Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
7,270 detections
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Detects the obfuscated index.js payload bundled in the compromised actions-cool/issues-helper and actions-cool/maintain-one-comment commit a0c53dd42fc842d2f9276c5a1d4f9a26abe8713d, executed via 'bun run $GITHUB_ACTION_PATH/index.js'
Detects the obfuscated index.js payload bundled in the compromised actions-cool/issues-helper and actions-cool/maintain-one-comment commit a0c53dd42fc842d2f9276c5a1d4f9a26abe8713d, executed via 'bun run $GITHUB_ACTION_PATH/index.js'
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 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 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 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 Linux processes masquerading as 'php-fpm' or 'postgres' that match known SHA-256 hashes associated with the RedTail malware family. The rule identifies processes attempting to hide their identity using techniques similar to prctl(PR_SET_NAME) to rename themselves while running malicious code.
Detects the deletion of specific files associated with the indexed-btree malware loader, such as 'sharedLoad.min.js' or files within an 'extended/' directory. This behavior indicates potential anti-forensic cleanup following malicious code execution.
Detects the GHAPPIER loader identified across 65 public npm/GitHub repositories and 22 developer accounts, based on its embedded remote-fetch/eval pattern and campaign markers
Detects the presence of a hardcoded X25519 public key associated with the 'indexed-btree' npm malware loader. This loader uses the key for ECDH key derivation to decrypt secondary payloads retrieved from an Ethereum smart contract, a technique used by malicious packages to hide their functionality.
This rule detects the installation or presence of ten identified malicious npm packages associated with the indexed-btree/sorted-btree typosquatting campaign. It monitors command-line activity for 'npm install' commands, package name resolution, and entries within 'package.json' files.
This rule monitors for suspicious Node.js process activity, specifically focusing on the loading of scripts associated with obfuscated or malicious packages (e.g., sharedLoad.min.js, indexed-btree) and the use of child_process spawn patterns that indicate detached, hidden, or unreferenced child processes, which are common tactics for bypassing detection or maintaining stealthy execution.
This rule detects command-line activity where a remote resource is downloaded via 'curl', decoded using 'base64', and then piped directly into a shell interpreter (sh, bash, or zsh). This pattern is commonly used in fileless execution or stage-one payload delivery by attackers to execute malicious scripts directly from memory.
Detects instances where AI-assisted coding tools (such as Claude, Copilot, or Gemini) initiate Git operations (clone, checkout, fetch) in a non-interactive context. This behavior may indicate an automated process using AI tools to perform code repository interactions that should typically be user-initiated or are otherwise suspicious in a non-interactive environment.
Detects Git command line executions involving 'branch', 'checkout', or 'push' operations that reference what appear to be full commit hashes as branch references. The rule flags instances where 'refname is ambiguous' warnings are generated, which may indicate an attempt to manipulate or interact with git repositories in ways that conflict with existing branches, potentially suggesting malicious repository tampering or discovery activity.
Detects a git checkout to a specific SHA followed by a git rev-parse HEAD verification command within AI coding assistant plugin directories (e.g., claude, codex, copilot, gemini). This pattern is monitored to identify potential supply chain compromises where a working tree might be silently substituted during plugin installation or updates.
Detects the creation of a Git branch where the branch name matches the pattern of a 40-character hexadecimal SHA-1 hash. This behavior is associated with the 'Plugin4Shell' technique, which exploits how some git implementations prioritize matching ref names over target commit objects, enabling an attacker to manipulate or swap code in automated systems or AI coding agents that rely on SHA-pinning.
This rule monitors for potential exploitation of AI supply chain vulnerabilities, specifically targeting AML.T0058 (Publish Poisoned Models). It identifies the download of suspicious model artifact files (e.g., .pt, .pkl, .h5, .onnx) via common tools like curl, wget, or pip, and detects the subsequent insecure deserialization of these files using Python's pickle library when dangerous opcodes (REDUCE/GLOBAL) are combined with system command execution primitives (os.system, subprocess).
Detects potential model weight poisoning or unauthorized integrity compromise by identifying training jobs launched by non-authorized users, model file modifications in production paths that bypass CI/CD approval gates, or integrity mismatches between deployed model artifacts and their corresponding approved training outputs.
Detects potential model weight poisoning or unauthorized integrity compromise by identifying training jobs launched by non-authorized users, model file modifications in production paths that bypass CI/CD approval gates, or integrity mismatches between deployed model artifacts and their corresponding approved training outputs.
Detects anomalous activity where an AI coding assistant process reads sensitive local credential files (such as .env, .aws/credentials, or SSH keys) followed by an outbound network connection to a destination not associated with known package registries or AI provider APIs. This pattern is indicative of potential prompt-injection attacks targeting autonomous AI coding tools to exfiltrate developer secrets.
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