Browse Rules

Web Server Potential SQL Injection Request

This rule detects potential SQL injection attempts in web server requests by identifying common SQL injection patterns in URLs. Such activity may indicate reconnaissance or exploitation attempts by attackers trying to manipulate backend databases or extract sensitive information.

Web Server Local File Inclusion Activity

This rule detects potential Local File Inclusion (LFI) activity on web servers by identifying HTTP GET requests that attempt to access sensitive local files through directory traversal techniques or known file paths. Attackers may exploit LFI vulnerabilities to read sensitive files, gain system information, or further compromise the server.

Web Server Potential Remote File Inclusion Activity

This rule detects potential Remote File Inclusion (RFI) activity on web servers by identifying HTTP GET requests that attempt to access sensitive remote files through directory traversal techniques or known file paths. Attackers may exploit RFI vulnerabilities to read sensitive files, gain system information, or further compromise the server.

Web Server Suspicious User Agent Requests

This rule detects unusual spikes in web server requests with uncommon or suspicious user-agent strings. Such activity may indicate reconnaissance attempts by attackers trying to identify vulnerabilities in web applications or servers. These user-agents are often associated with automated tools used for scanning, vulnerability assessment, or brute-force attacks.

Web Server Cloud Metadata SSRF Request

Detects HTTP requests to web servers whose URL or query string references cloud instance metadata endpoints or equivalent encoded variants. Attackers exploit server-side request forgery (SSRF) vulnerabilities in web applications to reach link-local metadata services on AWS, GCP, Azure, and similar cloud providers and harvest temporary credentials, tokens, or instance details.

Web Server Discovery or Fuzzing Activity

This rule detects potential web server discovery or fuzzing activity by identifying a high volume of HTTP GET requests resulting in 404 or 403 status codes from a single source IP address within a short timeframe. Such patterns may indicate that an attacker is attempting to discover hidden or unlinked resources on a web server, which can be a precursor to more targeted attacks.

Web Server Potential Spike in Error Response Codes

This rule detects unusual spikes in error response codes (500, 502, 503, 504) from web servers, which may indicate reconnaissance activities such as vulnerability scanning or fuzzing attempts by adversaries. These activities often generate a high volume of error responses as they probe for weaknesses in web applications. Error response codes may potentially indicate server-side issues that could be exploited.

Potential Spike in Web Server Error Logs

This rule detects unusual spikes in error logs from web servers, which may indicate reconnaissance activities such as vulnerability scanning or fuzzing attempts by adversaries. These activities often generate a high volume of error responses as they probe for weaknesses in web applications. Error response codes may potentially indicate server-side issues that could be exploited.

Web Server Potential Command Injection Request

This rule detects potential command injection attempts via web server requests by identifying URLs that contain suspicious patterns commonly associated with command execution payloads. Attackers may exploit vulnerabilities in web applications to inject and execute arbitrary commands on the server, often using interpreters like Python, Perl, Ruby, PHP, or shell commands. By monitoring for these indicators in web traffic, security teams can identify and respond to potential threats early.

Potential HTTP Downgrade Attack

Through the new_terms rule type, this rule detects potential HTTP downgrade attacks by identifying HTTP traffic that uses a different HTTP version than the one typically used in the environment. An HTTP downgrade attack occurs when an attacker forces a connection via an older HTTP version, resulting in potentially less secure communication. For example, an attacker might downgrade a connection from HTTP/2 to HTTP/1.1 or HTTP/1.0 to exploit known vulnerabilities or weaknesses in the older protoc

AWS Lambda Event Source Mapping Creation

Identifies the creation of an AWS Lambda event source mapping, which connects an event source such as an Amazon SQS queue, an Amazon Kinesis or DynamoDB stream, an Amazon MSK or self-managed Apache Kafka topic, or an Amazon MQ broker to a Lambda function so the function is automatically invoked when new records arrive. Adversaries with "lambda:CreateEventSourceMapping" permissions can abuse this to establish stealthy, event-driven persistence and execution, or to continuously siphon records from

Potential Webshell Deployed via Apache Struts CVE-2023-50164 Exploitation

Identifies successful exploitation of CVE-2023-50164, a critical path traversal vulnerability in Apache Struts 2 file upload functionality. This high-fidelity rule detects a specific attack sequence where a malicious multipart/form-data POST request with WebKitFormBoundary is made to a Struts .action upload endpoint, immediately followed by the creation of a JSP web shell file by a Java process in Tomcat's webapps directories. This correlated activity indicates active exploitation resulting in r

Vulnerable Machines related to log4j CVE-2021-44228

'This query uses the Azure Defender Security Nested Recommendations data to find machines vulnerable to log4j CVE-2021-44228. Log4j is an open-source Apache logging library that is used in many Java-based applications. Security Nested Recommendations data is sent to Microsoft Sentinel using the continuous export feature of Azure Defender(refrence link below). Reference: https://msrc-blog.microsoft.com/2021/12/11/microsofts-response-to-cve-2021-44228-apache-log4j2/ Reference: https://docs.micro

Azure AKS Secret get or list with Suspicious User Agent

Detects successful AKS (Azure Kubernetes Service) secret get or list operations where the user agent matches scripting runtimes (python, ruby, perl), command-line HTTP clients (curl, wget, HTTPie), or generic HTTP libraries (Go-http-client, okhttp, Apache-HttpClient, Guzzle, axios, undici) rather than typical kubectl or named controller traffic. Reading Kubernetes secrets with a generic client is a common credential-access step after a token or kubeconfig is stolen, and offensive tooling (for ex

User agent search for log4j exploitation attempt

'This query uses various log sources having user agent data to look for log4j CVE-2021-44228 exploitation attempt based on user agent pattern. Log4j is an open-source Apache logging library that is used in many Java-based applications. The regex and the string matching look for the most common attacks. This might not be comprehensive to detect every possible user agent variation. Reference: https://msrc-blog.microsoft.com/2021/12/11/microsofts-response-to-cve-2021-44228-apache-log4j2/'

Thrift RPC Method from an External Client

Identifies the first decoded Apache Thrift RPC relationship from a public client address to a server. Thrift commonly connects trusted internal microservices and data platforms, and an externally originated method invocation can indicate an exposed service, unauthorized access, or exploitation of a public-facing Thrift endpoint.

Suspicious Java Classes

The following analytic identifies suspicious Java classes often used for remote command execution exploits in Java frameworks like Apache Struts. It detects this activity by analyzing HTTP POST requests with specific content patterns using Splunk's `stream_http` data source. This behavior is significant because it may indicate an attempt to exploit vulnerabilities in web applications, potentially leading to unauthorized remote code execution. If confirmed malicious, this activity could allow att