Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
53 detections
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Detects web requests targeting sensitive Jenkins/CI-CD administrative and build-triggering endpoints, such as /script, /manage, and /job/*/build. These paths are high-value targets for reconnaissance and automated exploitation efforts. This rule identifies potentially malicious activity by monitoring for access to these specific paths, which should be correlated with frequency and source data to differentiate automated scanning from authorized administrative or system activities.
Detects modifications, creations, or deletions of critical CI/CD pipeline configuration files (GitHub Actions, GitLab CI, Jenkins, Azure Pipelines) across cloud service logs. The rule correlates these actions with the account's historical IP baseline to identify potentially unauthorized changes originating from anomalous locations.
This rule detects potential AI/ML supply chain compromise by monitoring for package installations (via pip, conda, poetry, etc.) from non-standard repositories, direct URLs, or version control systems within data science and CI/CD pipelines (e.g., Jupyter, Airflow, Jenkins). It further correlates this activity with subsequent suspicious outbound network connections from the host, which is indicative of slopsquatting or malicious dependency execution.
Detects anomalous activity in a CI/CD environment where a runner process accesses sensitive credential files (such as PyPI tokens or GitHub secrets) followed closely by a network connection to public package registry domains, potentially indicating a supply-chain compromise where stolen credentials are used to publish malicious packages.
Detects the installation of software packages in build or CI pipelines where the package was published to its registry between 30 and 90 days prior to being first used in the environment. This pattern is indicative of potential dependency confusion or typosquatting attacks, where adversaries use newly created packages to deliver malicious code.
Detects the installation of software packages in build or CI pipelines where the package was published to its registry between 30 and 90 days prior to being first used in the environment. This pattern is indicative of potential dependency confusion or typosquatting attacks, where adversaries use newly created packages to deliver malicious code.
Detects the installation of software packages in build or CI pipelines where the package was published to its registry between 30 and 90 days prior to being first used in the environment. This pattern is indicative of potential dependency confusion or typosquatting attacks, where adversaries use newly created packages to deliver malicious code.
Detects the installation of software packages in build or CI pipelines where the package was published to its registry between 30 and 90 days prior to being first used in the environment. This pattern is indicative of potential dependency confusion or typosquatting attacks, where adversaries use newly created packages to deliver malicious code.
Detects the installation of software packages in build or CI pipelines where the package was published to its registry between 30 and 90 days prior to being first used in the environment. This pattern is indicative of potential dependency confusion or typosquatting attacks, where adversaries use newly created packages to deliver malicious code.
Detects the installation of software packages in build or CI pipelines where the package was published to its registry between 30 and 90 days prior to being first used in the environment. This pattern is indicative of potential dependency confusion or typosquatting attacks, where adversaries use newly created packages to deliver malicious code.
Detects unauthorized access or modification to common CI/CD credential files (.npmrc, .pypirc, .git-credentials, jenkins credentials.xml) and CI/CD configuration files (.github/workflows, .gitlab-ci.yml). The rule monitors both file system activities by non-standard processes and the execution of command-line tools that reference these sensitive files.
Detects unauthorized access or modification to common CI/CD credential files (.npmrc, .pypirc, .git-credentials, jenkins credentials.xml) and CI/CD configuration files (.github/workflows, .gitlab-ci.yml). The rule monitors both file system activities by non-standard processes and the execution of command-line tools that reference these sensitive files.
Detects the creation of files indicative of the compromised Checkmarx AST plugin version 2026.5.09 being deployed. This rule identifies specific malicious filenames or known compromised SHA256 hashes associated with the TeamPCP backdoor.
Detects unauthorized interactive modifications to CI/CD pipeline configurations that could indicate supply chain attack or malicious dependency code injection attempts.
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 attempts to exploit Jenkins Arbitrary File Read CVE-2024-23897. It detects HTTP POST requests to Jenkins URLs containing "*/cli?remoting=false*" with a 200 status code. This activity is significant as it indicates potential unauthorized access to sensitive files on the Jenkins server, such as credentials and private keys. If confirmed malicious, this could lead to severe data breaches, unauthorized access, and further exploitation within the environment.
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.
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