Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects potential exploitation attempts related to the Squidbleed vulnerability by monitoring 'squid.exe' or processes with 'squid' in their command line for AddressSanitizer related keywords indicating memory safety issues like overflow, underflow, or use-after-free.
Detects potential exploitation attempts related to the Squidbleed vulnerability by monitoring 'squid.exe' process command lines for indicators of ASAN (AddressSanitizer) heap overflow reports. This includes keywords like '==', 'ERROR', 'ERROR SUMMARY', and 'SUMMARY', which are often present in ASAN crash reports.
This rule detects potential exploitation of the Squidbleed vulnerability by identifying network connections to FTP port 21 where the remote URL contains 'PASV' or 'EPSV' commands, and the initiating process is 'squid.exe'. This indicates an attempt to abuse FTP passive mode through the Squid proxy.
This rule detects potential exploitation attempts related to the Squidbleed vulnerability, specifically looking for command-line arguments indicative of heap buffer overread conditions within the 'squid.exe' process. It monitors for keywords such as 'strchr', 'buffer', 'overread', 'parse', 'directory', and 'listing' in the command line.
Detects network connections originating from a Squid proxy process (squid.exe or processes containing 'squid' in their name) attempting to connect to an FTP server (port 21). This could indicate a reachability test or other activity related to the Squidbleed vulnerability, or general proxy misuse.
Detects FTP protocol traffic (on port 21) initiated by a process named 'squid.exe' or containing 'squid' in its filename. This rule is designed to identify potential exploitation attempts related to the Squidbleed vulnerability, where a compromised Squid proxy might be used for unauthorized FTP communications.
Detects a single host making Zulip REST API POST requests to multiple distinct topics, specifically looking for patterns associated with system profiling and command/tasking as observed in ZiChatBot. The rule identifies activity where a source IP uses the Zulip API to send messages to at least two different topics, with the raw request content indicating either 'channel/stream' and 'topic/subject' or keywords like 'system info', 'shellcode', 'task', or 'command'.
Detects HTTP GET requests to api.adblock-for-youtube.com to retrieve scriptlet rules. This endpoint is used by various browser extensions to fetch dynamic configuration updates, which can potentially facilitate MAIN-world JavaScript injection.
Detects a web session that proxies an enterprise authorisation endpoint while also handling session-cookie names associated with authenticated access. This rule identifies suspicious activity indicative of an Adversary-in-the-Middle (AiTM) attack where an attacker proxies authentication requests and steals session cookies.
Detects HTTP requests to the SaaS platforms 'holabrief.com' or 'loopedin.io' that contain common URL redirection parameters, characteristic of an open-redirect abuse tactic used in phishing campaigns. These platforms are leveraged as intermediate hops to obfuscate the final destination of phishing links. The rule identifies the initial request and suggests correlation with subsequent traffic to unknown destinations to confirm a two-hop redirection chain.
Detects many unique, high-entropy URL paths that each receive a small number of requests before redirecting to authentication. This behavior is indicative of adversaries like Volt Typhoon (Tycoon) using one-time session paths for each victim in phishing campaigns.
Detects network events where a remote URL contains repeated null bytes (\0 or \x00) on port 21, which could indicate an attempt to exploit the Squidbleed vulnerability or similar memory dump patterns.
Detects outbound network connections from 'squid.exe' to internal IP addresses on port 21 (FTP). This could indicate an attacker controlling an FTP server within the internal network, potentially exploiting a vulnerability like Squidbleed.
This rule monitors network logs (from proxies and firewalls) for web traffic (HTTP GET requests) containing suspicious JavaScript code injection patterns in the request context or message fields. It specifically looks for common XSS indicators such as <script tags, eval(), document.write(), fromCharCode(), and atob().
Detects attempts to exploit the Squidbleed vulnerability by monitoring 'squid.exe' process command lines for keywords related to FTP default configuration exploitation, specifically 'Safe_ports', 'CONNECT', 'PORT', 'PASV', '21', and 'tcp_outgoing_address'. This indicates an adversary trying to bypass security controls or exfiltrate data via FTP.
Fraud framework powering scam domains for crypto and gambling schemes
Fraud framework powering scam domains for crypto and gambling schemes
This rule detects successful HTTP requests (status 200, 201, 202) directed towards management interfaces (Redfish, CIMC, OSS APIs) by unauthenticated or anonymous users originating from outside the internal network. The rule calculates a risk level based on the variety and frequency of requested paths, flagging potential unauthorized enumeration or exploitation attempts against server infrastructure.
This rule detects successful HTTP requests (status 200, 201, 202) directed towards management interfaces (Redfish, CIMC, OSS APIs) by unauthenticated or anonymous users originating from outside the internal network. The rule calculates a risk level based on the variety and frequency of requested paths, flagging potential unauthorized enumeration or exploitation attempts against server infrastructure.
This rule detects processes other than standard web browsers performing network connections that coincide with OAuth token acquisition activities (POST requests containing 'access_token' in the response body). This behavior is indicative of potential token theft or unauthorized programmatic access to OAuth-protected resources.
This rule identifies potential Command and Control (C2) beaconing activity by detecting recurring, consistent HTTP(S) requests from internal hosts to unknown or newly seen external destinations, characterized by stable time intervals and low variance in response sizes. The detection logic filters out common User-Agents, indicating non-browser or automated traffic often associated with malware communication.
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