Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
68 detections
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Detects processes attempting to scrape unmasked CI runner secrets from memory, specifically looking for techniques used by the Miasma worm to bypass GitHub Actions secret masking. This rule identifies suspicious command-line patterns indicative of secret extraction, such as searching for 'isSecret' or accessing '/proc/mem' with Python.
Detects outbound web requests or GitHub API interactions containing the specific description string "Miasma: The Spreading Blight". This string is used as a signature by the Miasma malware (a Shai-Hulud variant involved in the @redhat-cloud-services npm supply chain attack) when creating malicious GitHub repositories to exfiltrate stolen credentials.
For network requests to the GitHub API containing specific staging and fallback markers (LongLiveTheResistanceAgainstMachines or beautifulcastle) used by TeamPCP in the Bitwarden CLI campaign to retrieve C2 instructions.
Detects a specific combination of HTTP GET, POST, and PUT requests to the GitHub API (api.github.com) within a short timeframe. This sequence matches the behavior of the AdaptixC2 custom listener used by Tropic Trooper, which uses GitHub issues for beaconing/task polling and the contents API for data exfiltration. The detection has been refined to look for specific AdaptixC2 operational keywords ('beat', 'upload', 'download') to reduce noise.
Detects a vulnerable GitHub Actions workflow configuration known as a "Pwn Request". This occurs when a workflow is triggered by `pull_request_target` but checks out and executes untrusted code from the head of the pull request (`${{ github.event.pull_request.head.sha }}`). This allows an attacker to run arbitrary code with the permissions of the target repository, including access to secrets. This pattern was actively exploited by the 'hackerbot-claw' campaign. This rule should be applied to files in the `.github/workflows/` directory.
Detects a suspicious sequence where a new credential (PAT, SSH key, or OAuth authorization) is created and then used to establish durable persistence via webhooks or deploy keys. This behavior typically occurs after an account compromise as an attacker attempts to "lock in" access that survives credential rotation and provides real-time exfiltration of repository data.
Detects the creation or modification of repository webhooks in GitHub. Attackers can use webhooks for real-time data exfiltration of code pushes, pull request comments, and workflow data. Once a webhook is established, it operates independently of the creating user; revoking the creator's credentials will NOT stop the data flow. Only deleting the webhook itself terminates the exfiltration.
Detects the Kimsuky TTP of using legitimate cloud services like GitHub and Google Drive to download and subsequently execute malicious payloads, often using interpreters like mshta.exe or rundll32.exe.
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.
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.
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.
Detects outbound API calls to api.github.com for repository creation or file uploads matching patterns used in the s1ngularity supply chain attack.
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.
'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 a fork activity against a repository done by a user who is not the owner of the repo nor a contributes.'
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.
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