Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects a suspicious sequence of Git operations ('fetch' followed by 'checkout') executed within a 5-minute window, where the initiating process is a known AI-assisted development tool (Claude, Codex, Copilot, or Gemini). This behavioral pattern may indicate an attempt to use AI tooling to facilitate the unauthorized access or exfiltration of code repositories.
This rule detects potentially unauthorized network communication with AI platforms (OpenAI, Anthropic, Gemini) or Telegram from hosts executing suspicious processes or tools, such as 'frpc' (Fast Reverse Proxy), 'liblocal-service.so', or processes interacting with Android Debug Bridge (adb) utilities. This behavior often suggests the use of automated tools, remote tunneling, or interaction with AI models for post-exploitation tasks, such as code generation for malicious scripts or C2 activity.
Detects the use of database or archiving command-line utilities to export data related to RADIUS services. The rule filters out known backup service accounts and authorized backup processes, flagging activity that occurs outside of standard business hours or is performed by accounts not explicitly designated for administrative or database maintenance tasks.
Detects execution of the JITTERLY Linux backdoor masquerading as 'configd' (with its -nodel fallback flag) and/or beaconing to its hardcoded C2 domain (s2.981666.xyz:8082), matching Red Heron's observed tradecraft.
Detects files containing specific structural footers (537 bytes) and RSA key generation strings commonly associated with ransomware encryption. The footer indicates the use of RSA-4096 to encrypt files, with modes for full, striped, or header-only encryption patterns.
Detects DNS resolution requests for graph.microsoft.com initiated by a python interpreter (python.exe, python3.exe, or pythonw.exe). This activity may indicate a script or custom tool communicating with Microsoft Graph API, which is frequently used for data collection, exfiltration, or cloud service interaction in adversarial campaigns.
Detects DNS resolution requests to 'graph.microsoft.com' on endpoint devices. This is intended to act as a precursor indicator for potential Microsoft 365 or Azure environment enumeration and discovery activities, which would typically involve subsequent API calls.
Detects the forged Next.js Server Action invocation used to deliver the
RCE payload after the encryption key has been disclosed. The request is
a multipart/form-data POST containing a "$ACTION_REF_*" field together
with the numbered "$ACTION_<ref>:0/1/2" descriptor fields (action id,
argument list, base64-encoded AES-GCM ciphertext for the closure-bound
args) plus one additional attacker-controlled form field carrying the
payload. Legitimate Server Action calls originate only from the app's
own client bundle and do not normally arrive as raw multipart posts
crafted with a generic HTTP client / no browser fingerprint, especially
shortly after a manifest-disclosure request from the same source (see
companion rule "Next.js Server-Reference-Manifest Disclosure via
Backslash Path Traversal").
RCE payload after the encryption key has been disclosed. The request is
a multipart/form-data POST containing a "$ACTION_REF_*" field together
with the numbered "$ACTION_<ref>:0/1/2" descriptor fields (action id,
argument list, base64-encoded AES-GCM ciphertext for the closure-bound
args) plus one additional attacker-controlled form field carrying the
payload. Legitimate Server Action calls originate only from the app's
own client bundle and do not normally arrive as raw multipart posts
crafted with a generic HTTP client / no browser fingerprint, especially
shortly after a manifest-disclosure request from the same source (see
companion rule "Next.js Server-Reference-Manifest Disclosure via
Backslash Path Traversal").
This rule monitors network connections, file events, and process execution command lines for references to a list of known malicious URLs, including indicators associated with ClearFake, IClickFix, AMOS, Remus, and Mozi botnet payloads.
This rule detects internal network activity (DNS queries, network connections, and security logs) communicating with domains associated with the GhostCode phishing kit. These domains are typically used to host credential harvesting pages or phishing infrastructure.
This rule detects shell processes (sh or bash) being spawned from suspicious, commonly writable directories (e.g., /tmp, /dev/shm) within a short window of file system activity (creation, modification, or deletion) and associated MQTT protocol network communication. This pattern is indicative of potential malware staging or command-and-control activity on Linux systems.
Detects the creation of files or folders containing suspicious command-line parameters associated with tar or Parallels toolchain injection, specifically looking for the 'use-compress-program' flag which can be abused for command injection.
Detects the 'prl_disp_service' process spawning 'tar' with the '--use-compress-program' flag set to an unconventional binary. This behavior is associated with the exploitation of ParaShells (CVE-2026-90894), where an attacker leverages the compression program hook in 'tar' to execute arbitrary commands with root privileges.
Detects interaction with known infrastructure associated with the PREY-0058 campaign, which utilizes Adversary-in-the-Middle (AiTM) phishing lures mimicking passkey or MFA registration pages to target Microsoft 365 and other SaaS applications.
Detects automated searching (via grep, findstr, or select-string) for sensitive strings (e.g., API keys, private keys, wallet data) across multiple occurrences on a single host, or the creation of suspicious sensitive files (e.g., .env, credentials, wallet.dat) on devices where such automated sweeping activity has been observed. This pattern indicates an adversary staging data for exfiltration.
Detects automated searching (via grep, findstr, or select-string) for sensitive strings (e.g., API keys, private keys, wallet data) across multiple occurrences on a single host, or the creation of suspicious sensitive files (e.g., .env, credentials, wallet.dat) on devices where such automated sweeping activity has been observed. This pattern indicates an adversary staging data for exfiltration.
Detects unauthorized access and data retrieval attempts from a MinIO object storage instance using default administration credentials (minioadmin:minioadmin). The rule monitors both command-line execution of MinIO client (mc) operations and network connections to MinIO service ports (9000/9001) that involve the use of the default credential pair.
Detects unauthorized access and data retrieval attempts from a MinIO object storage instance using default administration credentials (minioadmin:minioadmin). The rule monitors both command-line execution of MinIO client (mc) operations and network connections to MinIO service ports (9000/9001) that involve the use of the default credential pair.
Detects suspicious process execution patterns originating from Langflow or Nacos AI orchestration services, including the spawning of sensitive binaries like cmd, powershell, or scripts (python/bash/sh) with suspicious command-line arguments. Additionally, it identifies potential exploitation attempts targeting known-vulnerable API endpoints (code validation and Nacos user authentication).
CloudSEK researchers uncovered BigBear 2.0, a global Microsoft 365 phishing-as-a-service operation targeting hundreds of organizations across 40+ countries. The investigation exposed the attacker’s admin panel, affiliate network, phishing infrastructure, and thousands of stolen credentials and session cookies, revealing how modern AiTM attacks can hijack authenticated sessions even after MFA.
This rule monitors for interactions with known malicious files (by SHA256 hash), connections to confirmed C2 infrastructure (specific IP address and domains), or requests to known malicious URLs. It aggregates indicators across file, process, network, and web browsing telemetry to identify potential threats or compromised systems communicating with known attacker infrastructure.
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