Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
300 detections
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Detects browser extension updates that request a combination of high-risk permissions capable of account takeover (e.g., cookies, webRequest, identity) for extensions previously classified as low-risk. This pattern often indicates a supply chain compromise where a benign extension is acquired and subsequently updated with malicious capabilities.
Detects browser extension updates that request a combination of high-risk permissions capable of account takeover (e.g., cookies, webRequest, identity) for extensions previously classified as low-risk. This pattern often indicates a supply chain compromise where a benign extension is acquired and subsequently updated with malicious capabilities.
Detects potential remote code execution triggered by loading untrusted AI model files (e.g., pickle, .pt, .pth, .ckpt) in Python-based environments. The rule identifies suspicious child process execution or the presence of known dangerous deserialization/payload indicators (like os.system, subprocess, or socket) within the command line of common model-loading applications, suggesting a model-based supply chain compromise.
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 execution of package management commands (pip, npm, conda) that attempt to pull packages from registries other than the approved internal repositories. This behavior is indicative of potential AI supply-chain compromise, including malicious packages or namesquatting targeted at machine-learning or LLM-related development workflows.
This rule detects the execution of common Node.js package managers (npm, yarn, pnpm) attempting to install specific dependencies that match known patterns of malicious or typosquatted packages often used in supply chain attacks. The detection focuses on suspicious package names that mimic common data structure library naming conventions.
Detects unauthorized access to CRM platforms (Salesforce) using Salesloft or Drift OAuth tokens, where the source IP address does not match the known legitimate service provider IP ranges. This behavior is indicative of potential token theft or misuse, particularly relevant to supply-chain campaign activities.
Detects when a victim user invokes an AI agent tool that has been potentially poisoned. This is identified by either a change in the tool definition hash sourced from unverified or untrusted locations, or by an invocation that results in unexpected, undocumented, or unauthorized secondary tool calls.
This rule detects unauthorized tampering with AI container images within a container registry. It identifies when existing image tags are overwritten by actors outside of authorized CI/CD pipelines, or when production MLOps infrastructure pulls unsigned or invalid container images, potentially indicating a supply chain attack targeting AI model artifacts.
Detects instances where a Terraform process or its associated provider plugins spawn a Go toolchain process (go.exe) to execute a local package. This pattern is associated with malicious Terraform providers that use this technique to bootstrap second-stage payloads such as the Graphalgo RAT.
Detects suspicious force push events to the 'main' branch in GitHub repositories. The rule flags pushes that include hardcoded commit hashes associated with known malicious activity, pushes where the author and committer identities do not match, or pushes containing specific indicators such as 'indexe.cjs' or 'preinstall' scripts which are common in software supply chain attacks.
Detects potential supply chain attacks where a common external script (e.g., a widget) is loaded across multiple unrelated domains, followed by correlation with suspicious user-executed commands indicative of 'ClickFix' tactics (copying and pasting malicious code from a browser-based overlay into the Windows Run dialog).
This rule detects potential AI/ML supply chain compromise by monitoring for package installations (via pip, conda, poetry, etc.) from non-standard repositories, direct URLs, or version control systems within data science and CI/CD pipelines (e.g., Jupyter, Airflow, Jenkins). It further correlates this activity with subsequent suspicious outbound network connections from the host, which is indicative of slopsquatting or malicious dependency execution.
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 file removal, write, or rename operations performed by the Node.js process within the 'node_modules' directory, specifically targeting sensitive files like 'sharedLoad.min.js' or paths containing 'extended' or 'btree'. This behavior is indicative of potential malicious activity related to software supply chain compromise or unauthorized modification of dependencies within a Node.js application environment.
This rule detects instances where the Terraform CLI tool connects to suspected lookalike domains associated with supply chain compromise attempts (e.g., hashicorp-aws[.]com or hashicorp-terraform[.]io) during common initialization or application workflows. It correlates process execution with network connection logs and optionally validates the presence of a related .terraform.lock.hcl file associated with the malicious origin.
This rule detects instances where the Terraform CLI tool connects to suspected lookalike domains associated with supply chain compromise attempts (e.g., hashicorp-aws[.]com or hashicorp-terraform[.]io) during common initialization or application workflows. It correlates process execution with network connection logs and optionally validates the presence of a related .terraform.lock.hcl file associated with the malicious origin.
Detects unauthorized write or modification operations against machine learning training data, feature stores, and model registries. The rule identifies suspicious activity by flagging actions occurring outside established change windows, attempts by identities with no prior history of modifying the resource, or bulk data modifications that could indicate training data poisoning or model tampering.
Detects a single developer identity performing a high volume of git push or clone operations across a large number of distinct repositories within a short timeframe. This behavior is indicative of a compromised developer account being used to programmatically inject code into multiple repositories, potentially for a supply chain attack or mass payload propagation.
Detects a suspicious runtime pattern in GitHub Actions where a runner environment installs the Bun runtime and immediately executes an action's index.js file. This pattern is characteristic of supply chain attacks where a compromised GitHub Action uses mutable-tag pinning to smuggle and execute an obfuscated JavaScript payload before legitimate action logic.
This rule detects instances where the Terraform CLI tool connects to suspected lookalike domains associated with supply chain compromise attempts (e.g., hashicorp-aws[.]com or hashicorp-terraform[.]io) during common initialization or application workflows. It correlates process execution with network connection logs and optionally validates the presence of a related .terraform.lock.hcl file associated with the malicious origin.
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