Splunk Security
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The following analytic detects the execution of WMI command lines used to create or execute processes. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line events that include specific keywords like "process," "call," and "create." This activity is significant because adversaries often use WMI to execute malicious payloads on local or remote hosts, potentially bypassing traditional security controls. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a severe threat to organizational security.
The following analytic identifies a LOLBAS process being executed where it's process name does not match it's original file name attribute.
Processes that have been renamed and executed may be an indicator that an adversary is attempting to evade defenses or execute malicious code.
The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code.
Processes that have been renamed and executed may be an indicator that an adversary is attempting to evade defenses or execute malicious code.
The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code.
The following analytic detects unauthorized container uploads to AWS Elastic Container Service (ECR) by monitoring AWS CloudTrail events.
It identifies instances where a new container is uploaded by a user not previously recognized as authorized.
It identifies instances where a new container is uploaded by a user not previously recognized as authorized.
Detects bursts of five or more distinct Cisco AI Defense event IDs, or three or more occurrences of the same event ID, matching prompt injection, code detection, PII, or PCI guardrails for the same application and connection within ten minutes.
Allowed PII/PCI prompt events are handled by Cisco AI Defense Allowed Sensitive Prompt.
These patterns can indicate iterative prompt-injection probing or repeated attempts to use an AI application to access restricted data or capabilities.
Allowed PII/PCI prompt events are handled by Cisco AI Defense Allowed Sensitive Prompt.
These patterns can indicate iterative prompt-injection probing or repeated attempts to use an AI application to access restricted data or capabilities.
This analytic detects the usage of the Utility osascript on a macOS device initiated a network connection lasting longer than 10 minutes (600 seconds).
Adversaries may abuse osascript and AppleScript shell execution to establish long-lived command-and-control or remote connections.
Adversaries may abuse osascript and AppleScript shell execution to establish long-lived command-and-control or remote connections.
The following analytic detects file downloads that were classified as malware by Cisco Secure Firewall Threat Defense. It relies on the `SHA_Disposition` field with a value of "Malware" and includes metadata such as file name, file_hash hash, and threat classification. This analytic is critical for surfacing file-based threats that are identified via Cisco's AMP or Threat Grid integrations. If confirmed malicious, this could indicate delivery of malware.
The following analytic detects instances where Azure AD has blocked a user's attempt to grant consent to a risky or potentially malicious application.
This detection leverages Azure AD audit logs, focusing on user consent actions and system-driven blocks.
Monitoring these blocked consent attempts is crucial as it highlights potential threats early on, indicating that a user might be targeted or that malicious applications are attempting to infiltrate the organization.
If confirmed malicious, this activity suggests that Azure's security measures successfully prevented a harmful application from accessing organizational data, warranting immediate investigation to understand the context and take preventive measures.
This detection leverages Azure AD audit logs, focusing on user consent actions and system-driven blocks.
Monitoring these blocked consent attempts is crucial as it highlights potential threats early on, indicating that a user might be targeted or that malicious applications are attempting to infiltrate the organization.
If confirmed malicious, this activity suggests that Azure's security measures successfully prevented a harmful application from accessing organizational data, warranting immediate investigation to understand the context and take preventive measures.
The following analytic identifies a popular 3rd party software process being executed where it's process name does not match it's original file name attribute.
Processes that have been renamed and executed may be an indicator that an adversary is attempting to evade defenses or execute malicious code.
Processes that have been renamed and executed may be an indicator that an adversary is attempting to evade defenses or execute malicious code.
The following analytic detects instances where network traffic, identified by port and transport layer protocol as prohibited in the "lookup_interesting_ports" table, is allowed.
It uses the Network_Traffic data model to cross-reference traffic data against predefined security policies.
This activity is significant for a SOC as it highlights potential misconfigurations or policy violations that could lead to unauthorized access or data exfiltration.
If confirmed malicious, this could allow attackers to bypass network defenses, leading to potential data breaches and compromising the organization's security posture.
It uses the Network_Traffic data model to cross-reference traffic data against predefined security policies.
This activity is significant for a SOC as it highlights potential misconfigurations or policy violations that could lead to unauthorized access or data exfiltration.
If confirmed malicious, this could allow attackers to bypass network defenses, leading to potential data breaches and compromising the organization's security posture.
This detection identifies potential abuse of SSH "ProxyCommand" or "LocalCommand" by monitoring for suspicious process execution patterns.
Specifically, it looks for instances where ssh.exe (as a parent process) containing "ProxyCommand" or "LocalCommand" in its arguments spawns potentially malicious child processes like mshta, powershell, wscript, or cscript, or processes containing "http" in their command line.
This technique can be used by attackers to execute arbitrary commands through SSH proxy configurations, potentially enabling command & control activities or remote code execution. The detection focuses on commonly abused Windows scripting engines and web requests that may indicate malicious activity when spawned through SSH proxy commands.
Specifically, it looks for instances where ssh.exe (as a parent process) containing "ProxyCommand" or "LocalCommand" in its arguments spawns potentially malicious child processes like mshta, powershell, wscript, or cscript, or processes containing "http" in their command line.
This technique can be used by attackers to execute arbitrary commands through SSH proxy configurations, potentially enabling command & control activities or remote code execution. The detection focuses on commonly abused Windows scripting engines and web requests that may indicate malicious activity when spawned through SSH proxy commands.
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