Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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BigBear 2.0 IOC HUNT (Gravwell)
Gravwell
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.
BigBear 2.0 IOC HUNT (Gravwell)
Gravwell
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.
BigBear 2.0 IOC HUNT (Gravwell)
Gravwell
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.
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.
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 detects attempts to exploit the LiteLLM proxy pass-through endpoint to interact with the cloud Instance Metadata Service (IMDS). Specifically, it identifies configuration attempts to route traffic to the IMDS IP (169.254.169.254) and subsequent requests to retrieve IAM security credentials, which is indicative of a Server-Side Request Forgery (SSRF) attack to steal cloud identity tokens.
This rule detects suspicious clipboard access attempts initiated by common scripting interpreters (node.exe, python.exe, powershell.exe) in non-standard paths or correlated with recent external network connections. It is designed to identify potential credential theft or data staging using clipboard interaction methods often employed by malware.
This rule detects suspicious clipboard access attempts initiated by common scripting interpreters (node.exe, python.exe, powershell.exe) in non-standard paths or correlated with recent external network connections. It is designed to identify potential credential theft or data staging using clipboard interaction methods often employed by malware.
Detects periodic outbound network connections (beaconing) to rare remote destinations initiated by processes running from non-standard or user-writable locations. The rule calculates the interval coefficient of variation (CV) between successful connections to identify regular, consistent patterns indicative of command and control communication while excluding known legitimate update and service traffic.
Detects periodic outbound network connections (beaconing) to rare remote destinations initiated by processes running from non-standard or user-writable locations. The rule calculates the interval coefficient of variation (CV) between successful connections to identify regular, consistent patterns indicative of command and control communication while excluding known legitimate update and service traffic.
Detects periodic outbound network connections (beaconing) to rare remote destinations initiated by processes running from non-standard or user-writable locations. The rule calculates the interval coefficient of variation (CV) between successful connections to identify regular, consistent patterns indicative of command and control communication while excluding known legitimate update and service traffic.
Detects browser processes (Chrome or Firefox) originating from specific file paths or extension IDs attempting to perform OAuth validation against the Twitch API. This behavior is indicative of potentially malicious browser extensions or extensions masquerading as legitimate tools attempting to handle user authentication tokens.
This rule monitors for network connections to known command-and-control (C2) IP addresses and the execution or presence of files matching known hashes associated with the 'PhantomC2' threat group or malware family. It correlates data from DeviceNetworkEvents, DeviceFileEvents, and DeviceProcessEvents to identify potential compromise.
Detects network communication with known Command and Control (C2) IP addresses associated with Phantom Stealer malware. This rule monitors both outbound connections from the internal network and inbound traffic from the identified malicious infrastructure.
Detects network communication with known Command and Control (C2) IP addresses associated with Phantom Stealer malware. This rule monitors both outbound connections from the internal network and inbound traffic from the identified malicious infrastructure.
Detects outbound network connections to 'registry.npmjs.org' originating from 'node' or 'bun' processes. This behavior may indicate an attempt to exfiltrate data, interact with malicious packages, or perform credential harvesting via npm tokens during execution.
Detects anomalous execution or network activity involving a 'content.js' file in conjunction with specific proxy-related artifacts or authorization headers. This pattern is indicative of a malicious browser extension or script attempting to intercept or proxy web traffic and credentials.
Detects network communication with identified C2 infrastructure associated with VectraRAT, Amadey, and ClickFix campaigns, as well as the presence of a specific 'callback.json' artifact in temporary directories, which is commonly used for C2 address overrides.
Detects network traffic on TCP port 3308 consistent with VectraRAT beaconing, utilizing MessagePack-over-TCP. The rule monitors for established connections to external networks with specific stream size and content characteristics typical of this C2 communication channel.
Detects HTTP GET requests to '/api.php?t=' containing a 13-digit timestamp identifier, followed by a response body containing 'download_link'. This pattern is indicative of the SilverFox malware relaying instructions or stage-two payload delivery.
Detects outbound network traffic associated with the Tailscale VPN protocol, specifically identifying potential unauthorized or personal tailnet communication channels. The detection monitors for TLS SNI strings ending in '.ts.net' and WireGuard handshakes on UDP port 41641. These indicators are common to all Tailscale client activity and require environment-specific exclusions of sanctioned Tailscale infrastructure to minimize noise.
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