Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects potential sensitive data leakage from an AI model response (AIGatewayLogs_CL). The rule monitors AI responses for patterns indicative of PII (SSN, credit card numbers), secrets (API keys, tokens), or sensitive document markers. It correlates these signals with RAG (Retrieval-Augmented Generation) ingestion logs to identify data cross-tenant violations or improper access to confidential/restricted datasets.
This rule detects anomalous behavior in AI agent tool invocations, specifically monitoring for high volumes of tool calls, high costs associated with token consumption, or suspicious tool interactions following a flagged indirect prompt injection. This behavior is indicative of potential resource exhaustion (denial of service) or abusive agent behavior as defined by the MITRE ATLAS framework for AI security.
Detects potential sensitive data leakage from an AI model response (AIGatewayLogs_CL). The rule monitors AI responses for patterns indicative of PII (SSN, credit card numbers), secrets (API keys, tokens), or sensitive document markers. It correlates these signals with RAG (Retrieval-Augmented Generation) ingestion logs to identify data cross-tenant violations or improper access to confidential/restricted datasets.
Detects autonomous AI agents triggered by routine, non-interactive workflows (such as scheduled tasks, tickets, or calendar invites) that subsequently invoke sensitive tools. The rule correlates these invocations with content hashes previously flagged as containing malicious indirect prompt injections, indicating an attack where a payload remains dormant until processed by an automated agent.
Detects potential sensitive data leakage from an AI model response (AIGatewayLogs_CL). The rule monitors AI responses for patterns indicative of PII (SSN, credit card numbers), secrets (API keys, tokens), or sensitive document markers. It correlates these signals with RAG (Retrieval-Augmented Generation) ingestion logs to identify data cross-tenant violations or improper access to confidential/restricted datasets.
Detects autonomous AI agents triggered by routine, non-interactive workflows (such as scheduled tasks, tickets, or calendar invites) that subsequently invoke sensitive tools. The rule correlates these invocations with content hashes previously flagged as containing malicious indirect prompt injections, indicating an attack where a payload remains dormant until processed by an automated agent.
Detects the execution of suspicious child processes (e.g., cmd.exe, powershell.exe, bash, wget) spawned by processes associated with public-facing appliances like Fortinet SSL-VPN, Veeam Backup & Replication, Citrix ADC, and cPanel/WHM. This behavior is often indicative of exploitation of public-facing applications (T1190) for initial access, frequently associated with ransomware actors targeting unpatched infrastructure.
Detects the execution of suspicious child processes (e.g., cmd.exe, powershell.exe, bash, wget) spawned by processes associated with public-facing appliances like Fortinet SSL-VPN, Veeam Backup & Replication, Citrix ADC, and cPanel/WHM. This behavior is often indicative of exploitation of public-facing applications (T1190) for initial access, frequently associated with ransomware actors targeting unpatched infrastructure.
Detects the creation of new administrator or technician accounts within the SimpleHelp Remote Monitoring and Management (RMM) platform via API calls. This activity is often associated with unauthorized persistence creation by an adversary who has gained access to the platform.
Detects rapid termination of specific mobile messenger applications (Max) within seconds of starting, while indicators of the Frida dynamic instrumentation tool are concurrently detected on the device. This pattern suggests the application is identifying the presence of a hooking/instrumentation environment and self-terminating as a defense mechanism. The detection is further correlated with failed connection attempts to the application's backend.
Detects the use of 'terraform init' where the command line arguments reference known typosquatted HashiCorp registry domains. This behavior is indicative of a supply chain attack attempting to pull malicious providers or modules from an adversary-controlled source.
This rule monitors application, build system, and dependency scanning logs for references to known-malicious versions of the @memtensor/memos-cloud-openclaw-plugin (npm) and MemoryOS (PyPI) packages. These versions contain the sckit Go worm and are associated with automated supply chain attacks involving the reuse of stolen credentials, indicated by the 'exact-ref-one-use-NPM_TOKEN' marker.
Detects JavaScript loaders associated with the Exvicy/ErrTraffic campaign. The rule identifies obfuscated code that utilizes atob() for Base64 decoding, single-byte XOR operations, TextDecoder for content processing, and dynamic code execution via 'new Function()'. This pattern is commonly used to inject malicious payloads into compromised WordPress sites.
Detects JavaScript code within HTML pages designed to block browser developer tools (F12, Inspect, View Source) using keydown event listeners. This technique is commonly associated with ClickFix-style phishing campaigns to prevent users or security analysts from inspecting malicious page content or fake CAPTCHA overlays.
Detects a specific SharePoint markup injection technique targeting the EditingPageParser type-check mechanism. An attacker smuggles a 'Register' directive into a crafted WebPartMarkup POST request using the 'publishingribbon' Tagprefix to bypass SafeControl validation.
Detects malicious prompt injection payloads targeting AI agent frameworks, specifically designed to bypass Semantic Kernel's filter blocklist. The payload utilizes Python reflection mechanisms by crawling the type hierarchy (starting from a tuple) to locate and invoke 'BuiltinImporter.load_module()', effectively bypassing filters that look for direct references to forbidden modules or functions like '__import__', 'eval', or 'os'. This allows for indirect arbitrary command execution within the agent's application context.
Detects suspicious/unexpected usernames appearing in Cisco ISE access.log,
consistent with exploitation of CVE-2026-76460, an unauthenticated REST API
auth-bypass in Cisco ISE / ISE-PIC (CVSS 10.0, actively exploited, CISA KEV).
Cisco's own guidance is to hunt access.log for usernames not present in the
legitimate identity store; "dummyuser" is their published example artifact.
consistent with exploitation of CVE-2026-76460, an unauthenticated REST API
auth-bypass in Cisco ISE / ISE-PIC (CVSS 10.0, actively exploited, CISA KEV).
Cisco's own guidance is to hunt access.log for usernames not present in the
legitimate identity store; "dummyuser" is their published example artifact.
This rule monitors cloud application events for high-frequency, automated API calls directed at AI/ML inference endpoints. A high volume of queries (over 1000) accessing a diverse range of objects (over 50) within a short window (60 minutes) by a single identity or IP address is flagged. Such behavior is characteristic of model extraction or distillation attacks, where an adversary probes an AI model to replicate its capabilities or internal parameters.
Detects the presence of prompt-injection framing and malicious intent instructions within the first few lines of source code files. These strings are designed to deceive LLM-based SAST or code review tools into ignoring, misclassifying, or bypassing security analysis of malicious code.
Detects specific Android device admin command sequences that indicate attempts to establish persistent access by hardening the device against removal (e.g., enabling uninstall protection, setting device admin passwords, disabling biometrics) or destructive actions like performing a factory reset or self-uninstallation.
Detects malicious software bundles consistent with the Contagious Interview (G1052) threat group's multi-stage infection chain. The rule identifies the presence of multiple malware family identifiers (such as BeaverTail, InvisibleFerret, and others) within the context of npm or VS Code task configuration files, which are commonly used in job-assessment themed social engineering campaigns.
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