Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
7,143 detections
Filters
Last updated
All Time
Detection languages
3,220
1,086
504
478
470
Contributors
1,824
1,607
871
505
300
Categories
2,972
2,473
1,061
1,025
907
Platforms
39,770
7,143
6,517
3,850
3,602
Products / Services
1,324
1,069
971
657
488
MITRE Techniques
2,231
1,317
1,313
874
871
CVEs
66
65
33
32
28
IDS Classtypes
81
80
56
27
10
IDS Protocols
149
98
6
5
4
Detects browser search traffic being hijacked to the typosquatted domain 'perplexity-ai.online' by the malicious Chromium extension 'Search for perplexity ai'. The rule monitors for network connections to the malicious domain and the presence of extension-related file artifacts on the system. The extension utilizes chrome_settings_overrides to redirect browser Omnibox queries and exfiltrate user search activity to attacker-controlled infrastructure.
Detects the execution of known network exploitation tools like Responder.py or MultiRelay.py, as well as unauthorized processes binding to ports used for LLMNR (UDP 5355) and NBT-NS (UDP 137), which are classic indicators of potential adversary-in-the-middle attacks.
Detects post-compromise persistence techniques, including crontab modifications, SSH authorized_keys updates, and systemd service creation, when performed by child processes of AI agentic tools like Claude, Node.js, or Python. This activity suggests malicious persistence establishment following an indirect prompt injection or supply-chain compromise of the AI agent environment.
Detects AI agent processes (e.g., claude, node) executing environment enumeration commands (env, printenv) following suspected prompt injection, combined with subsequent network traffic associated with credential exfiltration patterns.
Detects instances where a shell interpreter (bash or sh) accesses sensitive cloud and environment configuration files, specifically when initiated by parent processes associated with IDE-integrated development tools or AI assistants (claude, cursor, copilot-cli). This behavior suggests potential unauthorized access to credentials or environment variables.
Detects MCP (Model Context Protocol) server processes or developer tools spawned by IDE extensions performing environment variable enumeration to access cloud credentials (e.g., AWS, Azure, GCP, GitHub tokens) followed by anomalous outbound network connections, indicating potential exfiltration of sensitive developer environment secrets.
Detects the use of the 'docker cp' command with suspicious path traversal characters or known PoC artifacts in the destination parameter. This behavior is indicative of an attempt to exploit path traversal vulnerabilities in the Docker daemon (such as those allowing container escape) by writing files outside the container's intended scope onto the host filesystem.
This rule detects the execution of python.exe or python3.exe using the '-m init' command line flag while attempting to establish a network connection over specific, often non-standard, ports (4444, 1337, 9001, 9002, 5555, 6666, 7777, 8888). This pattern is commonly associated with reverse shells or C2 agent check-ins initiated via Python scripts.
Detects the creation of unrecognized Go-compiled binaries by common developer tools (e.g., node.exe, code.exe) in user-writable or temporary directories, followed shortly by outbound network connections to known C2 infrastructure (e.g., vercel.app, jsonkeeper.com). This behavior is consistent with the 'Contagious Interview' campaign attributed to the Lazarus Group.
Detects evidence of exploitation attempts related to the hypothetical CVE-2026-55200 vulnerability. The rule monitors for known exploit file names, command-line arguments, and specific network patterns targeting SSH ports (22, 2222) that indicate potential LibSSH2 exploitation.
Detects exploitation attempts against the Flowise Model Context Protocol (MCP) environment, specifically targeting the node_options case-sensitivity bypass (CVE-2026-20182). The rule monitors for malicious node child process spawning, command line injection via the --require flag, suspicious file creation (flowise_marker.txt), and unauthorized outbound network connections from the Flowise process.
Detects indicators of the DirtyClone (CVE-2026-43503) Linux kernel local privilege escalation exploit. The rule identifies the execution or compilation of the PoC binary, the specific su invocation used for shell spawning by the exploit, and subsequent post-exploitation actions including modifying sensitive files (/etc/passwd, /etc/sudoers) and creating new local accounts with sudo privileges.
Detects high-frequency, sequential authentication failures across multiple internal targets (SMB/LDAP) initiated from a single internal IP address. This behavior is consistent with credential spraying or lateral movement reconnaissance tools like NetExec or similar automation scripts, often originating from compromised internal assets such as virtual machines.
Detects a highly relevant Antivirus alert that reports a remote access tool.
This event must not be ignored just because the AV has blocked the malware but investigate, how it came there in the first place.
This event must not be ignored just because the AV has blocked the malware but investigate, how it came there in the first place.
This rule detects command-line activity and process execution associated with the exploitation of various Linux kernel vulnerabilities, including CVE-2026-43503, CVE-2026-46300, CVE-2026-43284, CVE-2026-31431, and CVE-2026-43500. It monitors for the compilation and execution of exploit code, manipulation of kernel modules via modprobe for specific network protocols (esp4, esp6, rxrpc), configuration changes to network namespaces (xfrm, unshare), and the presence of known exploit filenames like DirtyClone or DirtyFrag.
Detects suspicious command-line patterns on Linux systems commonly associated with post-exploitation activity, including the establishment of reverse shells, encoded payload delivery via DNS, and manual modifications to crontab or SSH authorized_keys for persistence.
Detects remote code execution exploitation targeting CVE-2026-20253 in Splunk. The rule monitors for unauthorized child processes, such as shells or scripting interpreters, being spawned by the Splunk daemon (splunkd). It also flags processes containing exploit-related strings in the command line as a secondary indicator.
Detects high-volume outbound network connections (exceeding 10MB) originating from processes associated with AI-driven chatbot or productivity applications (e.g., ChatGPT, Perplexity). This rule identifies potential data exfiltration occurring over legitimate, expected software channels.
Detects the execution of Node.js where the command line arguments reference a .woff2 font file. This behavior is indicative of an adversary attempting to execute a malicious payload disguised as a legitimate font file, a technique previously associated with the PolinRider campaign.
Detects network traffic attempting to modify SSH authorized_keys or the sshd_config file using SCP or SFTP protocols, which is a common technique used by attackers to maintain persistence on compromised systems.
Detects the AWS CLI being spawned by processes associated with AI coding extensions (e.g., VS Code extension hosts, Cursor, Windsurf) while executing commands indicative of cloud credential discovery or harvesting (e.g., sts, get-caller-identity, configure export). This activity is indicative of a supply chain compromise or exploitation where an adversary leverages an AI coding tool environment to access cloud credentials stored on the developer's workstation.
Page 162 of 341