Splunk Security
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Detects API reconnaissance and endpoint scanning activity against Ollama servers by identifying sources probing multiple API endpoints within short timeframes, particularly when using HEAD requests or accessing diverse endpoint paths, which indicates systematic enumeration to map the API surface, discover hidden endpoints, or identify vulnerabilities before launching targeted attacks.
Detects critical service crashes, fatal errors, and abnormal process terminations in Ollama that may indicate exploitation attempts, resource exhaustion attacks, malicious input triggering unhandled exceptions, or deliberate denial of service attacks designed to disrupt AI model availability and degrade system stability.
Detects data leakage and exfiltration attempts targeting Ollama model metadata and configuration endpoints. Adversaries repeatedly query /api/show, /api/tags, and /api/v1/models to systematically extract sensitive model information including architecture details, fine-tuning parameters, system paths, Modelfile configurations, and proprietary customizations. Multiple inspection attempts within a 15-minute window indicate automated exfiltration of valuable intellectual property such as custom model configurations, system prompts, and internal model specifications. This activity represents unauthorized data disclosure that could enable competitive intelligence gathering, model replication, or preparation for advanced attacks against the AI infrastructure.
Detects Ollama server errors and failures during model loading operations that may indicate malicious model injection, path traversal attempts, or exploitation of model loading mechanisms to achieve remote code execution. Adversaries may attempt to load specially crafted malicious models or exploit vulnerabilities in the model loading process to execute arbitrary code on the server. This detection monitors error messages and failure patterns that could signal attempts to abuse model loading functionality for malicious purposes.
The following analytic detects the execution of tools commonly exploited by cybercriminals, such as those used for unauthorized access, network scanning, privilege escalation, password dumping or data exfiltration.
It leverages process activity data from Endpoint Detection and Response (EDR) agents, focusing on known attacker tool names.
This activity is significant because it serves as an early warning system for potential security incidents, enabling prompt response. If confirmed malicious, this activity could lead to unauthorized access, data theft, or further network compromise, posing a severe threat to the organization's security infrastructure.
It leverages process activity data from Endpoint Detection and Response (EDR) agents, focusing on known attacker tool names.
This activity is significant because it serves as an early warning system for potential security incidents, enabling prompt response. If confirmed malicious, this activity could lead to unauthorized access, data theft, or further network compromise, posing a severe threat to the organization's security infrastructure.
The following analytic identifies a local successful authentication event on a Windows endpoint using the Kerberos package.
It detects EventCode 4624 with LogonType 3 and source address 127.0.0.1, indicating a login to the built-in local Administrator account.
This activity is significant as it may suggest a Kerberos relay attack, a method attackers use to escalate privileges.
If confirmed malicious, this could allow an attacker to gain unauthorized access to sensitive systems, execute arbitrary code, or create new accounts in Active Directory, leading to potential system compromise.
It detects EventCode 4624 with LogonType 3 and source address 127.0.0.1, indicating a login to the built-in local Administrator account.
This activity is significant as it may suggest a Kerberos relay attack, a method attackers use to escalate privileges.
If confirmed malicious, this could allow an attacker to gain unauthorized access to sensitive systems, execute arbitrary code, or create new accounts in Active Directory, leading to potential system compromise.
This analytic detects unexpected outbound network connections initiated by known webserver processes such as `httpd.exe`, `nginx.exe`, or `tomcat.exe` to common file sharing or public content hosting services like GitHub, Discord CDN, Transfer.sh, or Pastebin.
Webservers are rarely expected to perform outbound downloads, especially to dynamic or anonymous file hosting domains. This behavior is often associated with server compromise,
where an attacker uses a reverse shell, webshell, or injected task to fetch malware or tools post-exploitation.
The detection leverages Cisco Network Visibility Module flow data, enriched with process context, to identify this highly suspicious behavior.
Webservers are rarely expected to perform outbound downloads, especially to dynamic or anonymous file hosting domains. This behavior is often associated with server compromise,
where an attacker uses a reverse shell, webshell, or injected task to fetch malware or tools post-exploitation.
The detection leverages Cisco Network Visibility Module flow data, enriched with process context, to identify this highly suspicious behavior.
The following analytic detects the execution of native .NET binaries from non-standard directories within the Windows operating system.
It leverages Endpoint Detection and Response (EDR) telemetry, comparing process names and original file names against a predefined lookup "is_net_windows_file".
This activity is significant because adversaries may move .NET binaries to unconventional paths to evade detection and execute malicious code.
If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a significant security risk.
Also this analytic leverages a sub-search to enhance performance. sub-searches have limitations on the amount of data they can return. Keep this in mind if you have an extensive list of ransomware note file names.
It leverages Endpoint Detection and Response (EDR) telemetry, comparing process names and original file names against a predefined lookup "is_net_windows_file".
This activity is significant because adversaries may move .NET binaries to unconventional paths to evade detection and execute malicious code.
If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a significant security risk.
Also this analytic leverages a sub-search to enhance performance. sub-searches have limitations on the amount of data they can return. Keep this in mind if you have an extensive list of ransomware note file names.
The following analytic detects the use of the `[Adsisearcher]` type accelerator in PowerShell to query Active Directory for domain users. It leverages PowerShell Script Block Logging (EventCode=4104) to identify script blocks containing `[adsisearcher]`, `objectcategory=user`, and `.findAll()`. This activity is significant as it may indicate an attempt by adversaries or Red Teams to enumerate domain users for situational awareness and Active Directory discovery. If confirmed malicious, this could lead to further reconnaissance, privilege escalation, or lateral movement within the network.
The following analytic detects the use of `bitsadmin.exe` to schedule a BITS job for persistence on an endpoint. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line parameters such as `create`, `addfile`, and `resume`. This activity is significant because BITS jobs can be used by attackers to maintain persistence, download malicious payloads, or exfiltrate data. If confirmed malicious, this could allow an attacker to persist in the environment, execute arbitrary code, or transfer sensitive information, necessitating further investigation and potential remediation.
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