EXPLORE DETECTIONS
Okta Unauthorized Access to Application
The following analytic identifies attempts by users to access Okta applications that have not been assigned to them. It leverages Okta Identity Management logs, specifically focusing on failed access attempts to unassigned applications. This activity is significant for a SOC as it may indicate potential unauthorized access attempts, which could lead to exposure of sensitive information or disruption of services. If confirmed malicious, such activity could result in data breaches, non-compliance with data protection laws, and overall compromise of the IT environment.
Okta User Logins from Multiple Cities
The following analytic identifies instances where the same Okta user logs in from different cities within a 24-hour period. This detection leverages Okta Identity Management logs, analyzing login events and their geographic locations. Such behavior is significant as it may indicate a compromised account, with an attacker attempting unauthorized access from multiple locations. If confirmed malicious, this activity could lead to account takeovers and data breaches, allowing attackers to access sensitive information and potentially escalate their privileges within the environment.
Ollama Abnormal Network Connectivity
Detects abnormal network activity and connectivity issues in Ollama including non-localhost API access attempts and warning-level network errors such as DNS lookup failures, TCP connection issues, or host resolution problems that may indicate network-based attacks, unauthorized access attempts, or infrastructure reconnaissance activity.
Ollama Abnormal Service Crash Availability Attack
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.
Ollama Excessive API Requests
Detects potential Distributed Denial of Service (DDoS) attacks or rate limit abuse against Ollama API endpoints by identifying excessive request volumes from individual client IP addresses. This detection monitors GIN-formatted Ollama server logs to identify clients generating abnormally high request rates within short time windows, which may indicate automated attacks, botnet activity, or resource exhaustion attempts targeting local AI model infrastructure.
Ollama Possible API Endpoint Scan Reconnaissance
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.
Ollama Possible Memory Exhaustion Resource Abuse
Detects abnormal memory allocation patterns and excessive runner operations in Ollama that may indicate resource exhaustion attacks, memory abuse through malicious model loading, or attempts to degrade system performance by overwhelming GPU/CPU resources. Adversaries may deliberately load multiple large models, trigger repeated model initialization cycles, or exploit memory allocation mechanisms to exhaust available system resources, causing denial of service conditions or degrading performance for legitimate users.
Ollama Possible Model Exfiltration Data Leakage
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.
Ollama Possible RCE via Model Loading
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.
Ollama Suspicious Prompt Injection Jailbreak
Detects potential prompt injection or jailbreak attempts against Ollama API endpoints by identifying requests with abnormally long response times. Attackers often craft complex, layered prompts designed to bypass AI safety controls, which typically result in extended processing times as the model attempts to parse and respond to these malicious inputs. This detection monitors /api/generate and /api/chat endpoints for requests exceeding 30 seconds, which may indicate sophisticated jailbreak techniques, multi-stage prompt injections, or attempts to extract sensitive information from the model.
Outbound Network Connection from Java Using Default Ports
The following analytic detects outbound network connections from Java processes to default ports used by LDAP and RMI protocols, which may indicate exploitation of the CVE-2021-44228-Log4j vulnerability. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and network traffic logs. Monitoring this activity is crucial as it can signify an attacker's attempt to perform JNDI lookups and retrieve malicious payloads. If confirmed malicious, this activity could lead to remote code execution and further compromise of the affected server.
Overwriting Accessibility Binaries
The following analytic detects modifications to Windows accessibility binaries such as sethc.exe, utilman.exe, osk.exe, Magnify.exe, Narrator.exe, DisplaySwitch.exe, and AtBroker.exe. It leverages filesystem activity data from the Endpoint.Filesystem data model to identify changes to these specific files. This activity is significant because adversaries can exploit these binaries to gain unauthorized access or execute commands without logging in. If confirmed malicious, this could allow attackers to bypass authentication mechanisms, potentially leading to unauthorized system access and further compromise of the environment.
PaperCut NG Remote Web Access Attempt
The following analytic detects potential exploitation attempts on publicly accessible PaperCut NG servers. It identifies connections from public IP addresses to the server, specifically monitoring URI paths commonly used in proof-of-concept scripts for exploiting PaperCut NG vulnerabilities. This detection leverages web traffic data from the `Web` datamodel, focusing on specific URI paths and excluding internal IP ranges. This activity is significant as it may indicate an attempt to exploit known vulnerabilities in PaperCut NG, potentially leading to unauthorized access or control of the server. If confirmed malicious, attackers could gain administrative access, leading to data breaches or further network compromise.
PaperCut NG Suspicious Behavior Debug Log
The following analytic identifies potential exploitation attempts on a PaperCut NG server by analyzing its debug log data. It detects unauthorized or suspicious access attempts from public IP addresses and searches for specific URIs associated with known exploits. The detection leverages regex to parse unstructured log data, focusing on admin login activities. This activity is significant as it can indicate an active exploitation attempt on the server. If confirmed malicious, attackers could gain unauthorized access, potentially leading to data breaches or further compromise of the server.
Permission Modification using Takeown App
The following analytic detects the modification of file or directory permissions using the takeown.exe Windows application. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include process GUID, process name, and command-line details. This activity is significant because it is a common technique used by ransomware to take ownership of files or folders for encryption or deletion. If confirmed malicious, this could lead to unauthorized access, data encryption, or data destruction, severely impacting the integrity and availability of critical data.
PetitPotam Network Share Access Request
The following analytic detects network share access requests indicative of the PetitPotam attack (CVE-2021-36942). It leverages Windows Event Code 5145, which logs attempts to access network share objects. This detection is significant as PetitPotam can coerce authentication from domain controllers, potentially leading to unauthorized access. If confirmed malicious, this activity could allow attackers to escalate privileges or move laterally within the network, posing a severe security risk. Ensure Event Code 5145 is enabled via Group Policy to utilize this analytic effectively.
PetitPotam Suspicious Kerberos TGT Request
The following analytic detects a suspicious Kerberos Ticket Granting Ticket (TGT) request, identified by Event Code 4768. This detection leverages Windows Security Event Logs to identify TGT requests with unusual fields, which may indicate the use of tools like Rubeus following the exploitation of CVE-2021-36942 (PetitPotam). This activity is significant as it can signal an attacker leveraging a compromised certificate to request Kerberos tickets, potentially leading to unauthorized access. If confirmed malicious, this could allow attackers to escalate privileges and persist within the environment, posing a severe security risk.
Ping Sleep Batch Command
The following analytic identifies the use of ping commands as a delay or sleep mechanism within batch-style command execution. It leverages process creation telemetry from Endpoint Detection and Response (EDR) agents and examines process and parent process command-line fields for ping commands that specify a count and are chained with additional commands using command separators or redirection operators. While execution of the command may generate individual process creation events, such as a separate ping.exe process, this analytic specifically focuses on the original command string passed to a command interpreter, such as cmd.exe /c, that combines the ping-based delay with subsequent command execution. Adversaries may use ping as an alternative to explicit sleep or timeout commands to introduce execution delays, potentially evading automated analysis, sandboxing, or behavior-based detection. Because ping is commonly used for legitimate network troubleshooting, findings should be reviewed in the context of the complete command line, parent process, user, and commands executed before or after the delay.
PingID Mismatch Auth Source and Verification Response
The following analytic identifies discrepancies between the IP address of an authentication event and the IP address of the verification response event, focusing on differences in the originating countries. It leverages JSON logs from PingID, comparing the 'auth_Country' and 'verify_Country' fields. This activity is significant as it may indicate suspicious sign-in behavior, such as account compromise or unauthorized access attempts. If confirmed malicious, this could allow attackers to bypass authentication mechanisms, potentially leading to unauthorized access to sensitive systems and data.
PingID Multiple Failed MFA Requests For User
The following analytic identifies multiple failed multi-factor authentication (MFA) requests for a single user within a PingID environment. It triggers when 10 or more MFA prompts fail within 10 minutes, using JSON logs from PingID. This activity is significant as it may indicate an adversary attempting to bypass MFA by bombarding the user with repeated authentication requests. If confirmed malicious, this could lead to unauthorized access, as the user might eventually accept the fraudulent request, compromising the security of the account and potentially the entire network.
PingID New MFA Method After Credential Reset
The following analytic identifies the provisioning of a new MFA device shortly after a password reset. It detects this activity by correlating Windows Event Log events for password changes (EventID 4723, 4724) with PingID logs indicating device pairing. This behavior is significant as it may indicate a social engineering attack where a threat actor impersonates a valid user to reset credentials and add a new MFA device. If confirmed malicious, this activity could allow an attacker to gain persistent access to the compromised account, bypassing traditional security measures.
PingID New MFA Method Registered For User
The following analytic detects the registration of a new Multi-Factor Authentication (MFA) method for a PingID (PingOne) account. It leverages JSON logs from PingID, specifically looking for successful device pairing events. This activity is significant as adversaries who gain unauthorized access to a user account may register a new MFA method to maintain persistence. If confirmed malicious, this could allow attackers to bypass existing security measures, maintain long-term access, and potentially escalate their privileges within the compromised environment.
Plain HTTP POST Exfiltrated Data
The following analytic detects potential data exfiltration using plain HTTP POST requests. It leverages network traffic logs, specifically monitoring the `stream_http` data source for POST methods containing suspicious form data such as "wermgr.exe" or "svchost.exe". This activity is significant because it is commonly associated with malware like Trickbot, trojans, keyloggers, or APT adversaries, which use plain text HTTP POST requests to communicate with remote C2 servers. If confirmed malicious, this activity could lead to unauthorized data exfiltration, compromising sensitive information and potentially leading to further network infiltration.
Possible Browser Pass View Parameter
The following analytic identifies processes with command-line parameters associated with web browser credential dumping tools, specifically targeting behaviors used by Remcos RAT malware. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and specific file paths. This activity is significant as it indicates potential credential theft, a common tactic in broader cyber-espionage campaigns. If confirmed malicious, attackers could gain unauthorized access to sensitive web credentials, leading to further system compromise and data breaches.