Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
228 detections
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This query will identify the Microsoft Defender Antivirus Engine version and Microsoft Defender Antivirus Security Intelligence version (and timestamp), Product update version (aka Platform Update version) as well as the Microsoft Defender Antivirus Mode on the endpoint (Active, Passive, etc.).
Microsoft has observed threat actors exploiting vulnerabilities associated with Log4J.
This query was originally published in the threat analytics report, Confluence and WebLogic abuse.
2019 has seen several seemingly related campaigns targeting Atlassian Confluence Server and Oracle WebLogic Server. Although these campaigns use different implants and delivery methods, they consistently use the same infrastructure, and exploit the same vulnerabilities.
The campaigns have specifically targeted:
1. CVE-2019-3396 - Software update
2. CVE-2019-2725 - Software update
The following query detects activity broadly associated with these campaigns.
2019 has seen several seemingly related campaigns targeting Atlassian Confluence Server and Oracle WebLogic Server. Although these campaigns use different implants and delivery methods, they consistently use the same infrastructure, and exploit the same vulnerabilities.
The campaigns have specifically targeted:
1. CVE-2019-3396 - Software update
2. CVE-2019-2725 - Software update
The following query detects activity broadly associated with these campaigns.
Microsoft has observed attackers who have gained entry to an environment via the Log4J vulnerability utilizing identifiable strings in PowerShell commands.
'This query uses the Azure Defender Security Nested Recommendations data to find machines vulnerable to OMIGOD CVE-2021-38647.
OMI is the Linux equivalent of Windows WMI and helps users manage configurations across remote and local environments. The query aims to find machines that have this OMI vulnerability (CVE-2021-38647).
Security Nested Recommendations data is sent to Microsoft Sentinel using the continuous export feature of Azure Defender(refrence link below).
Reference: https://www.wiz.io/blog/omigod-critical-vulnerabilities-in-omi-azure
Reference: https://docs.microsoft.com/azure/security-center/continuous-export?tabs=azure-portal'
OMI is the Linux equivalent of Windows WMI and helps users manage configurations across remote and local environments. The query aims to find machines that have this OMI vulnerability (CVE-2021-38647).
Security Nested Recommendations data is sent to Microsoft Sentinel using the continuous export feature of Azure Defender(refrence link below).
Reference: https://www.wiz.io/blog/omigod-critical-vulnerabilities-in-omi-azure
Reference: https://docs.microsoft.com/azure/security-center/continuous-export?tabs=azure-portal'
Microsoft has observed attackers who have gained entry to an environment via the Log4J vulnerability utilizing uncommon PowerShell flags to communicate to command-and-control infrastructure.
// Author: jan geisbauer
// @janvonkirchheim
// ------------------------
// 1. A list of all devices that have this vulnerability
// 2. A list of all users that uses those devices
// 3. If these users received .mkv files recently
// @janvonkirchheim
// ------------------------
// 1. A list of all devices that have this vulnerability
// 2. A list of all users that uses those devices
// 3. If these users received .mkv files recently
// Author: jan geisbauer
// @janvonkirchheim
// ------------------------
// 1. A list of all devices that have this vulnerability
// 2. A list of all users that uses those devices
// 3. If these users received .mkv files recently
// @janvonkirchheim
// ------------------------
// 1. A list of all devices that have this vulnerability
// 2. A list of all users that uses those devices
// 3. If these users received .mkv files recently
This query was originally published in the threat analytics report, Solorigate supply chain attack. Please note that these attacks are currently known as the Nobelium campaign.
Microsoft detects the 2020 SolarWinds supply chain attack implant and its other components as part of a campaign by the Nobelium activity group. Nobelium is the threat actor behind the attack against SolarWinds, which was previously referred to as Solorigate.
Nobelium silently added malicious code to legitimate software updates for Orion, which is IT monitoring software provided by SolarWinds. In this way, malicious dynamic link libraries (DLLs) were distributed to SolarWinds customers.
The following query retrieves an inventory of SolarWinds Orion software use in your organization, organized by product name and ordered by how many devices the software is installed on.
More Nobelium-related queries can be found listed under the See also section of this document.
References:
https://msrc-blog.microsoft.com/2020/12/13/customer-guidance-on-recent-nation-state-cyber-attacks/
https://www.microsoft.com/security/blog/2020/12/18/analyzing-solorigate-the-compromised-dll-file-that-started-a-sophisticated-cyberattack-and-how-microsoft-defender-helps-protect/
Microsoft detects the 2020 SolarWinds supply chain attack implant and its other components as part of a campaign by the Nobelium activity group. Nobelium is the threat actor behind the attack against SolarWinds, which was previously referred to as Solorigate.
Nobelium silently added malicious code to legitimate software updates for Orion, which is IT monitoring software provided by SolarWinds. In this way, malicious dynamic link libraries (DLLs) were distributed to SolarWinds customers.
The following query retrieves an inventory of SolarWinds Orion software use in your organization, organized by product name and ordered by how many devices the software is installed on.
More Nobelium-related queries can be found listed under the See also section of this document.
References:
https://msrc-blog.microsoft.com/2020/12/13/customer-guidance-on-recent-nation-state-cyber-attacks/
https://www.microsoft.com/security/blog/2020/12/18/analyzing-solorigate-the-compromised-dll-file-that-started-a-sophisticated-cyberattack-and-how-microsoft-defender-helps-protect/
This analytic identifies internal systems that generate a high volume of intrusion detection events within a 30-minute window, specifically leveraging Cisco Secure Firewall Threat Defense logs. It counts hosts that trigger 15 or more Snort-based signatures. A sudden increase in intrusion alerts from a single host can indicate malicious activity such as malware execution, command-and-control communication, vulnerability scanning, or lateral movement. It may also be caused by misconfigured software. Systems exhibiting this pattern require prompt investigation as repeated Snort rule matches are often early indicators of compromise or active exploitation.
The following analytic detects unauthenticated requests to an Amazon EKS Kubernetes cluster, specifically identifying actions by the "system:anonymous" user. It leverages AWS CloudWatch Logs data, focusing on user agents and authentication details. This activity is significant as it may indicate unauthorized scanning or probing of the Kubernetes cluster, which could be a precursor to an attack. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or disruption of services within the Kubernetes environment.
This analytic detects internal hosts triggering multiple IDS signatures, which may include either more than 25 signatures against a single host or a single signature across over 25 destination IP addresses. Such patterns can indicate active vulnerability scanning activities within the network. By monitoring IDS logs, this detection helps identify and respond to potential vulnerability scanning attempts, enhancing the network's security posture and preventing potential exploits.
The following analytic identifies high-severity findings from AWS Elastic Container Registry (ECR) image scans. It detects these activities by analyzing AWS CloudTrail logs for the DescribeImageScanFindings event, specifically filtering for findings with a high severity level. This activity is significant for a SOC because high-severity vulnerabilities in container images can lead to potential exploitation if not addressed. If confirmed malicious, attackers could exploit these vulnerabilities to gain unauthorized access, execute arbitrary code, or escalate privileges within the container environment, posing a significant risk to the overall security posture.
The following analytic identifies low, informational, or unknown severity findings from AWS Elastic Container Registry (ECR) image scans. It leverages AWS CloudTrail logs, specifically the DescribeImageScanFindings event, to detect these findings. This activity is significant for a SOC as it helps in early identification of potential vulnerabilities or misconfigurations in container images, which could be exploited if left unaddressed. If confirmed malicious, these findings could lead to unauthorized access, data breaches, or further exploitation within the containerized environment.
The following analytic identifies specific GET or HEAD requests to web servers that indicate reconnaissance attempts to find vulnerable JBoss servers. It leverages data from the Web data model, focusing on HTTP methods and URLs associated with JBoss management interfaces. This activity is significant because it often precedes exploitation attempts using tools like JexBoss, which can compromise the server. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary code, or escalate privileges, leading to potential data breaches and system compromise.
The following analytic identifies unusually long strings in the Content-Type HTTP header sent by the client to the server. It uses data from the Stream:HTTP source, specifically evaluating the length of the `cs_content_type` field. This activity is significant because excessively long Content-Type headers can indicate attempts to exploit vulnerabilities or evade detection mechanisms. If confirmed malicious, this behavior could allow attackers to execute code, manipulate data, or bypass security controls, potentially leading to unauthorized access or data breaches.
This rule detects suspicious user agent strings commonly used by various hacking tools, including vulnerability scanners, brute-force tools, and SQL injection tools, within proxy logs. The rule identifies specific strings indicative of tools like Arachni, Hydra, DirBuster, SQLMap, and others, which are often employed during reconnaissance, initial access, and credential access phases of an attack.
Detects suspicious DNS queries to external service interaction domains often used for out-of-band interactions after successful RCE
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