Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
7,129 detections
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Detects the first-ever observed network connection from a device to WhatsApp Web or Telegram Web within a 30-day lookback period. This behavior is used to identify potentially unauthorized companion-device linking to a user's messenger account, which could indicate credential theft or unauthorized access.
Detects a sequential pattern where a browser process (Chrome, Edge, or Safari) accesses an AI chat platform's conversation API, followed within 5 minutes by a network connection to a known unauthorized exfiltration destination (api.pbapi.xyz). This pattern suggests the potential use of a browser-based tool or extension (e.g., Poper Blocker or similar) to intercept and exfiltrate AI chat history.
Detects indicators of compromise associated with malicious Firefox wallet-clone extensions (Rabby/OKX clones) by checking for known file hashes, extension file names in Firefox profile directories, and network connections to known C2 domains and workers.dev subdomains.
Detects potential remote shell behavior where a process named libgcrypt establishes an outbound connection on TCP port 9999 and spawns a shell (such as sh, bash, dash, zsh, or ash) within a short time window.
Detects an exploitation attempt against PoeLLM or LiteLLM MCP test/connection endpoint using a python3 stdio payload that fetches data from port 81.
Detects network connections to known Indicators of Compromise (IOCs) associated with the TraderTraitor campaign, including trojanized Terraform providers (FLATROOF/ROOFDECK malware families) using specific domains and URL paths.
Sweeps Defender XDR telemetry for known NEBULA / KNTRAT indicators (SHA256 hashes, C2 IP, decoy domain and URLs, burner publisher emails). Hash matching is scoped per table (execution hash only in process events), noisy tooling is excluded from command-line matches, network hits are limited to connection events, and results are aggregated per device and IOC.
Sweeps Defender XDR telemetry for known NEBULA / KNTRAT indicators (SHA256 hashes, C2 IP, decoy domain and URLs, burner publisher emails). Hash matching is scoped per table (execution hash only in process events), noisy tooling is excluded from command-line matches, network hits are limited to connection events, and results are aggregated per device and IOC.
Detects endpoint connections to known DarkSword/Coruna exploit-host and C2 infrastructure. The shared AWS IP is only matched on C2 ports 8881/8882 to reduce collateral hits.
Detects endpoint connections to known DarkSword/Coruna exploit-host and C2 infrastructure. The shared AWS IP is only matched on C2 ports 8881/8882 to reduce collateral hits.
Sweeps network connection, HTTP/proxy, file, and process telemetry for exact matches against known Cling botnet indicators: dedicated STUN C2/operator IPs, Cling MIPS loader staging URLs, and known Cling sample SHA256 hashes. Tightened to exclude broad domain-level matching on stun.l.google.com (legitimate, ubiquitous Google STUN infrastructure that Cling spoofs/abuses rather than owns) and to match payload-staging hosts only by full URL rather than bare IP, reducing false positives from shared/compromised hosting and unrelated legitimate STUN traffic.
Sweeps network connection, HTTP/proxy, file, and process telemetry for exact matches against known Cling botnet indicators: dedicated STUN C2/operator IPs, Cling MIPS loader staging URLs, and known Cling sample SHA256 hashes. Tightened to exclude broad domain-level matching on stun.l.google.com (legitimate, ubiquitous Google STUN infrastructure that Cling spoofs/abuses rather than owns) and to match payload-staging hosts only by full URL rather than bare IP, reducing false positives from shared/compromised hosting and unrelated legitimate STUN traffic.
Detects the execution of suspicious child processes (shells, interpreters, or system reconnaissance tools) directly spawned by critical Citrix NetScaler service binaries (nsppe, httpd, nsaaad). This behavior is indicative of post-exploitation activity, such as command injection or lateral movement following the exploitation of a public-facing NetScaler application vulnerability.
This rule monitors NetScaler ADC logs for indicators associated with the PitScaler campaign, specifically targeting exploitation of CVE-2026-88771 and CVE-2026-88772. It detects exploitation artifacts (DTLS handshake errors, NSPPE crashes), deployment of WHIPSHOT/SLAPSHOT malware, web shell installation, httpd.conf configuration tampering, persistence mechanisms involving privilege escalation (chmod), and connections to known malicious infrastructure.
Detects an unauthenticated remote code execution attempt against AhsayCBS via the UpdateReceivers.do endpoint using the checkSysPwd parameter, associated with CVE-2026-105133 and CVE-2026-105134.
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 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.
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