Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects outbound HTTP POST requests destined for 'aware-cr1.com' targeting the URI '/api/beacon', which is indicative of command-and-control (C2) communication associated with the 2CLoader malware.
This rule detects HTTP GET requests to a specific IP address (62.60.226.185) attempting to download a file named 't0907.exe'. This pattern is associated with the delivery phase of the 2CLoader malware.
Detects incoming HTTP POST requests to the 'diffpatch' endpoint in Gitea that contain suspicious 'diff --git' patterns combined with path traversal sequences ('../') targeting 'hooks/' directories. This indicates an attempt to exploit CVE-2026-60004 to achieve unauthorized file system access or remote code execution via repository hooks.
This rule detects HTTP GET requests to known malicious domains or URI patterns associated with fake video-conferencing lures. These lures redirect users to download RMM (Remote Monitoring and Management) MSI installers, often masquerading as legitimate software like Adobe Reader or video conferencing tools, as part of a social engineering campaign for initial access.
Detects HTTP POST requests to Telegram Bot API and Discord webhooks containing large, Base64-encoded strings within request bodies, which may indicate exfiltration of reconnaissance data or sensitive information.
Detects HTTP POST requests directed at common Large Language Model (LLM) chat completion and completion endpoints that are not associated with authorized OpenAI or Azure OpenAI services. This behavior may indicate an attempt to exfiltrate potentially sensitive corporate or proprietary data to third-party or unauthorized Shadow-AI platforms.
Detects incoming HTTP POST requests to internal AI/LLM API endpoints containing common prompt injection strings, such as instructions to ignore previous system prompts or attempts to trigger jailbreak modes like 'DAN'.
Detects attempts to exploit a vulnerability (CVE-2026-68536) in the Apache MyFaces JSF resource handler. The rule monitors for malicious URI parameters ('ln' or 'library') that attempt Server-Side Request Forgery (SSRF) targeting cloud metadata services, path traversal, or URI-based injection attacks.
This rule detects attempts to exploit CVE-2026-93647, a Stored Cross-Site Scripting (XSS) vulnerability in the Zimbra Classic Webclient. The exploit involves injecting malicious script tags or JavaScript event handlers into calendar fields such as SUMMARY, DESCRIPTION, or LOCATION. The detection monitors inbound TCP traffic on web ports used by the application, specifically looking for common XSS injection patterns within calendar-related fields.
Detects potential SQL injection attempts targeting Cisco AsyncOS Email Gateways via the SMTP DATA command. The rule monitors for malicious patterns, specifically those attempting to execute unauthorized commands or database queries (such as COPY TO PROGRAM or embedded SQL statements) within SMTP traffic.
Detects attempts to exploit an unauthenticated OS command injection vulnerability in Cisco ISE (CVE-2026-20130). The rule monitors inbound HTTP traffic for shell metacharacters and common command-line binaries in the URI or request body, indicating an attempt to execute arbitrary commands on the underlying host.
Detects network traffic patterns characteristic of the EternalBlue (MS17-010) exploit targeting the SMBv1 protocol. Specifically, it monitors for a malformed SESSION_SETUP request containing an unusually large payload, which is indicative of an attempt to trigger a buffer overflow in the SMB service.
Detects network requests to the domain 'cloud-ack.workers.dev', which is known to be used as a redirector for hosting Microsoft authentication lookalike pages to conduct phishing attacks.
Detects unauthenticated network requests targeting MLflow Model Registry and Ajax API endpoints on port 5000. These requests are characteristic of unauthorized reconnaissance against exposed machine learning management services.
Detects unauthorized attempts to access exposed Chroma or Weaviate vector database APIs via standard HTTP requests (GET/POST) targeting known API endpoints for collections or objects. This may indicate an adversary conducting reconnaissance on publicly accessible database infrastructure.
Detects a high volume of HTTP POST requests to common login endpoints (e.g., /login or /wp-login.php) from a single source IP address within a short time frame, which is indicative of a credential stuffing or automated brute-force attack.
This rule detects potential command and control (C2) beaconing activity by monitoring for high-frequency POST requests to OpenAI and Google Generative Language chat completion API endpoints. The rule triggers when a source host makes 5 or more requests within a 60-second window, which may indicate automated polling for commands or exfiltrated data transfer using legitimate LLM infrastructure.
Detects a potential automated exploit sweep or brute-force attempt, identified by a high frequency of HTTP requests (GET or POST) directed to the same URI from a single source IP address within a short time frame.
Detects inbound HTTP POST requests directed at common paths associated with self-hosted OpenAI-compatible LLM inference APIs, such as LocalAI or vLLM. This rule identifies potentially unauthorized or unintended public exposure of internal LLM infrastructure, which could lead to unauthorized model usage, information disclosure, or exploitation of the inference engine.
This analytic detects intrusion events from known threat activity using Cisco Secure Firewall Intrusion Events.
It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where one or multiple Snort signatures
associated with a known threat or threat actor activity have been triggered within a one-hour time window. The detection uses a
lookup table (cisco_snort_ids_to_threat_mapping) to map Snort signature IDs to known threat actors and their techniques.
When multiple signatures associated with the same threat actor are triggered within the time window, and the count of
unique signatures matches or exceeds the expected number of signatures for that threat technique, an alert is generated.
This helps identify potential coordinated threat activity in your network environment by correlating related intrusion
events that occur in close temporal proximity.
Currently, this detection will alert on the following threat actors or malware families as defined in the cisco_snort_ids_to_threat_mapping lookup:
* AgentTesla
* Amadey
* ArcaneDoor
* AsyncRAT
* CastleRAT
* Chafer
* DCRAT
* LokiBot
* Lumma Stealer
* Nobelium
* Quasar
* Remcos
* Snake
* Static Tundra
* Xworm
To add or update threat actors, update the cisco_snort_ids_to_threat_mapping.csv lookup file with new or modified threat names and associated Snort signature IDs.
It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where one or multiple Snort signatures
associated with a known threat or threat actor activity have been triggered within a one-hour time window. The detection uses a
lookup table (cisco_snort_ids_to_threat_mapping) to map Snort signature IDs to known threat actors and their techniques.
When multiple signatures associated with the same threat actor are triggered within the time window, and the count of
unique signatures matches or exceeds the expected number of signatures for that threat technique, an alert is generated.
This helps identify potential coordinated threat activity in your network environment by correlating related intrusion
events that occur in close temporal proximity.
Currently, this detection will alert on the following threat actors or malware families as defined in the cisco_snort_ids_to_threat_mapping lookup:
* AgentTesla
* Amadey
* ArcaneDoor
* AsyncRAT
* CastleRAT
* Chafer
* DCRAT
* LokiBot
* Lumma Stealer
* Nobelium
* Quasar
* Remcos
* Snake
* Static Tundra
* Xworm
To add or update threat actors, update the cisco_snort_ids_to_threat_mapping.csv lookup file with new or modified threat names and associated Snort signature IDs.
Detects outbound network traffic from internal hosts to the domain 'skyleen.fr' over TLS/HTTPS, which is characteristic of credential exfiltration associated with the Sckit supply chain worm.
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