Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
295 detections
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Detects the execution of specific Python scripts associated with the FortiBleed campaign. These scripts include 'spray_da.py' for domain admin password spraying, 'smb_test.py' for SMB validation and lateral movement, 'spider.py' for Active Directory crawling, and 'ad_full_audit.py' for Active Directory auditing. Detection is based on the Python interpreter executing these specific script filenames.
Detects the use of Node.js or Python with inline execution flags (-e or -c) and a long command line, a technique used by DEV#POPPER stagers to run obfuscated code. The rule specifically looks for keywords like 'base64', 'urlopen', 'exec', and 'import' in the command line, which are indicative of malicious script execution.
Detects the Python Imaging Library (PIL) memory leak used to bypass ASLR prior to the JPEG2000 heap overflow. This KQL query specifically looks for syslog messages containing 'cannot identify image file' and 'BytesIO object at 0x', which are indicators of this specific memory leak vulnerability being triggered.
Detects Python or a Cython module opening Chromium Login Data and Local State files outside browser execution. This activity is indicative of credential theft, specifically targeting web browser stored credentials.
Detects the installation of known malicious PyPI packages ('frint', 'skytext', 'slogsec', 'logcrypt.cryptography') associated with the ChocoPoC supply chain campaign. The rule monitors process execution commands related to 'pip' package installation and tracks the appearance of malicious compiled extension files ('gradient.pyd', 'gradient.so') on the filesystem.
Detects the decoded execution of bash/sh reverse shells initiated from processes like node.exe or python.exe, which is indicative of the Void Dokkaebi threat actor's tactics. The rule specifically looks for command lines containing 'echo' with base64 encoded strings, followed by 'base64 -d' and then 'sh' or 'bash' for execution.
Detects malicious Python or JavaScript package archives (such as those used in dependency confusion attacks) that contain obfuscated code execution logic (base64 encoded commands) within package installation hooks (setup.py), or those embedding suspicious binary executables (PE/ELF headers) within the package structure.
Detects Python scripts or processes accessing common browser credential and cookie storage files (such as Login Data, key4.db, or cookies.sqlite) and subsequently making outbound network connections, indicating potential theft and exfiltration of browser data.
Detects the spawning of suspicious command-line interfaces or utility processes (e.g., cmd.exe, powershell.exe, curl.exe) by an AutoGen Studio Python process. This pattern is indicative of potential exploitation attempts where an adversary leverages AutoGen Studio's capabilities to achieve arbitrary code execution on the underlying host.
Detects potential Remote Code Execution (RCE) in an AutoGen Studio environment by correlating an unexpected outbound network connection from the AutoGen process to an external endpoint, followed by the immediate spawning of a command interpreter (shell) child process containing common command-line interface indicators (e.g., whoami, curl, wget, encoded PowerShell).
Detects unauthorized inbound WebSocket connections from external (non-private) IP addresses to the AutoGen Studio MCP API endpoint at /api/mcp/ws/ on port 8081, followed by the execution of interpreter processes (autogenstudio, uvicorn, or python) on the same host, indicating a potential exploitation of an origin validation bypass for remote code execution.
Detects a potential OS command injection attack targeting AutoGen Studio. The rule correlates a suspicious WebSocket connection to the Model Context Protocol (MCP) endpoint on localhost:8081, specifically containing the 'server_params' parameter, with a subsequent shell process spawning from an AutoGen Studio, Python, or Uvicorn parent process. The shell command line includes common injection characters (e.g., ;, |, &&, ||) and OS enumeration commands (e.g., whoami, id, net user).
Detects the macOS.Gaslight embedded Python collection module executing sequential reconnaissance functions: collect_system_info(), collect_running_processes(), collect_applications(), and collect_terminal_history() via ps aux and system_profiler, consistent with DPRK-aligned macOS.Gaslight stealer activity.
Detects Python executing adconnectdump.py, which targets credentials stored by Microsoft Entra Connect. This tool is used to dump credentials from Microsoft Entra Connect (formerly Azure AD Connect) installations.
Detects the execution of Python scripts that include libraries (pynput, pyxhook) or functions (GetAsyncKeyState, WH_KEYBOARD_LL) frequently used by keyloggers to capture user keystrokes. It also monitors for suspicious input device access and command-line arguments indicative of keystroke monitoring.
Detects the macOS.Gaslight Python collection module compressing staged data into a ZIP archive (collected_data.zip) using shutil.make_archive for subsequent exfiltration over Telegram C2. The module calls compress_output() which archives browser credentials, keychain, terminal history, and process snapshots into temp/collected_data.zip.
Detects attempts to access SSH private keys (id_rsa, id_ed25519) or public keys (.pub files) by 'node' or 'python' processes. This activity could indicate an attacker attempting to collect SSH credentials for lateral movement or persistence.
Detects pip installing the benign-looking termncolor package together with colorinal. This dependency relationship was used to conceal delivery of platform-specific ZiChatBot droppers, as observed in campaigns attributed to OceanLotus (APT32). The rule specifically looks for command-line executions of pip or python with 'pip install' and the presence of 'termncolor', 'colorinal', or 'uuid32-utils' in the command line.
Detects Python-based command and control activity associated with LegionLoader by monitoring for network connections, DNS queries to specific malicious domains, and file modifications involving '_socket.pyd' within the 'Traiolx Custom Utils' directory path.
Detects behavior indicative of the InvisibleFerret information stealer, specifically focusing on Python execution spawned by development tools (VS Code/Node.js), unauthorized access to browser-based credential stores or local crypto wallets, and suspicious outbound network activity originating from Python processes spawned by development/automation tools.
Detects instances where common script interpreters (node.exe, python.exe, etc.) simultaneously access sensitive local credential files and establish outbound network connections to common web ports (80, 443, 8080, etc.), which is indicative of credential exfiltration behavior.
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