Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
3,640 detections
Filters
Last updated
All Time
Detection languages
1,764
375
349
273
255
Contributors
908
661
593
359
164
Categories
1,498
1,162
780
529
520
Platforms
39,950
7,275
6,526
3,865
3,640
Products / Services
812
512
432
342
298
MITRE Techniques
1,184
574
524
506
443
CVEs
17
16
9
8
8
IDS Classtypes
41
15
10
6
5
IDS Protocols
47
24
3
2
1
This rule detects the installation or execution of known malicious, typosquatted npm packages associated with the SANDWORM_MODE campaign. It identifies process creation events related to common Node.js package managers (npm, yarn, npx) and checks for command lines indicating package installation or execution, specifically looking for the presence of malicious package names from the SANDWORM_MODE campaign.
Detects DKnife text markers and config file names referenced by Cisco Talos (dkimsi tag, default IMSI, credential tag, and common DKnife config artefact names).
Hunts for unwarranted hardware additions through USB device connections in macOS Unified Logs. In an environment where USB devices are not permitted.
In the absence of system.log, which used to show USB entries in previous macOS versions, this rule attempts to watch for this activity.
In the absence of system.log, which used to show USB entries in previous macOS versions, this rule attempts to watch for this activity.
This rule detects outbound network connections from devices to IP addresses identified as malicious by a threat intelligence feed. It joins device network events with threat intelligence indicators, specifically looking for successful connections or connection requests to IPs with a confidence score greater than 50. The alert provides details about the device, the remote IP, the initiating process, and the threat intelligence context.
Detects suspicious modification of AI assistant configuration files potentially related to MCP server injection.
This rule detects the presence of hardcoded tokens associated with the dYdX supply chain attack. These tokens were used for C2 authentication and as an API key for data exfiltration. The rule searches across process command lines, network URLs, network headers, and common security logs for these specific tokens.
This rule detects the execution of multiple common network discovery commands within a short time frame. This behavior can indicate reconnaissance activities aimed at understanding network topology, identifying active hosts, and discovering network services.
This rule detects network connections or DNS queries to a list of domains identified as phishing campaign infrastructure. These domains are associated with a 'Signal Phishing Campaign' and are likely used for malicious purposes such as credential harvesting or malware distribution. The rule checks for these domains in `RemoteUrl` or `DestinationHost` fields across various network-related logs.
Detects the execution of osascript to display a deceptive dialog box mimicking 'System Preferences' to harvest user passwords. This technique was observed being used by the Atomic Stealer (AMOS) malware.
This rule detects the presence of environment variables used to control the malicious SANDWORM_MODE payload in CI environments or on local systems. The presence of these variables in a process command line is a strong indicator of an active infection or a workflow being manipulated by the malware's control logic.
This detection identifies network connections from non-browser processes to known Large Language Model (LLM) API endpoints. While some connections may be legitimate developer or application traffic, unexpected processes connecting to these services could indicate unauthorized data exfiltration, corporate espionage, or misuse of AI services.
This detection identifies suspicious command-line pipelines commonly used in "ClickFix" attacks. It specifically searches for the use of 'curl' or 'wget' to download a payload that is immediately executed via a shell, often hidden within a subshell `$(...)` or piped directly to an interpreter. This "download and execute" pattern is a high-fidelity indicator of a successful ClickFix lure, where a user has been tricked into running a malicious command.
This detection identifies a command-line pattern characteristic of "ClickFix" attacks on macOS. It looks for a shell process (like zsh, bash, sh) that is spawned by a known terminal application and executes a command involving echoing a string, decoding it with 'base64 -D', and piping it into another shell for execution. This technique is used by adversaries to trick users into running malicious code, bypassing standard OS protections.
Detects modification of the VSCode settings.json file by a process other than VSCode itself. This could indicate an attempt to exploit vulnerabilities where an attacker tricks a user into applying a malicious configuration to achieve remote code execution.
Detects potential command injection attacks against AI agents through
suspicious prompt patterns or system command execution by AI processes.
suspicious prompt patterns or system command execution by AI processes.
This rule detects attempts to use outdated or weak cryptographic algorithms and protocols by monitoring command-line arguments of common network and cryptographic utilities. Specifically, it looks for the use of RC4, DES, 3DES, RSA1024, MD5 ciphers, or TLS 1.0, TLS 1.1, and SSL 3.0 protocols, as well as the '--insecure' flag, when executed by applications like openssl, gpg, ssh, or curl. This could indicate an adversary attempting to downgrade cryptographic protections to facilitate eavesdropping or other attacks.
Detects the execution of a command pattern commonly associated with "ClickFix" social engineering attacks. This technique tricks users into pasting a command into their terminal, which typically uses 'echo' to pass a base64 encoded string to the 'base64' utility for decoding, and then pipes the output to a shell for execution. This method is used to bypass traditional OS-level protections like Gatekeeper and Notarization.
This rule detects the creation or modification of specific files associated with the SHub v2.0 infostealer on macOS. It looks for a fake 'GoogleUpdate' binary within the '~/Library/Application Support/Google/' directory or a LaunchAgent plist file named 'com.google.keystone.agent.plist' in the 'LaunchAgents/' directory. These file paths are indicative of the SHub stealer's persistence mechanism, which impersonates legitimate Google update components.
This rule detects suspicious Python command-line executions that involve importing the 'requests' library, using 'exec()' for dynamic code execution, and making HTTP/HTTPS requests. This pattern is often observed in malicious Python scripts used for command and control, data exfiltration, or downloading additional payloads.
Detects malicious activity related to the TCC.db, commonly abused by macOS malware
to bypass Transparency, Consent, and Control (TCC) restrictions.
to bypass Transparency, Consent, and Control (TCC) restrictions.
This rule detects Python scripts that use base64 decoding and execution, specifically looking for the 'import base64' statement appearing early in the file, followed by 'exec(base64.b64decode(' and ').decode('utf-8'))'. This pattern is indicative of a common method for obfuscating and executing malicious Python payloads, often seen in malware like KongTuke.
Page 157 of 174