Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects anomalous AI-assisted activity where multiple processes (specifically AI developer tools) appear to be chaining operations by writing to and reading from shared local directories (e.g., 'handoff', 'artifacts') followed by network requests to known AI service APIs. This pattern may indicate automated task chaining or unauthorized exfiltration of context/code to AI platforms.
The following analytic identifies instances where CrushFTP has blocked access due to exceeding the maximum number of simultaneous connections from a single IP address. This activity may indicate brute force attempts, credential stuffing, or automated attacks against the CrushFTP server. This detection is particularly relevant following the discovery of CVE-2025-31161, an authentication bypass vulnerability in CrushFTP versions 10.0.0 through 10.8.3 and 11.0.0 through 11.3.0.
The following analytic detects Log4Shell JNDI payload injections via outbound connections. It identifies suspicious LDAP lookup functions in web logs, such as `${jndi:ldap://PAYLOAD_INJECTED}`, and correlates them with network traffic to known malicious IP addresses. This detection leverages the Web and Network_Traffic data models in Splunk. Monitoring this activity is crucial as it targets vulnerabilities in Java web applications using log4j, potentially leading to remote code execution. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary code, and compromise sensitive data within the affected environment.
Detects AI agent tooling (e.g., Copilot, Cursor, Ollama) that makes outbound network calls to known AI model providers followed by the execution of suspicious shell commands within a short duration. The rule flags high-severity activities such as download-cradles, credential reconnaissance, LOLBin usage, and persistence mechanisms. Informational alerts are generated for standard CLI activity within the same timeframe.
Detects AI coding assistants and LLM agent frameworks attempting to access, read, or export sensitive credential files, registry hives, or application secrets. The rule monitors both file system operations on known secret locations and process execution patterns indicative of credential dumping or API token retrieval by these tools.
Detects AI coding assistants and LLM agent frameworks spawning shell processes or interpreters to execute potentially malicious commands. The rule monitors for suspicious activity often associated with download cradles, credential access, reconnaissance, or persistence, while excluding standard interactive terminal usage.
Detects the presence of PAYLOAD ransomware ELF binaries specifically configured to target VMware ESXi environments. The detection logic looks for a combination of ELF magic bytes, the 'PAYLOAD' string, references to common virtual machine file extensions (e.g., .vmdk, .vmx), and usage of ESXi-specific administration tools such as esxcli or vim-cmd.
Detects UNC6240 PSEMHUB.war web shell/tunnel/trojanized installer artifacts by distinctive content markers (x.jsp, u.jsp, tunnel.jsp/jspx, Ple64.exe)
Detects UNC6240 PSEMHUB.war web shell/tunnel/trojanized installer artifacts by distinctive content markers (x.jsp, u.jsp, tunnel.jsp/jspx, Ple64.exe)
Detects the execution of the 'fanout.sh' utility from the /tmp directory, which subsequently spawns 'sshpass' child processes to perform rapid, non-interactive SSH authentication attempts ('StrictHostKeyChecking=no') against multiple internal hosts within a 10-minute window. This behavior is indicative of automated lateral movement, specifically observed by threat actor UNC6240.
Detects Neo-reGeorg tunnel.jsp/tunnel.jspx servlets used by UNC6240 to establish SOCKS5-over-HTTP(S) tunneling from compromised PeopleSoft PSEMHUB.war hosts
Detects the initialization of known-malicious Terraform providers (dockerd) followed within one hour by the execution of a 'go run' process from the .terraform directory. This pattern is indicative of a supply chain compromise where an adversary leverages a typosquatted or malicious Terraform provider to facilitate the execution of a secondary payload.
Detects anomalous Telnet (TCP port 23) traffic associated with Mirai-variant botnet propagation, such as scanning for new targets or inbound compromise attempts, often following cPanel/WHM vulnerabilities.
This rule detects unauthorized or anomalous file modifications or creations to machine learning training datasets (e.g., fine-tuning data, training corpora) by users not belonging to the authorized data engineering group, occurring within one hour of a scheduled training or fine-tuning job execution. This behavioral pattern is indicative of potential data poisoning, where an adversary attempts to inject malicious data into the training set to bias or compromise the resulting model.
Detects network connection events associated with the download of machine learning model artifacts from public hubs that display indicators of malicious intent, such as unverified or newly created publisher accounts, missing or invalid cryptographic signatures, or the presence of embedded executable/pickle-deserialization payloads.
Detects network connection events associated with the download of machine learning model artifacts from public hubs that display indicators of malicious intent, such as unverified or newly created publisher accounts, missing or invalid cryptographic signatures, or the presence of embedded executable/pickle-deserialization payloads.
Detects the installation of machine learning or AI software dependencies (pip, conda, npm, poetry) that either pull from unauthorized registries or match known malicious/typosquatting naming patterns (e.g., LiteLLM supply-chain compromise). The rule also monitors for newly published packages that have been flagged as suspicious.
Detects an AI agent session that performs a data exfiltration action (e.g., sending an email, webhook, or file share) to an unauthorized or non-allowlisted destination shortly after the agent has ingested potentially untrusted external content. This behavior is indicative of an AI agent being manipulated to exfiltrate data after processing malicious or adversarial inputs.
Detects attempts by AI agent code-interpreter or sandbox processes to break out of their containerized environment. The rule monitors for the execution of common container escape techniques, such as namespace manipulation (nsenter, unshare), mounting host filesystems, accessing sensitive host devices like /dev/kmsg or /dev/mem, or referencing host-level paths from within an isolated AI agent execution process.
Experimental behavioral hunt for a Java process directly spawning a root-owned shell or selected command-line utility on explicitly inventoried ISE devices. Potential post-exploitation signal relevant to ISE Java RCE investigations. It does not prove Java deserialization or identify CVE-2026-20307/20211, and requires verified appliance process telemetry.
This rule monitors package installation logs for indicators of packages potentially generated or suggested by AI tools (e.g., Copilot, code assistants) being installed in a target environment. It specifically looks for a low volume of installations (<=3) for packages that have been published within the last 14 days, which is a pattern often associated with the 'Publish Hallucinated Entities' technique in AI systems, where malicious or hallucinated code packages are introduced into the supply chain.
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