Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule identifies a discrepancy between actual network connections processed by HAProxy and the corresponding entries in its server-side logs. Such inconsistencies can indicate the presence of a malicious backdoor compiled into or injected into the HAProxy process, designed to facilitate C2 traffic while evading standard monitoring by suppressing or omitting logging for those connections.
This rule detects potential exploitation or unauthorized interaction with an HAProxy service. It correlates process execution on Linux systems involving the HAProxy PID file with subsequent network traffic containing the suspicious string 'writeservice_info' within a one-hour window.
Detects the 'ted' backdoor or similar malicious activity attempting to manipulate HAProxy's internal connection counters by accessing or writing to the HAProxy process memory (/proc/[pid]/mem or /proc/self/mem). This behavior is characteristic of an attempt to evade backend logging and statistics tracking by directly modifying process memory structures.
Detects the creation or modification of files within the /tmp directory that follow a specific naming convention (t[number]_w.pipe) by an HAProxy process. This pattern is often associated with inter-process communication or potential post-exploitation activity where HAProxy is leveraged to handle named pipes for malicious command execution or data redirection.
This rule identifies a discrepancy between actual network connections processed by HAProxy and the corresponding entries in its server-side logs. Such inconsistencies can indicate the presence of a malicious backdoor compiled into or injected into the HAProxy process, designed to facilitate C2 traffic while evading standard monitoring by suppressing or omitting logging for those connections.
Detects the presence of a malicious HAProxy binary (version 2.8.12) that has been recompiled with an embedded 'ted' plugin. The plugin is designed to intercept and manipulate web traffic, as evidenced by specific strings related to pipe file handling, logging, and registration operations found within the binary.
Detects the 'ted' backdoor or similar malicious activity attempting to manipulate HAProxy's internal connection counters by accessing or writing to the HAProxy process memory (/proc/[pid]/mem or /proc/self/mem). This behavior is characteristic of an attempt to evade backend logging and statistics tracking by directly modifying process memory structures.
This rule detects potential exploitation or unauthorized interaction with an HAProxy service. It correlates process execution on Linux systems involving the HAProxy PID file with subsequent network traffic containing the suspicious string 'writeservice_info' within a one-hour window.
This rule detects potential brute-force or credential stuffing attempts against web application authentication endpoints by monitoring patterns of success and failure. It aggregates requests over 5-minute intervals, calculating a risk score based on high volumes of authentication failures relative to successful requests from individual source IPs targeting sensitive API paths (e.g., auth, session, login, token).
This rule detects attempts to perform Large Language Model (LLM) prompt injection via HTTP requests. It monitors proxy traffic for common jailbreaking or adversarial instruction patterns such as 'ignore previous instructions', 'bypass restrictions', or 'fictional environment' within URI parameters or request bodies. Such patterns are indicative of an attacker attempting to manipulate the behavior of an AI/LLM application.
This rule detects potential brute-force or credential stuffing attempts against web application authentication endpoints by monitoring patterns of success and failure. It aggregates requests over 5-minute intervals, calculating a risk score based on high volumes of authentication failures relative to successful requests from individual source IPs targeting sensitive API paths (e.g., auth, session, login, token).
This rule detects potential brute-force or credential stuffing attempts against web application authentication endpoints by monitoring patterns of success and failure. It aggregates requests over 5-minute intervals, calculating a risk score based on high volumes of authentication failures relative to successful requests from individual source IPs targeting sensitive API paths (e.g., auth, session, login, token).
This rule detects HTTP/1.1 requests containing an Upgrade header (specifically targeting WebSocket upgrades) accompanied by a non-zero Content-Length header. This pattern is characteristic of a request smuggling primitive targeting the nghttpx reverse proxy, where the provided body may be misinterpreted by the backend as a separate, smuggled request due to improper request queue handling.
Detects unauthorized HTTP requests to Gitea containing an 'X-WEBAUTH-USER' header set to 'admin'. This targets CVE-2026-20896, where a misconfigured reverse proxy trust setting allows external users to spoof authentication and gain administrative privileges.
This rule monitors web traffic logs for requests targeting administrative URI paths or containing the username 'admin' in the 'X-WEBAUTH-USER' header. It specifically filters out requests originating from trusted reverse proxies, identifying potential attempts to bypass authentication by manually injecting administrative headers.
Detects Server-Side Request Forgery (SSRF) attempts directed at the Google Cloud Platform (GCP) instance metadata service endpoints. The rule monitors HTTP request logs from proxies or web servers, identifying requests that contain suspicious metadata hostnames or IP addresses combined with sensitive API paths typically used for exfiltrating cloud instance identity tokens.
Detects one web session posting username, password, and MFA or one-time-code form fields through the same intermediary host. This behavior is indicative of an Adversary-in-the-Middle (AiTM) attack, where an attacker intercepts and relays authentication requests, including multi-factor authentication (MFA) codes, to gain unauthorized access.
Detects signs of HTTP request smuggling attempts targeting nghttpx or similar proxies. The rule monitors for a specific poisoning precondition where an HTTP/1.1 Upgrade request contains both an 'Upgrade' header and a 'Content-Length' header, which can be leveraged to desynchronize the connection and smuggle subsequent requests (e.g., GET /poisoned).
Detects a single host serving materially different status codes, body sizes, or redirects to automated user agents (bots, crawlers, scanners, curl, wget, headless browsers, python, go-http) compared to normal browsers. This behavior can indicate an Adversary-in-the-Middle (AiTM) attack where the adversary is attempting to serve different content to different user agents to evade detection or deliver malicious payloads.
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.
This rule detects attempts to perform Large Language Model (LLM) prompt injection via HTTP requests. It monitors proxy traffic for common jailbreaking or adversarial instruction patterns such as 'ignore previous instructions', 'bypass restrictions', or 'fictional environment' within URI parameters or request bodies. Such patterns are indicative of an attacker attempting to manipulate the behavior of an AI/LLM application.
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