Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
411 detections
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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.
The following analytic identifies the execution of RAR utilities to archive files on a system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, GUIDs, and command-line arguments. This activity is significant as threat actors, including red-teamers and malware like DarkGate, use RAR archiving to compress and exfiltrate collected data from compromised hosts. If confirmed malicious, this behavior could lead to the unauthorized transfer of sensitive information to command and control servers, posing a severe risk to data confidentiality and integrity.
Detects the creation or modification of a GitHub Actions workflow file that attempts to exfiltrate secrets. This pattern is associated with the Shai-Hulud worm, which injects malicious workflows to steal credentials using `toJSON(secrets)` and exfiltrate them using tools like `curl` and `base64`.
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.
The analytic detects when a Windows RDP client attempts to execute an RDP file from a temporary directory, downloads directory, or Outlook directories. This detection is significant as it can indicate an attempt for an adversary to deliver a .rdp file, which may be leveraged by attackers to control or exfiltrate data. If confirmed malicious, this activity could lead to unauthorized access, data theft, or further lateral movement within the network.
The analytic identifies the execution of the native mimikatz.exe binary on Windows systems, including instances where the binary is renamed. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and original file names. This activity is significant because Mimikatz is a widely used tool for extracting authentication credentials, posing a severe security risk. If confirmed malicious, this activity could allow attackers to obtain sensitive credentials, escalate privileges, and move laterally within the network, leading to potential data breaches and system compromise.
This rule is designed to identify any non-bot user accessing an unusually high number of repositories in a short time window (30 mins), which may indicate credential abuse or insider threat activity.
This analytic detects when classic branch protection rules are disabled in GitHub Organizations. It monitors GitHub Organizations audit logs for `protected_branch.destroy` events, tracking actor details, repository information, and associated metadata. Identifying disabled branch protection is critical as it could indicate attempts to bypass code review requirements and security controls. Branch protection rules are essential security controls that enforce code review, prevent force pushes, and maintain code quality. Disabling these protections could allow malicious actors to directly push unauthorized code changes or backdoors to protected branches. The impact of disabled branch protection includes potential code tampering, bypass of security reviews, introduction of vulnerabilities or malicious code, and compromise of software supply chain integrity. This activity could be part of a larger attack chain where an adversary first disables security controls before attempting to inject malicious code.
The analytic detects when a repository is archived in GitHub Organizations. The detection monitors GitHub Organizations audit logs for repository archival events by tracking actor details, repository information, and associated metadata. For a SOC, identifying repository archival is important as it could indicate attempts to make critical code inaccessible or preparation for repository deletion. While archiving is a legitimate feature, unauthorized archival of active repositories could signal account compromise, insider threats, or attempts to disrupt development operations. The impact of unauthorized repository archival includes loss of active development access, disruption to workflows and CI/CD pipelines, and potential business delays if critical repositories are affected. Additionally, archived repositories may be targeted for subsequent deletion, potentially resulting in permanent loss of intellectual property if proper backups are not maintained.
This analytic identifies when a repository is deleted within a GitHub organization. The detection monitors GitHub Organizations audit logs for repository deletion events by tracking actor details, repository information, and associated metadata. This behavior is concerning for SOC teams as malicious actors may attempt to delete repositories to destroy source code, intellectual property, or evidence of compromise. Repository deletion can result in permanent loss of code, documentation, and project history if proper backups are not maintained. Additionally, unauthorized repository deletion could indicate account compromise, insider threats, or attempts to disrupt business operations. The impact of a repository deletion attack includes loss of intellectual property, disruption to development workflows, and potential financial losses from lost work. Early detection of unauthorized repository deletions allows security teams to investigate potential compromises and restore from backups if needed.
The analytic detects when branch rulesets are deleted in GitHub Organizations. The detection monitors GitHub Organizations audit logs for branch ruleset deletion events by tracking actor details, repository information, and associated metadata. For a SOC, identifying deleted branch rulesets is critical as it could indicate attempts to bypass code review requirements and security controls. Branch rulesets are essential security controls that enforce code review, prevent force pushes, and maintain code quality. Disabling these protections could allow malicious actors to directly push unauthorized code changes or backdoors to protected branches. The impact of disabled branch protection includes potential code tampering, bypass of security reviews, introduction of vulnerabilities or malicious code, and compromise of software supply chain integrity. This activity could be part of a larger attack chain where an adversary first disables security controls before attempting to inject malicious code.
The following analytic detects when a user disables Dependabot security features within a GitHub repository. Dependabot helps automatically identify and fix security vulnerabilities in dependencies. The detection monitors GitHub Enterprise logs for configuration changes that disable Dependabot functionality. This behavior could indicate an attacker attempting to prevent the automatic detection of vulnerable dependencies, which would allow them to exploit known vulnerabilities that would otherwise be patched. For a SOC, identifying the disabling of security features like Dependabot is critical as it may be a precursor to supply chain attacks where attackers exploit vulnerable dependencies. The impact could be severe if vulnerabilities remain unpatched, potentially leading to code execution, data theft, or other compromises through the software supply chain.
'Attacker can exfiltrate data from your GitHub repository by cloning it. This hunting query tracks clone activities for each repository, allowing quick identification of anomalies/excessive clones to investigate repo access & permissions.'
'This hunting query identifies GitHub activites where a repo was changed from private to public that may be a sign of compromise.'
'This hunting query identifies a fork activity against a repository done by a user who is not the owner of the repo nor a contributes.'
'This hunting query identifies GitHub activites where permissions are updated that may be a sign of compromise.'
'This hunting query identifies GitHub activites where there are a large number of deletions that may be a sign of compromise.'
'This hunting query identifies a user that add/invite a member to the organization for the first time. This technique can be leveraged by attackers to add stealth account access to the organization.'
'This hunting query identifies GitHub activites its the first time a user deleted a repo that may be a sign of compromise.'
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.
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