EXPLORE DETECTIONS
Cisco TFTP Server Configuration for Data Exfiltration
This analytic detects the configuration of TFTP services on Cisco IOS devices that could be used to exfiltrate sensitive configuration files. Threat actors like Static Tundra have been observed configuring TFTP servers to make device configuration files accessible for exfiltration after gaining initial access. The detection specifically looks for commands that expose critical configuration files such as startup-config, running-config, and other sensitive system information through TFTP. This activity is particularly concerning as it may represent an attempt to steal credentials, network topology information, and other sensitive data stored in device configurations.
Citrix ADC and Gateway CitrixBleed 2 Memory Disclosure
This detection identifies potential exploitation attempts of CVE-2025-5777 (CitrixBleed 2), a memory disclosure vulnerability in Citrix NetScaler ADC and Gateway. The vulnerability is triggered by sending POST requests with incomplete form data to the /p/u/doAuthentication.do endpoint, causing the device to leak memory contents including session tokens and authentication materials. This search looks for POST requests to the vulnerable endpoint that may indicate scanning or exploitation attempts.
Citrix ADC and Gateway Unauthorized Data Disclosure
The following analytic detects attempts to exploit the Citrix Bleed vulnerability (CVE-2023-4966), which can lead to the leaking of session tokens. It identifies HTTP requests with a 200 status code targeting the /oauth/idp/.well-known/openid-configuration URL endpoint. By parsing web traffic and filtering based on user agent details, HTTP method, source and destination IPs, and sourcetype, it aims to identify potentially malicious requests. This activity is significant for a SOC because successful exploitation can allow attackers to impersonate legitimate users, bypass authentication, and access sensitive data. If confirmed malicious, it could lead to unauthorized data access, network propagation, and critical information exfiltration.
Citrix ADC Exploitation CVE-2023-3519
The following analytic identifies potential exploitation attempts against Citrix ADC related to CVE-2023-3519. It detects POST requests to specific web endpoints associated with this vulnerability by leveraging the Web datamodel. This activity is significant as CVE-2023-3519 involves a SAML processing overflow issue that can lead to memory corruption, posing a high risk. If confirmed malicious, attackers could exploit this to execute arbitrary code, escalate privileges, or disrupt services, making it crucial for SOC analysts to monitor and investigate these alerts promptly.
Citrix ShareFile Exploitation CVE-2023-24489
The following analytic detects potentially malicious file upload attempts to Citrix ShareFile via specific suspicious URLs and the HTTP POST method. It leverages the Web datamodel to identify URL patterns such as "/documentum/upload.aspx?parentid=", "/documentum/upload.aspx?filename=", and "/documentum/upload.aspx?uploadId=*", combined with the HTTP POST method. This activity is significant for a SOC as it may indicate an attempt to upload harmful scripts or content, potentially compromising the Documentum application. If confirmed malicious, this could lead to unauthorized access, data breaches, and operational disruptions.
Clear Unallocated Sector Using Cipher App
The following analytic detects the execution of `cipher.exe` with the `/w` flag to clear unallocated sectors on a disk. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line arguments, and parent processes. This activity is significant because it is a technique used by ransomware to prevent forensic recovery of deleted files. If confirmed malicious, this action could hinder incident response efforts by making it impossible to recover critical data, thereby complicating the investigation and remediation process.
Clop Common Exec Parameter
The following analytic identifies the execution of CLOP ransomware variants using specific arguments ("runrun" or "temp.dat") to trigger their malicious activities. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. Monitoring this activity is crucial as it indicates potential ransomware behavior, which can lead to file encryption on network shares or local machines. If confirmed malicious, this activity could result in significant data loss and operational disruption due to encrypted files, highlighting the need for immediate investigation and response.
Clop Ransomware Known Service Name
The following analytic identifies the creation of a service with a known name used by CLOP ransomware for persistence and high-privilege code execution. It detects this activity by monitoring Windows Event Logs (EventCode 7045) for specific service names ("SecurityCenterIBM", "WinCheckDRVs"). This activity is significant because the creation of such services is a common tactic used by ransomware to maintain control over infected systems. If confirmed malicious, this could allow attackers to execute code with elevated privileges, maintain persistence, and potentially disrupt or encrypt critical data.
Cloud API Calls From Previously Unseen User Roles
The following analytic detects cloud API calls executed by user roles that have not previously run these commands. It leverages the Change data model in Splunk to identify commands executed by users with the user_type of AssumedRole and a status of success. This activity is significant because new commands from different user roles can indicate potential malicious activity or unauthorized actions. If confirmed malicious, this behavior could lead to unauthorized access, data breaches, or other damaging outcomes by exploiting new or unmonitored commands within the cloud environment.
Cloud Compute Instance Created By Previously Unseen User
The following analytic identifies the creation of cloud compute instances by users who have not previously created them. It leverages data from the Change data model, focusing on 'create' actions by users, and cross-references with a baseline of known user activities. This activity is significant as it may indicate unauthorized access or misuse of cloud resources by new or compromised accounts. If confirmed malicious, attackers could deploy unauthorized compute instances, leading to potential data exfiltration, increased costs, or further exploitation within the cloud environment.
Cloud Compute Instance Created In Previously Unused Region
The following analytic detects the creation of a cloud compute instance in a region that has not been previously used within the last hour. It leverages cloud infrastructure logs and compares the regions of newly created instances against a lookup file of historically used regions. This activity is significant because the creation of instances in new regions can indicate unauthorized or suspicious activity, such as an attacker attempting to evade detection or establish a foothold in a less monitored area. If confirmed malicious, this could lead to unauthorized resource usage, data exfiltration, or further compromise of the cloud environment.
Cloud Compute Instance Created With Previously Unseen Image
The following analytic detects the creation of cloud compute instances using previously unseen image IDs. It leverages cloud infrastructure logs to identify new image IDs that have not been observed before. This activity is significant because it may indicate unauthorized or suspicious activity, such as the deployment of malicious payloads or unauthorized access to sensitive information. If confirmed malicious, this could lead to data breaches, unauthorized access, or further compromise of the cloud environment. Immediate investigation is required to determine the legitimacy of the instance creation and to mitigate potential threats.
Cloud Compute Instance Created With Previously Unseen Instance Type
The following analytic detects the creation of EC2 instances with previously unseen instance types. It leverages Splunk's tstats command to analyze data from the Change data model, identifying instance types that have not been previously recorded. This activity is significant for a SOC because it may indicate unauthorized or suspicious activity, such as an attacker attempting to create instances for malicious purposes. If confirmed malicious, this could lead to unauthorized access, data exfiltration, system compromise, or service disruption. Immediate investigation is required to determine the legitimacy of the instance creation.
Cloud Instance Modified By Previously Unseen User
The following analytic identifies cloud instances being modified by users who have not previously modified them. It leverages data from the Change data model, focusing on successful modifications of EC2 instances. This activity is significant because it can indicate unauthorized or suspicious changes by potentially compromised or malicious users. If confirmed malicious, this could lead to unauthorized access, configuration changes, or potential disruption of cloud services, posing a significant risk to the organization's cloud infrastructure.
Cloud Provisioning Activity From Previously Unseen City
The following analytic detects cloud provisioning activities originating from previously unseen cities. It leverages cloud infrastructure logs and compares the geographic location of the source IP address against a baseline of known locations. This activity is significant as it may indicate unauthorized access or misuse of cloud resources from an unexpected location. If confirmed malicious, this could lead to unauthorized resource creation, potential data exfiltration, or further compromise of cloud infrastructure.
Cloud Provisioning Activity From Previously Unseen Country
The following analytic detects cloud provisioning activities originating from previously unseen countries. It leverages cloud infrastructure logs and compares the geographic location of the source IP address against a baseline of known locations. This activity is significant as it may indicate unauthorized access or potential compromise of cloud resources. If confirmed malicious, an attacker could gain control over cloud assets, leading to data breaches, service disruptions, or further infiltration into the network.
Cloud Provisioning Activity From Previously Unseen IP Address
The following analytic detects cloud provisioning activities originating from previously unseen IP addresses. It leverages cloud infrastructure logs to identify events where resources are created or started, and cross-references these with a baseline of known IP addresses. This activity is significant as it may indicate unauthorized access or potential misuse of cloud resources. If confirmed malicious, an attacker could gain unauthorized control over cloud resources, leading to data breaches, service disruptions, or increased operational costs.
Cloud Provisioning Activity From Previously Unseen Region
The following analytic detects cloud provisioning activities originating from previously unseen regions. It leverages cloud infrastructure logs to identify events where resources are started or created, and cross-references these with a baseline of known regions. This activity is significant as it may indicate unauthorized access or misuse of cloud resources from unfamiliar locations. If confirmed malicious, this could lead to unauthorized resource creation, potential data exfiltration, or further compromise of cloud infrastructure.
Cloud Security Groups Modifications by User
The following analytic identifies unusual modifications to security groups in your cloud environment by users, focusing on actions such as modifications, deletions, or creations over 30-minute intervals. It leverages cloud infrastructure logs and calculates the standard deviation for each user, using the 3-sigma rule to detect anomalies. This activity is significant as it may indicate a compromised account or insider threat. If confirmed malicious, attackers could alter security group configurations, potentially exposing sensitive resources or disrupting services.
CMD Carry Out String Command Parameter
The following analytic detects the use of `cmd.exe /c` to execute commands, a technique often employed by adversaries and malware to run batch commands or invoke other shells like PowerShell. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process metadata. Monitoring this activity is crucial as it can indicate script-based attacks or unauthorized command execution. If confirmed malicious, this behavior could lead to unauthorized code execution, privilege escalation, or persistence within the environment.
CMD Echo Pipe - Escalation
The following analytic identifies the use of named-pipe impersonation for privilege escalation, commonly associated with Cobalt Strike and similar frameworks. It detects command-line executions where `cmd.exe` uses `echo` to write to a named pipe, such as `cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53`. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. This activity is significant as it indicates potential privilege escalation attempts. If confirmed malicious, attackers could gain elevated privileges, enabling further compromise and persistence within the environment.
CMLUA Or CMSTPLUA UAC Bypass
The following analytic detects the use of COM objects like CMLUA or CMSTPLUA to bypass User Account Control (UAC). It leverages Sysmon EventCode 7 to identify the loading of specific DLLs (CMLUA.dll, CMSTPLUA.dll, CMLUAUTIL.dll) by processes not typically associated with these libraries. This activity is significant as it indicates an attempt to gain elevated privileges, a common tactic used by ransomware adversaries. If confirmed malicious, this could allow attackers to execute code with administrative rights, leading to potential system compromise and further malicious activities.
Common Ransomware Extensions
The following analytic detects modifications to files with extensions commonly associated with ransomware. It leverages the Endpoint.Filesystem data model to identify changes in file extensions that match known ransomware patterns. This activity is significant because it suggests an attacker is attempting to encrypt or alter files, potentially leading to severe data loss and operational disruption. If confirmed malicious, this activity could result in the encryption of critical data, rendering it inaccessible and causing significant damage to the organization's data integrity and availability.
Common Ransomware Notes
The following analytic detects the creation of files with names commonly associated with ransomware notes. It leverages file-system activity data from the Endpoint Filesystem data model, typically populated by endpoint detection and response (EDR) tools or Sysmon logs. This activity is significant because ransomware notes indicate a potential ransomware attack, which can lead to data encryption and extortion. If confirmed malicious, this activity could result in significant data loss, operational disruption, and financial impact due to ransom demands. Note that this analytic relies on a lookup table (ransomware_notes_lookup) that contains known ransomware note file names. Ensure that this lookup table is regularly updated to include new ransomware note file names as they are identified in the threat landscape. Also this analytic leverages a sub-search to enhance performance. sub-searches have limitations on the amount of data they can return. Keep this in mind if you have an extensive list of ransomware note file names.