Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
295 detections
Filters
Last updated
All Time
Detection languages
116
41
34
33
20
Contributors
106
38
28
23
20
Categories
165
157
111
46
39
Platforms
229
188
103
9
9
Products / Services
10,660
9,755
6,689
4,418
3,748
295
MITRE Techniques
172
112
60
51
43
CVEs
6
6
3
2
2
IDS Classtypes
4
IDS Protocols
4
Detects the scripted, non-human-paced registration of a rogue external authentication method by monitoring a sequence of specific Azure Audit Log operations (Add application, Add service principal, Add delegated permission grant) occurring within 60 seconds of each other by the same actor. The detection is further corroborated by analyzing Microsoft Graph Activity Logs for associated Python requests to specific Graph API URIs.
Detects the scripted, non-human-paced registration of a rogue external authentication method by monitoring a sequence of specific Azure Audit Log operations (Add application, Add service principal, Add delegated permission grant) occurring within 60 seconds of each other by the same actor. The detection is further corroborated by analyzing Microsoft Graph Activity Logs for associated Python requests to specific Graph API URIs.
Detects potential exploitation of CVE-2025-3248 in Langflow by identifying suspicious inbound network traffic targeting the /api/v1/validate/code endpoint, correlated with subsequent python child processes containing base64 decoding and execution primitives such as eval, exec, or subprocess.
This rule detects command line patterns associated with common Impacket tools (wmiexec, psexec, smbexec) executed via Windows processes (cmd.exe, powershell.exe) or via network connections to ports 445 and 135 initiated by Python processes. These tools are frequently used by adversaries for lateral movement, remote service execution, and command execution on compromised systems.
This rule monitors for potential exploitation of AI supply chain vulnerabilities, specifically targeting AML.T0058 (Publish Poisoned Models). It identifies the download of suspicious model artifact files (e.g., .pt, .pkl, .h5, .onnx) via common tools like curl, wget, or pip, and detects the subsequent insecure deserialization of these files using Python's pickle library when dangerous opcodes (REDUCE/GLOBAL) are combined with system command execution primitives (os.system, subprocess).
Detects Python processes (python.exe, python3.exe, pythonw.exe) performing DNS resolution for Microsoft identity, device login, or Graph API endpoints. This activity is consistent with automated tooling, such as python-requests, performing device-code flow polling or API token authentication within an environment.
This rule monitors for two distinct stages of potential account recovery abuse. First, it detects the execution of suspected Python scripts on an endpoint that contain command-line arguments related to password recovery or authentication codes. Second, it monitors network proxy/web logs for a high volume of API requests to the 'easy4ipcloud.com' domain associated with password reset or device probe endpoints, which may indicate automated credential stuffing or unauthorized device account recovery attempts.
Detects potential reflective code loading or memory injection attempts within Python interpreter processes (pythonw.exe, ipy.exe). The rule monitors for processes that lack a valid module path or file path on disk while referencing known indicators of malicious memory-based loading, such as 'hostfxr.dll' or specific suspicious strings like 'DLLMemLoader'. This technique is often used to execute payloads directly in memory to evade file-based security controls.
Detects file artifacts referencing InvisibleFerret, a Python-based backdoor used by WaterPlum/North Korean IT worker actors for persistent access to victim networks
Detects file artifacts referencing InvisibleFerret, a Python-based backdoor used by WaterPlum/North Korean IT worker actors for persistent access to victim networks
Detects file artifacts referencing InvisibleFerret, a Python-based backdoor used by WaterPlum/North Korean IT worker actors for persistent access to victim networks
Detects file artifacts referencing InvisibleFerret, a Python-based backdoor used by WaterPlum/North Korean IT worker actors for persistent access to victim networks
Detects suspicious Python execution patterns commonly associated with web shells or remote code execution, originating from typical web server or application middleware processes (e.g., w3wp, nginx, httpd, tomcat). The detection triggers on specific patterns including base64-encoded command execution, Python evaluation of base64-encoded strings, or long alphanumeric command-line strings suggesting obfuscated code execution.
Detects Python scripts that exhibit behavioral characteristics of agentic or LLM-generated malicious code. This is identified by the presence of self-narrating comments describing intent, next steps, or staging/exfiltration actions, combined with hardcoded indicators for the JADEPUFFER staging infrastructure.
Detects the presence of natural-language self-narration in code comments within Python or SQL scripts. This pattern is indicative of LLM-driven agentic execution, where automated agents narrate their own malicious actions, such as enumeration, data exfiltration, or database manipulation, as observed in the JADEPUFFER campaign.
Detects instances where AI-assisted coding tools or command-line interfaces spawn scripting runtimes (Node.js, Python) to access sensitive files such as SSH keys, cloud provider credentials, or Kubernetes configurations. This behavior may indicate an AI coding assistant being coerced or misconfigured to exfiltrate secrets from the development environment.
Detects the scripted, non-human-paced registration of a rogue external authentication method by monitoring a sequence of specific Azure Audit Log operations (Add application, Add service principal, Add delegated permission grant) occurring within 60 seconds of each other by the same actor. The detection is further corroborated by analyzing Microsoft Graph Activity Logs for associated Python requests to specific Graph API URIs.
Detects DNS resolution requests for graph.microsoft.com initiated by a python interpreter (python.exe, python3.exe, or pythonw.exe). This activity may indicate a script or custom tool communicating with Microsoft Graph API, which is frequently used for data collection, exfiltration, or cloud service interaction in adversarial campaigns.
Detects instances where a Langflow process spawns a Python process using command-line arguments involving base64 decoding or character-based execution, which is often indicative of malicious code injection or shellcode execution.
Detects Python scripts containing natural-language comments that narrate attack rationale, reasoning, or step-by-step intent. This behavior is a diagnostic signature of scripts generated or operated by AI agents, which tend to document their own processes in plain text comments, unlike human-written malicious code.
Detects potential exploitation of CVE-2025-3248 in Langflow by identifying suspicious inbound network traffic targeting the /api/v1/validate/code endpoint, correlated with subsequent python child processes containing base64 decoding and execution primitives such as eval, exec, or subprocess.
Page 3 of 15