Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects evidence of the SCKit supply chain worm within GitHub Actions CI pipelines. The rule identifies attempts to poison the build process by injecting a BASH_ENV path into GITHUB_ENV, which forces the execution of a malicious script (sckit-publish-bridge.sh) during subsequent shell steps. This script is designed to exfiltrate npm publishing credentials (NODE_AUTH_TOKEN) to external infrastructure. The detection logic covers both the injection mechanism and diagnostic output indicators from the bridge script.
Detects anomalous high-volume API requests to machine learning model inference endpoints (e.g., predict, classify, score). This behavior is characteristic of adversarial example crafting, where an attacker sends rapid, near-duplicate requests to estimate gradients or probe confidence scores to manipulate model output.
This rule detects unauthorized or anomalous file modifications or creations to machine learning training datasets (e.g., fine-tuning data, training corpora) by users not belonging to the authorized data engineering group, occurring within one hour of a scheduled training or fine-tuning job execution. This behavioral pattern is indicative of potential data poisoning, where an adversary attempts to inject malicious data into the training set to bias or compromise the resulting model.
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.
This rule monitors package installation logs for indicators of packages potentially generated or suggested by AI tools (e.g., Copilot, code assistants) being installed in a target environment. It specifically looks for a low volume of installations (<=3) for packages that have been published within the last 14 days, which is a pattern often associated with the 'Publish Hallucinated Entities' technique in AI systems, where malicious or hallucinated code packages are introduced into the supply chain.
Detects the execution of rclone.exe or renamed binaries with command-line flags indicative of data exfiltration to cloud storage providers such as S3, Mega, OneDrive, or Google Drive. This behavior is frequently associated with ransomware actors, including Akira, Qilin, and Storm-2570, who use Rclone to perform mass exfiltration of sensitive file share data prior to encryption (double extortion).
Detects a multi-stage extortion tactic where a host is targeted by a mass creation of initial decryption-style ransom notes across multiple directories, followed by a subsequent drop of distinct follow-up 'press release' or 'threat' notes within a 30-minute window, indicative of INC Ransom behavior.
Detects AI agent tooling (e.g., Copilot, Cursor, Ollama) that makes outbound network calls to known AI model providers followed by the execution of suspicious shell commands within a short duration. The rule flags high-severity activities such as download-cradles, credential reconnaissance, LOLBin usage, and persistence mechanisms. Informational alerts are generated for standard CLI activity within the same timeframe.
This rule identifies Linux-based devices that are vulnerable to specific security flaws tracked under CVE-2026-64507 and CVE-2026-64508. It correlates vulnerability assessment data with device inventory to highlight exposed systems.
This rule identifies Linux-based devices that are vulnerable to specific security flaws tracked under CVE-2026-64507 and CVE-2026-64508. It correlates vulnerability assessment data with device inventory to highlight exposed systems.
Detects post-exploitation activity against Citrix NetScaler appliances associated with CVE-2026-8452. The rule specifically monitors for HTTP GET requests targeting PHP files within the /var/vpn/theme/ directory, accompanied by efforts to execute shell commands (bin/sh) via the web server.
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 outbound network connections originating from managed devices where the remote URL or domain matches known underground LLM-based services, such as WormGPT, FraudGPT, or EvilGPT, which are often used by threat actors for malicious activities like generating phishing content or malware.
This rule detects network connections from internal devices to a specific remote IP address (134.122.155.135) over port 443. This is characteristic of communication with a known or suspicious Command and Control (C2) server.
Detects instances where a command-line utility or scripting interpreter references a 'SKILL.md' configuration file, typically associated with agent-skill payloads, followed shortly by network-based file download activity. This behavior is indicative of a secondary payload retrieval triggered by an adversary-controlled or poisoned configuration file.
Detects the presence of package.json or node_modules artifacts associated with a known typosquatting campaign targeting the 'sorted-btree' npm package. The rule identifies suspicious package names or dependencies by matching against a list of known malicious packages used in the campaign, or by detecting the co-occurrence of the malicious 'indexed-btree' package alongside the legitimate 'sorted-btree' package.
Detects unauthenticated GET requests to known AI and machine learning infrastructure management endpoints (e.g., DCGM, Ray, Kubeflow) that are commonly exposed without authentication. These interfaces provide operational metrics, cluster status, or job management details, which can be leveraged for reconnaissance.
Detects high-volume SSH authentication attempts originating from a single source IP targeting a server on port 22, indicating a potential brute force attack.
Detects outbound network traffic matching Stratum protocol JSON-RPC methods (mining.subscribe, mining.authorize, mining.submit) typically used by XMRig and other cryptomining software to communicate with a mining pool. The rule monitors for established TCP connections on non-standard ports, excluding common web ports 80 and 443.
Detects the execution of Python 3.12 commands via 'sudo' to elevate privileges to 'root'. The rule monitors for instances where a non-root account invokes 'sudo python3.12' and subsequently spawns a process or logs activity confirming a root-level execution session within a short time window.
Detects the Sckit supply chain implant reading sensitive credential files from a user's home directory. The rule monitors processes spawned by Node.js or Python interpreters (the primary loaders for the Sckit worm) attempting to access high-value files like SSH keys, .npmrc, .pypirc, and various token storage files, indicating potential credential harvesting behavior.
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