Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
960 detections
Filters
Last updated
All Time
Detection languages
215
211
197
168
63
Contributors
283
142
113
112
85
Categories
670
218
168
112
95
Platforms
39,915
7,245
6,529
3,864
3,627
960
Products / Services
713
68
64
59
59
MITRE Techniques
310
305
146
136
134
CVEs
5
3
3
2
1
IDS Classtypes
13
11
11
9
8
IDS Protocols
62
1
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 rapid, sequential identity-validation API calls (such as GetCallerIdentity, GetSessionToken, or AssumeRole) performed across a large number of distinct AWS access keys from a single source within a short time window. This pattern is indicative of automated tooling validating a harvested corpus of AWS access keys to identify active credentials.
Detects high-frequency S3 object upload activity (PutObject/CompleteMultipartUpload) originating from the Rclone tool, a common indicator of potential data exfiltration to cloud storage.
Detects attempts to bypass AWS IMDSv2 security protections via the LiteLLM /config/pass_through_endpoint, where attackers inject the 'x-pass-X-aws-ec2-metadata-token' header to potentially strip authentication requirements.
Detects attempts to bypass AWS IMDSv2 security protections via the LiteLLM /config/pass_through_endpoint, where attackers inject the 'x-pass-X-aws-ec2-metadata-token' header to potentially strip authentication requirements.
Detects the creation of a new AWS S3 bucket followed by a large volume of data uploads (more than 200 objects) within a 2-hour window. The detection focuses on activity attributed to a single identity with limited source IP diversity, which is highly indicative of scripted data staging and potential exfiltration preparation by an unauthorized actor.
Detects the creation of a new AWS S3 bucket followed by a large volume of data uploads (more than 200 objects) within a 2-hour window. The detection focuses on activity attributed to a single identity with limited source IP diversity, which is highly indicative of scripted data staging and potential exfiltration preparation by an unauthorized actor.
Detects the creation of a new AWS S3 bucket followed by a large volume of data uploads (more than 200 objects) within a 2-hour window. The detection focuses on activity attributed to a single identity with limited source IP diversity, which is highly indicative of scripted data staging and potential exfiltration preparation by an unauthorized actor.
Detects the creation of new AWS access keys or login profiles for a target identity immediately following the deletion or modification of a credential for the same identity, where the actors performing the two actions are different. This pattern indicates an adversary creating persistent access after a security incident responder or automated system has revoked a previously compromised credential.
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.
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.
KQL Query from file: cve-2026-19516-ssrf-exploitation-attempt-against-mcp-grafana-100.kql
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.
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 various event types (email, identity authentication, network connections, and cloud audit logs) originating from or interacting with the specific IP address range 160.79.104.0/22 to 160.79.111.0/22, which is associated with known malicious activity.
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 high-frequency S3 object upload activity (PutObject/CompleteMultipartUpload) originating from the Rclone tool, a common indicator of potential data exfiltration to cloud storage.
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 correlates email sender activity with network, cloud, and identity logs by matching traffic against a specific IP range (160.79.104.0/21) identified as malicious or suspicious. The rule flags instances where this range appears as an email sender IP or as a remote/source IP address across various infrastructure indices, potentially indicating adversary communication or exfiltration.
Detects Server-Side Request Forgery (SSRF) attempts targeting an MLflow server where an attacker-controlled HTTP redirect response forces the server to reach out to the cloud instance metadata service at 169.254.169.254. This rule specifically looks for HTTP 30x redirect status codes where the Location header contains the sensitive metadata service IP address.
Detects Server-Side Request Forgery (SSRF) attempts targeting an MLflow server where an attacker-controlled HTTP redirect response forces the server to reach out to the cloud instance metadata service at 169.254.169.254. This rule specifically looks for HTTP 30x redirect status codes where the Location header contains the sensitive metadata service IP address.
Page 16 of 46