Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
74 detections
Filters
Last updated
All Time
Detection languages
29
12
10
10
7
Contributors
32
24
7
5
2
Categories
34
17
16
12
12
Platforms
52
12
11
7
7
Products / Services
10,670
9,758
6,693
4,421
3,749
MITRE Techniques
19
17
16
14
14
CVEs
1
1
1
Detects attempts to access the GCP instance metadata service (metadata.google.internal) by exploiting Server-Side Request Forgery (SSRF) vulnerabilities. The rule looks for URI patterns often used to bypass filters by manipulating the authority component or using specific protocols like 'gcs://' to target internal cloud infrastructure.
Detects Server-Side Request Forgery (SSRF) attempts directed at the Google Cloud Platform (GCP) instance metadata service endpoints. The rule monitors HTTP request logs from proxies or web servers, identifying requests that contain suspicious metadata hostnames or IP addresses combined with sensitive API paths typically used for exfiltrating cloud instance identity tokens.
Detects access to sensitive GCP credential-related files or the presence of credential-related environment variables/secrets in command lines and network request bodies. This pattern is often associated with the theft of service account keys or environment-based cloud credentials, frequently seen in serverless or containerized environments.
Detects potential GCP logging sink bucket hijacking by monitoring for the deletion of a Google Cloud Storage bucket followed closely by an update to a logging sink configuration that points to a GCS destination. This pattern is characteristic of attackers attempting to intercept cloud log streams by recreating a target destination bucket in an environment they control.
Detects a sequence of events where an actor deletes a Google Cloud Storage bucket and then immediately recreates it, a pattern consistent with bucket hijacking intended to manipulate resource access or bypass security controls on data streams.
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.
'Matches domain name IOCs related to Forest Blizzard group activity published July 2019 with CommonSecurityLog, DnsEvents and VMConnection dataTypes.
References: https://blogs.microsoft.com/on-the-issues/2019/07/17/new-cyberthreats-require-new-ways-to-protect-democracy/.'
References: https://blogs.microsoft.com/on-the-issues/2019/07/17/new-cyberthreats-require-new-ways-to-protect-democracy/.'
'Identifies IP addresses that may be performing DNS lookups associated with common currency mining pools.
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
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.
This correlation rule identifies when a user exceeds a predefined risk threshold based on multiple suspicious Okta activities within a 24-hour period. It aggregates risk events from "Suspicious Okta Activity," "Okta Account Takeover," and "Okta MFA Exhaustion" analytic stories. The detection is significant as it highlights potentially compromised user accounts exhibiting multiple tactics, techniques, and procedures (TTPs). If confirmed malicious, this activity could indicate a serious security breach, allowing attackers to gain unauthorized access, escalate privileges, or persist within the environment.
This analytic identifies the creation of new open/public GCP Storage buckets. It leverages GCP PubSub events, specifically monitoring for the `storage.setIamPermissions` method and checks if the `allUsers` member is added. This activity is significant because open storage buckets can expose sensitive data to the public, posing a severe security risk. If confirmed malicious, an attacker could access, modify, or delete data within the bucket, leading to data breaches and potential compliance violations.
The following analytic identifies suspicious command-line parameters associated with the use of Impacket's smbexec.py for lateral movement. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns indicative of Impacket tool usage. This activity is significant as both Red Teams and adversaries use Impacket for remote code execution and lateral movement. If confirmed malicious, this activity could allow attackers to execute commands on remote endpoints, potentially leading to unauthorized access, data exfiltration, or further compromise of the network.
Page 3 of 4