Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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The following analytic detects suspicious attempts to find credentials stored in password stores, indicating a potential attacker's effort to access sensitive login information. Password stores are critical repositories that contain valuable credentials, and unauthorized access to them can lead to significant security breaches. By monitoring for unusual or unauthorized activities related to password store access, this analytic helps identify potential credential theft attempts, allowing security teams to respond promptly and prevent unauthorized access to critical systems and data.
The following analytic detects the creation of SSH key files in the ~/.ssh/ directory. It leverages filesystem data to identify new files in this specific path. This activity is significant because threat actors often create SSH keys to gain persistent access and escalate privileges on a compromised host. If confirmed malicious, this could allow attackers to remotely access the machine using the OpenSSH daemon service, leading to potential unauthorized control and data exfiltration.
Detects the creation of nsswitch.conf files in non-standard directories, which may indicate exploitation of CVE-2025-32463. This vulnerability requires an attacker to create a nsswitch.conf in a directory that will be used during sudo chroot operations. When sudo executes, it loads malicious shared libraries from user-controlled locations within the chroot environment, potentially leading to arbitrary code execution and privilege escalation.
The following analytic detects the deletion of SSH keys or configuration files on a Linux machine. It leverages filesystem event logs to identify when files within "/etc/ssh/*" or "~/.ssh/*" are deleted. This activity is significant because attackers may delete or modify SSH keys to evade security measures or as part of a destructive payload, similar to the AcidRain malware. If confirmed malicious, this behavior could lead to impaired security features, hindered forensic investigations, or further unauthorized access, necessitating immediate investigation to identify the responsible process and user.
This detection identifies file creation events associated with the installation of the Medusa rootkit, a userland LD_PRELOAD-based rootkit known for deploying shared objects, loader binaries, and configuration files into specific system directories. These files typically facilitate process hiding, credential theft, and backdoor access. Monitoring for such file creation patterns enables early detection of rootkit deployment before full compromise.
Detects the execution of suspicious commands often used in Command and Control (C2) activities, as identified in Florian Roth's 'Best Practice Auditd Configuration'. These commands include `wget`, `curl`, `base64`, `nc`, `netcat`, `ncat`, `ssh`, `socat`, `wireshark`, `rawshark`, `rdesktop`, and `nmap`. These tools can be leveraged for various adversarial techniques such as data transfer, remote access, network discovery, and data encoding.
The following analytic detects suspicious attempts to find SSH private keys, which may indicate an attacker's effort to compromise secure access to systems. SSH private keys are essential for secure authentication, and unauthorized access to these keys can enable attackers to gain unauthorized access to servers and other critical infrastructure. By monitoring for unusual or unauthorized searches for SSH private keys, this analytic helps identify potential threats to network security, allowing security teams to quickly respond and safeguard against unauthorized access and potential breaches.
Detects the removal or modification of system firewall rules using common Linux firewall management tools like iptables, firewall-cmd, ufw, and nft. This rule specifically looks for commands that delete or remove existing rules, which adversaries might use to bypass network controls and enable C2 communications, lateral movement, or data exfiltration. This detection aims to catch more granular firewall modifications than just a complete firewall disablement.
The following analytic detects the modification of SSH Authorized Keys on Linux systems. It leverages process execution data from Endpoint Detection and Response (EDR) agents, specifically monitoring commands like "bash" and "cat" interacting with "authorized_keys" files. This activity is significant as adversaries often modify SSH Authorized Keys to establish persistent access to compromised endpoints. If confirmed malicious, this behavior could allow attackers to maintain unauthorized access, bypassing traditional authentication mechanisms and potentially leading to further exploitation or data exfiltration.
The following analytic identifies potential exploitation of the WordPress Bricks Builder plugin RCE vulnerability. It detects HTTP POST requests to the URL path "/wp-json/bricks/v1/render_element" with a status code of 200, leveraging the Web datamodel. This activity is significant as it indicates an attempt to exploit CVE-2024-25600, a known vulnerability that allows remote code execution. If confirmed malicious, an attacker could execute arbitrary commands on the target server, leading to potential full system compromise and unauthorized access to sensitive data.
Detects abnormal network activity and connectivity issues in Ollama including non-localhost API access attempts and warning-level network errors such as DNS lookup failures, TCP connection issues, or host resolution problems that may indicate network-based attacks, unauthorized access attempts, or infrastructure reconnaissance activity.
Detects potential prompt injection or jailbreak attempts against Ollama API endpoints by identifying requests with abnormally long response times. Attackers often craft complex, layered prompts designed to bypass AI safety controls, which typically result in extended processing times as the model attempts to parse and respond to these malicious inputs. This detection monitors /api/generate and /api/chat endpoints for requests exceeding 30 seconds, which may indicate sophisticated jailbreak techniques, multi-stage prompt injections, or attempts to extract sensitive information from the model.
This YARA rule detects the BPFDoor backdoor, a sophisticated Linux-based malware. It identifies specific byte patterns (strings) associated with different versions of BPFDoor's packet loop, reverse shell functionality, and process name manipulation. The rule looks for two main versions of these components, ensuring a comprehensive detection of the malware's core functionalities.
This analytic detects instances where web or application server processes (such as Java, Tomcat, Apache, Nginx, Node, Caddy, IIS, PHP) spawn a Linux or Windows shell. This activity is highly indicative of successful exploitation, potentially related to vulnerabilities like Log4Shell (CVE-2021-44228) or Spring4Shell (CVE-2022-22965), leading to unauthorized command execution, privilege escalation, or persistent access. The detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on parent-child process relationships and process names.
Detects Ollama server errors and failures during model loading operations that may indicate malicious model injection, path traversal attempts, or exploitation of model loading mechanisms to achieve remote code execution. Adversaries may attempt to load specially crafted malicious models or exploit vulnerabilities in the model loading process to execute arbitrary code on the server. This detection monitors error messages and failure patterns that could signal attempts to abuse model loading functionality for malicious purposes.
The analytic detects instances where Java or Tomcat processes spawn a Linux shell. This activity may indicate exploitation attempts, such as those related to CVE-2021-44228 (Log4Shell). This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process names and parent-child process relationships. This activity is significant as it can signify a compromised Java application, potentially leading to unauthorized shell access. If confirmed malicious, attackers could execute arbitrary commands, escalate privileges, or maintain persistent access, posing a severe threat to the environment.
KQL Query from file: New Redis RCE Alert: CVE-2025-49844
Detects the execution of Python web servers via command line interface (CLI).
After gaining access to target systems, adversaries may use Python's built-in HTTP server modules to quickly establish a web server without requiring additional software.
This technique is commonly used in post-exploitation scenarios as it provides a simple method for transferring files between the compromised host and attacker-controlled systems.
After gaining access to target systems, adversaries may use Python's built-in HTTP server modules to quickly establish a web server without requiring additional software.
This technique is commonly used in post-exploitation scenarios as it provides a simple method for transferring files between the compromised host and attacker-controlled systems.
Detects critical service crashes, fatal errors, and abnormal process terminations in Ollama that may indicate exploitation attempts, resource exhaustion attacks, malicious input triggering unhandled exceptions, or deliberate denial of service attacks designed to disrupt AI model availability and degrade system stability. The rule aggregates multiple termination events within a 5-minute window to identify sustained instability or attack patterns.
Detects data leakage and exfiltration attempts targeting Ollama model metadata and configuration endpoints. Adversaries repeatedly query /api/show, /api/tags, and /api/v1/models to systematically extract sensitive model information including architecture details, fine-tuning parameters, system paths, Modelfile configurations, and proprietary customizations. Multiple inspection attempts within a 15-minute window indicate automated exfiltration of valuable intellectual property such as custom model configurations, system prompts, and internal model specifications. This activity represents unauthorized data disclosure that could enable competitive intelligence gathering, model replication, or preparation for advanced attacks against the AI infrastructure.
Detects abnormal memory allocation patterns and excessive runner operations in Ollama that may indicate resource exhaustion attacks, memory abuse through malicious model loading, or attempts to degrade system performance by overwhelming GPU/CPU resources. Adversaries may deliberately load multiple large models, trigger repeated model initialization cycles, or exploit memory allocation mechanisms to exhaust available system resources, causing denial of service conditions or degrading performance for legitimate users.
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