Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects high Ollama API request volumes from a single client in a short time window. A burst may indicate automated abuse or contribute to resource contention, but GIN access logs do not establish malicious intent or service impact; validate the client and workload against local usage baselines.
Identifies repeated Ollama inference requests with long response times on /api/generate, /api/chat, and /v1/chat/completions.
Long response time can result from model size, prompt length, host resources, or normal workload. GIN access logs do not include prompt content and cannot establish a prompt injection or jailbreak attempt, so use this as an exploratory performance signal.
Long response time can result from model size, prompt length, host resources, or normal workload. GIN access logs do not include prompt content and cannot establish a prompt injection or jailbreak attempt, so use this as an exploratory performance signal.
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.
Identifies high Ollama runner counts and large model buffer allocations as possible resource pressure indicators. Memory values vary with model size and hardware, and these operational logs do not establish malicious intent; validate against host capacity and workload baselines.
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.
This rule monitors for the presence of the PoperBlocker browser extension or network traffic directed to associated domains, which are often used by potentially unwanted programs (PUP) or adware.
This rule detects the installation of browser extensions that request highly sensitive, over-broad permissions such as proxy configuration management and web authentication provider access, combined with full host access (<all_urls>). This pattern is often indicative of malicious extensions masquerading as legitimate tools (e.g., VPNs) to facilitate credential theft or traffic interception.
Detects M365 Copilot impersonation and roleplay jailbreak attempts where users try to manipulate the AI into adopting alternate personas, behaving as unrestricted entities, or impersonating malicious AI systems to bypass safety controls. The detection searches exported eDiscovery prompt logs for roleplay keywords like "pretend you are," "act as," "you are now," "amoral," and "roleplay as" in the Subject_Title field. Prompts are categorized into specific impersonation types (AI_Impersonation, Malicious_AI_Persona, Unrestricted_AI_Persona, etc.) to identify attempts to override the AI's safety guardrails through persona injection attacks.
Detects browser extension updates that request a combination of high-risk permissions capable of account takeover (e.g., cookies, webRequest, identity) for extensions previously classified as low-risk. This pattern often indicates a supply chain compromise where a benign extension is acquired and subsequently updated with malicious capabilities.
This rule monitors for the presence of the PoperBlocker browser extension or network traffic directed to associated domains, which are often used by potentially unwanted programs (PUP) or adware.
This rule monitors for the presence of the PoperBlocker browser extension or network traffic directed to associated domains, which are often used by potentially unwanted programs (PUP) or adware.
Detects an SSL_HANDSHAKE_FAILURE syslog event on a Citrix NetScaler appliance characterized by a DTLSv1.0 client version and a 'Handshake failure-Internal Error' reason. This pattern is indicative of exploitation attempts against CVE-2026-88772, specifically associated with the WHIPSHOT/SLAPSHOT campaign. This is a high-sensitivity precursor signal that should be correlated with system crash events on the appliance for increased confidence.
This rule monitors the software inventory for vulnerable versions of OpenSSL and WolfSSL libraries. Specifically, it flags instances of WolfSSL prior to version 5.9.4, which are susceptible to peer-authentication bypass vulnerabilities. The rule is intended for patch management and asset exposure tracking rather than detecting active exploitation.
This rule monitors for the presence of the PoperBlocker browser extension or network traffic directed to associated domains, which are often used by potentially unwanted programs (PUP) or adware.
This rule identifies MikroTik devices running vulnerable versions of RouterOS (specifically those older than 7.24) that are susceptible to CVE-2026-84411. This vulnerability involves an integer underflow in the web management HTTP handling, which can lead to unauthenticated remote code execution or denial of service.
Detects LLM application prompts containing instruction-override or role-reassignment keywords combined with evidence of successful system prompt modification or abnormal downstream behavior (e.g., disclosure of internal instructions or sensitive data).
This rule monitors for high-impact configuration API calls and the creation of new administrator accounts within the Cisco Secure Firewall Management Center (FMC). These actions are indicative of post-exploitation activity related to CVE-2026-20079, which allows for authentication bypass. The detection focuses on sensitive administrative actions such as policy deployment, certificate exports, and user account creation which, if performed without valid session context, suggest an unauthorized actor.
The following analytic detects attempts to bypass application restrictions by identifying Windows AppLocker policy violations. It leverages Windows AppLocker event logs, specifically EventCodes 8007, 8004, 8022, 8025, 8029, and 8040, to pinpoint blocked actions. This activity is significant for a SOC as it highlights potential unauthorized application executions, which could indicate malicious intent or policy circumvention. If confirmed malicious, this activity could allow an attacker to execute unauthorized applications, potentially leading to further system compromise or data exfiltration.
This detection searches for ASR audit events that are generated when a process or application attempts to perform an action that would be blocked by an ASR rule, but is allowed to proceed for auditing purposes.
This rule detects a suspicious sequence of events where a user performs an unusual device-code authentication (potentially indicative of device code phishing) followed shortly by an OAuth application consent or permission grant event. It flags potential compromise if the sign-in is new for the user or occurs in a short timeframe, with optional indicators associated with the 'maillive.sbs' phishing infrastructure.
This rule monitors for the presence of the PoperBlocker browser extension or network traffic directed to associated domains, which are often used by potentially unwanted programs (PUP) or adware.
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