Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
72 detections
Filters
Last updated
All Time
Detection languages
41
21
4
4
1
Contributors
40
21
5
4
1
Categories
34
15
8
8
7
Platforms
54
7
7
2
2
Products / Services
10,650
9,755
6,688
4,418
3,745
MITRE Techniques
28
17
16
11
10
CVEs
1
1
1
Detects unauthorized attempts to disable, suspend, or downgrade critical native cloud security and monitoring services (e.g., AWS GuardDuty, AWS Security Hub, Azure Defender for Cloud, and GCP Security Command Center). Attackers often perform these actions to evade detection of subsequent malicious activity within the cloud environment.
Detects attempts to tamper with or disable AWS security monitoring controls, specifically GuardDuty detectors and AWS Config recording. Attackers frequently disable these services to evade detection after gaining administrative permissions in a cloud environment.
Detects unauthorized or suspicious modifications to AWS GuardDuty, including detector deletion, disabling of the service, disassociation from the master account, or the creation of suppression filters that archive findings, all of which indicate potential defense evasion by an adversary.
Detects administrative actions intended to disable, modify, or suppress logging and alerting capabilities within AWS and Azure, specifically targeting services like Amazon GuardDuty, AWS Security Hub, AWS Config, and Microsoft Defender for Cloud. Such actions are frequently observed as a precursor to unauthorized activity to hide malicious actions from detection systems.
Detects unauthorized attempts to disable, suppress, or modify security monitoring services in AWS, including GuardDuty, Security Hub, and AWS Config. This rule monitors for API calls that effectively reduce cloud visibility or detection capabilities, explicitly excluding actions performed by authorized security-engineering roles.
Detects administrative actions that disable or modify Amazon GuardDuty, which could be an attempt to blind security monitoring or evade detection of malicious activity. This includes disabling a detector, deleting publishing destinations, or modifying threat intel and IP sets.
This rule detects a correlation between an unauthorized IAM user access event and the subsequent execution of cryptocurrency mining tools within the same AWS account within a one-hour window. This is a strong indicator of an adversary leveraging compromised cloud credentials to perform resource hijacking.
Detects modifications to AWS GuardDuty configurations, including disabling the detector, deleting publishing destinations, or altering threat intelligence sets and trusted IP lists. These actions could indicate an attempt to blind security monitoring or evade detection.
Detects high severity (>= 9.0) AWS GuardDuty findings indicating that an EC2 instance or ECS cluster has been compromised, typically suggesting active exploitation or control by a malicious actor.
Detects attempts to tamper with AWS CloudTrail or GuardDuty logging configurations by monitoring specific API calls that can disable, modify, or delete logging mechanisms. This includes actions like stopping logging, deleting trails, updating trails, disassociating from master accounts, archiving findings, stopping member monitoring, and deleting or updating detectors. Successful execution of these actions without an error indicates potential malicious activity aimed at evading detection.
The following analytic detects the use of default or publicly known named pipes associated with Cobalt Strike. It leverages Sysmon EventID 17 and 18 to identify specific named pipes commonly used by Cobalt Strike's Artifact Kit and Malleable C2 Profiles. This activity is significant because Cobalt Strike is a popular tool for adversaries to conduct post-exploitation tasks, and identifying its named pipes can reveal potential malicious activity. If confirmed malicious, this could indicate an active Cobalt Strike beacon, leading to unauthorized access, data exfiltration, or further lateral movement within the network.
This analytic detects suspicious data transfer activities involving the use of the `split` syscall with the `-b` option, which specifies the size of each output file. This technique can be used by attackers to evade detection by breaking large files into smaller parts, bypassing size-based security controls, and facilitating covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall with a specified block size, this analytic helps identify potential data exfiltration attempts.
This analytic identifies the bulk creation of ransomware notes (e.g., .txt, .html, .hta files) on an infected machine. It leverages Sysmon EventCode 11 to detect multiple instances of these file types being created within a short time frame (10 seconds). A detection is triggered if 15 or more unique readme paths are observed. This activity is a strong indicator of an active ransomware attack, where the attacker is notifying the victim of the encryption. If confirmed malicious, this behavior could lead to widespread data encryption, rendering critical files inaccessible and potentially causing significant operational disruption.
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.
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.
This analytic detects suspicious modifications to Unix shell configuration files, such as `.bashrc` or `.profile`, which may indicate an attempt to alter system behavior or gain unauthorized access. Unauthorized changes to these files can be used to execute malicious commands, escalate privileges, or hide malicious activities. By monitoring for unusual or unauthorized modifications to shell configuration files, this analytic helps identify potential security threats, allowing security teams to respond quickly and mitigate risks.
The following analytic detects attempts to stop a service on Linux systems. It leverages data from Linux Auditd. This activity is significant as adversaries often stop or terminate security or critical services to disable defenses or disrupt operations, as seen in malware like Industroyer2. If confirmed malicious, this could lead to the disabling of security mechanisms, allowing attackers to persist, escalate privileges, or deploy destructive payloads, severely impacting system integrity and availability.
The following analytic detects the suspicious auditd service stop. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents.
The analytic detects a spike in the number of API calls made to cloud infrastructure by a user. It leverages cloud infrastructure logs and compares the current API call volume against a baseline probability density function to identify anomalies. This activity is significant because an unusual increase in API calls can indicate potential misuse or compromise of cloud resources. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or disruption of cloud services, posing a significant risk to the organization's cloud environment.
The 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.
The 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.
Page 1 of 4