Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
295 detections
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Detects the creation of cook.pth or cook.py in Python site-packages or dist-packages directories by package managers or interpreters, followed by a Python interpreter execution on the same device within one hour. This pattern matches the malicious supply chain behavior observed in compromised LiteLLM versions (e.g., 1.107.0) utilizing .pth auto-import chains for persistence and execution.
Detects the execution of the public Horizon3.ai PoC exploit for CVE-2026-106173 (FortiSIEM gRPC path traversal RCE) utilizing python interpreters with specific command-line arguments indicative of the exploit tool.
Detects the creation of cook.pth or cook.py in Python site-packages or dist-packages directories by package managers or interpreters, followed by a Python interpreter execution on the same device within one hour. This pattern matches the malicious supply chain behavior observed in compromised LiteLLM versions (e.g., 1.107.0) utilizing .pth auto-import chains for persistence and execution.
Detects the creation of cook.pth or cook.py in Python site-packages or dist-packages directories by package managers or interpreters, followed by a Python interpreter execution on the same device within one hour. This pattern matches the malicious supply chain behavior observed in compromised LiteLLM versions (e.g., 1.107.0) utilizing .pth auto-import chains for persistence and execution.
Detects the creation or modification of Python configuration files (.pth, sitecustomize.py, usercustomize.py) within site-packages or dist-packages directories when not initiated by recognized Python package managers. Such files can be abused to achieve arbitrary code execution upon Python interpreter startup, a common method for persistence or local privilege escalation in Python environments.
Detects an exploitation attempt against PoeLLM or LiteLLM MCP test/connection endpoint using a python3 stdio payload that fetches data from port 81.
Detects an exploitation attempt against PoeLLM or LiteLLM MCP test/connection endpoint using a python3 stdio payload that fetches data from port 81.
Detects an exploitation attempt against PoeLLM or LiteLLM MCP test/connection endpoint using a python3 stdio payload that fetches data from port 81.
Detects the execution of Python-based tunneling tools located in the 'C:\Users\Public\indigo\' directory, which is a known staging location for malicious implants used in covert tunneling operations.
Detects runtime processes associated with AI agents or scripting environments (e.g., python, node, deno) accessing sensitive credential storage paths and files, such as SSH keys, cloud provider configuration directories, and local environment variables, which may indicate credential exfiltration.
Detects an exploitation attempt against PoeLLM or LiteLLM MCP test/connection endpoint using a python3 stdio payload that fetches data from port 81.
Detects instances where AI agent runtime environments, such as Python or Node.js processes, spawn command interpreters to execute commands that suggest reconnaissance or unauthorized system manipulation. This behavior is indicative of a hijacked AI agent abusing its tool-use capabilities to execute commands outside of an approved workflow.
This analytic detects suspicious python package installations where the package name resembles popular Python libraries but may be typosquatted or slightly altered.
Typosquatting is a common technique used by attackers to trick users into installing malicious packages that mimic legitimate ones.
This detection leverages Cisco NVM flow telemetry and checks for pip or poetry package managers with the "install" or "add" flags, making outbound connections to package repository such as `pypi.org` with known or suspected typo package names.
Typosquatting is a common technique used by attackers to trick users into installing malicious packages that mimic legitimate ones.
This detection leverages Cisco NVM flow telemetry and checks for pip or poetry package managers with the "install" or "add" flags, making outbound connections to package repository such as `pypi.org` with known or suspected typo package names.
Detects attempts to redirect Python package resolution away from the default PyPI index. This is achieved by monitoring pip CLI arguments (--index-url, --extra-index-url), environment variables (PIP_INDEX_URL, PIP_EXTRA_INDEX_URL, PIP_CONFIG_FILE), or the creation/modification of pip configuration files (pip.conf, pip.ini). Such activity is often associated with software supply chain attacks where an adversary attempts to force the installation of malicious packages from a controlled repository.
Detects instances where a Python process spawns a command-line shell (cmd.exe, powershell.exe, or bash) within 15 seconds of starting, while executing common reconnaissance or utility commands. This behavior is indicative of malicious code executing at import-time, a technique often associated with supply chain compromises in Python packages.
Detects the execution of known Python scripts (CES_Enroll.py, ntlm_ces_relay.py, ces_negotiate_ntlm.py) often associated with NTLM relay attacks or forced authentication techniques. These scripts leverage Python to interact with authentication protocols or relay requests.
This rule detects suspicious process lineage where a Python interpreter executes system commands indicative of a compromised or trojanized pandas package. Specifically, it identifies Python processes spawning command-line utilities for malicious shell operations (e.g., cmd.exe /c calc.exe on Windows or uname -a on Linux/macOS), suggesting an attempt to verify successful arbitrary code execution via a hijacked library function like read_json.
Detects the execution of known Python scripts (CES_Enroll.py, ntlm_ces_relay.py, ces_negotiate_ntlm.py) often associated with NTLM relay attacks or forced authentication techniques. These scripts leverage Python to interact with authentication protocols or relay requests.
Detects instances where Node.js, Python, or Go processes spawn command shell interpreters (cmd.exe, powershell.exe, etc.). This behavior is often indicative of C2 command execution by malware, such as the Graphalgo RAT, which may use these languages to execute shell-level commands after initial infection or payload execution.
Detects the execution of the sckit Go implant, linked to the MemTensor supply chain compromise, during its host-profiling stage. The rule monitors for the 'sckit' binary being executed with 'stage0' and '--config64' command-line arguments, or being spawned by legitimate runtime interpreters like Node.js or Python, indicating an attempt to profile host information (timezone, language, privileges) and subsequent self-deletion.
Detects anomalous process execution originating from a Python-based sandbox (such as Semantic Kernel SessionsPythonPlugin in Azure Container Apps) that attempts to break out of the container isolation. The rule monitors for command-line arguments involving interaction with the Docker socket, sensitive host namespaces, or container privilege escalation techniques, indicative of exploitation attempts related to CVE-2026-25592.
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