions-cool/issues-helper' or 'actions-cool/maintain-one-comment' actions. The rule filters for process execution context tied to GitHub runner internals and validates that the Bun execution follows the pattern observed in these supply chain attacks.
Detects the GitHub Actions runner executing the Bun runtime to run a hidden index.js payload, specifically associated with the compromised 'actions-cool/issues-helper' or 'actions-cool/maintain-one-comment' actions. The rule filters for process execution context tied to GitHub runner internals and validates that the Bun execution follows the pattern observed in these supply chain attacks.
Detects the GitHub Actions runner executing the Bun runtime to run a hidden index.js payload, specifically associated with the compromised 'actions-cool/issues-helper' or 'actions-cool/maintain-one-comment' actions. The rule filters for process execution context tied to GitHub runner internals and validates that the Bun execution follows the pattern observed in these supply chain attacks.
Detects unauthorized execution of a Bun runtime executing an 'index.js' payload within a GitHub Actions workflow context, specifically targeting known compromised action paths (e.g., issues-helper, maintain-one-comment). The detection requires correlation with either sensitive environment/secret access or anomalous outbound network activity originating from the same process, indicating potential credential theft or C2 communication by a malicious CI/CD pipeline component.
Detects the use of specific malicious versions (v2.2.1) of GitHub Actions (actions-cool/issues-helper or actions-cool/maintain-one-comment) in workflow runs that also involve the exposure or referencing of the GITHUB_TOKEN, indicating potential credential theft and CI/CD pipeline compromise.
Detects instances where Microsoft Visual Studio Code (Code.exe) spawns common interpreters or command-line tools (such as node, python, cmd, or powershell) that are not associated with known internal VS Code process behaviors like renderer tasks or tunnel operations. This activity may indicate malicious use of the integrated terminal or extension execution contexts to execute unauthorized code.
Detects malicious tampering with GitHub Actions workflows by monitoring for two specific patterns within an hour: the addition of automatic push triggers for the main branch alongside the removal of manual approval gates, or rapid sequential commits that rewrite workflows to facilitate an unattended publish of a package. This rule also integrates with npm registry publish events to identify potential malicious distribution of compromised packages like @dforge-core/dforge-mcp.
Detects instances where npm.exe or node.exe initiate child node.exe processes, a behavior commonly associated with multi-stage loaders or malicious packages (e.g., GHAPPIER) that execute arbitrary JavaScript payloads during package installation or execution.
Detects instances where npm processes (install, preinstall, or postinstall) spawn suspicious child processes such as shell interpreters (cmd, powershell, bash, sh), network utilities (curl, wget), or scripting interpreters (python). This behavior is often associated with malicious npm package installation or software supply chain compromise where post-install scripts are used to gain persistence or download secondary payloads.
This rule monitors for the installation of the 'indexed-btree' JavaScript package via common package managers (npm, yarn, pnpm) or its presence within a 'node_modules' directory. This specific package has been associated with supply chain attacks where malicious code is embedded within dependencies to compromise build systems or environments.
Detects anomalous activity in a CI/CD environment where a runner process accesses sensitive credential files (such as PyPI tokens or GitHub secrets) followed closely by a network connection to public package registry domains, potentially indicating a supply-chain compromise where stolen credentials are used to publish malicious packages.
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