Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
7,276 detections
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Detects SSH persistence planted during the Red Heron campaign: authorized_keys file writes under a .ssh folder, correlated with process command lines referencing authorized_keys and the operator's observed key comment naming pattern (backdoor / root-shell).
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 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 HTTP POST requests to a Gitea repository's diffpatch API endpoint whose body contains both a real git diff header and the malicious 'hooks/post-index-change' file path used by the CVE-2026-60004 exploit chain to plant an executable Git hook via an add/add merge collision.
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.
Detects successful authentication attempts to a LiteLLM proxy instance using the hardcoded default master key 'sk-1234'. This activity indicates potential exploitation of misconfigured instances where default credentials remain unchanged.
Detects instances where LiteLLM, or related Python application processes (uvicorn, python), spawn a shell or command-line utility (bash, sh, id) under the root user account. This behavior is indicative of potential container breakout, application exploitation, or privilege escalation attempts originating from a web-accessible service.
Detects instances where LiteLLM, or related Python application processes (uvicorn, python), spawn a shell or command-line utility (bash, sh, id) under the root user account. This behavior is indicative of potential container breakout, application exploitation, or privilege escalation attempts originating from a web-accessible service.
Detects requests to a LiteLLM proxy that have been assigned the 'PROXY_ADMIN' role despite the absence of valid authentication credentials (Authorization headers, JWT tokens, or OAuth2 tokens). This pattern typically indicates that the LiteLLM instance is misconfigured, specifically that a 'master_key' is missing, causing the proxy to default to a permissive mode where every request is treated as having administrative privileges.
Detects requests to a LiteLLM proxy that have been assigned the 'PROXY_ADMIN' role despite the absence of valid authentication credentials (Authorization headers, JWT tokens, or OAuth2 tokens). This pattern typically indicates that the LiteLLM instance is misconfigured, specifically that a 'master_key' is missing, causing the proxy to default to a permissive mode where every request is treated as having administrative privileges.
Detects requests to a LiteLLM proxy that have been assigned the 'PROXY_ADMIN' role despite the absence of valid authentication credentials (Authorization headers, JWT tokens, or OAuth2 tokens). This pattern typically indicates that the LiteLLM instance is misconfigured, specifically that a 'master_key' is missing, causing the proxy to default to a permissive mode where every request is treated as having administrative privileges.
Detects instances where a LiteLLM proxy is utilized to invoke sensitive Model Context Protocol (MCP) tools (e.g., database, github, slack, jira, ci/cd) while authentication is bypassed ('allow_all_keys=true') and no explicit API key is provided. This indicates a potential unauthorized access attempt to sensitive integrations via a misconfigured proxy.
Detects instances where a LiteLLM proxy is utilized to invoke sensitive Model Context Protocol (MCP) tools (e.g., database, github, slack, jira, ci/cd) while authentication is bypassed ('allow_all_keys=true') and no explicit API key is provided. This indicates a potential unauthorized access attempt to sensitive integrations via a misconfigured proxy.
Detects instances where a LiteLLM proxy is utilized to invoke sensitive Model Context Protocol (MCP) tools (e.g., database, github, slack, jira, ci/cd) while authentication is bypassed ('allow_all_keys=true') and no explicit API key is provided. This indicates a potential unauthorized access attempt to sensitive integrations via a misconfigured proxy.
Detects instances where a LiteLLM proxy is utilized to invoke sensitive Model Context Protocol (MCP) tools (e.g., database, github, slack, jira, ci/cd) while authentication is bypassed ('allow_all_keys=true') and no explicit API key is provided. This indicates a potential unauthorized access attempt to sensitive integrations via a misconfigured proxy.
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.
This rule detects potential Server-Side Request Forgery (SSRF) activity where web application or runtime processes attempt to access the Cloud Instance Metadata Service (IMDS) or container task metadata endpoints. By monitoring network connections and application logs, the rule filters out known legitimate metadata clients and identifies suspicious processes frequently associated with web-based vulnerabilities that are repeatedly querying sensitive metadata paths.
Detects text/report artifacts (markdown, JSON, plain text) produced by an autonomous AI-driven vulnerability research pipeline that decompiles binaries, traces cross-references, hypothesizes memory-safety flaws, and generates/debugs proof-of-concept exploits
Detects anomalous activity patterns consistent with automated firmware reverse engineering pipelines, specifically correlating the high-frequency execution of firmware analysis tools (e.g., binwalk, Ghidra) with the creation of vulnerability research knowledge base artifacts. This behavioral heuristic aims to identify potential AI-driven or automated zero-day discovery workflows by tracking tool usage density and output characteristics.
Detects the initialization of containerized LLM-based gateway services (e.g., litellm, ollama) followed closely in time by high-volume network traffic containing suspicious web injection payloads (e.g., SQLi, XSS, SSRF). The rule correlates process execution events for container launches with network activity originating from the same device to identify potential unauthorized or unsupervised automated exploitation.
This rule detects unauthorized or suspicious automated scraping behavior by identifying periodic, non-interactive tasks that perform high-volume outbound network requests to multiple external hosts. It specifically filters for processes launched via scheduling mechanisms (Task Scheduler, cron) that exhibit indicators of anti-bot or proxy-rotation bypass techniques (such as usage of headless browser tools or proxy-related keywords in command lines). The rule is tuned to ignore legitimate system maintenance, update, and monitoring processes.
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