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detections/application/splunk_information_disclosure_in_splunk_add_on_builder.yml
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| name: Splunk Information Disclosure in Splunk Add-on Builder | ||
| id: b7b82980-4a3e-412e-8661-4531d8758735 | ||
| version: 1 | ||
| date: '2024-01-30' | ||
| author: Rod Soto, Eric McGinnis | ||
| status: production | ||
| type: Hunting | ||
| data_source: [] | ||
| description: In Splunk Add-on Builder versions below 4.1.4, the application writes sensitive information to its internal log files when you visit the Splunk Add-on Builder or when you build or edit a custom app or add-on. | ||
| search: '| rest /services/apps/local | search disabled=0 core=0 label="Splunk Add-on Builder" | dedup label | search version < 4.1.4 | ||
| | eval WarningMessage="Splunk Add-on Builder Versions older than v4.1.4 contain a critical vulnerability. Update to Splunk Add-on Builder v4.1.4 or higher immediately. For more information about this vulnerability, please refer to https://advisory.splunk.com/advisories/SVD-2024-0111" | ||
| | table label version WarningMessage | `splunk_information_disclosure_in_splunk_add_on_builder_filter`' | ||
| how_to_implement: This search should be run on search heads where Splunk Add-on Builder may be installed. The results of this search will conclusively show whether or not a vulnerable version of Splunk Add-on Builder is currently installed. | ||
| known_false_positives: This search is highly specific for vulnerable versions of Splunk Add-on Builder. There are no known false positives. | ||
| references: | ||
| - https://advisory.splunk.com/advisories/SVD-2024-0111 | ||
| tags: | ||
| analytic_story: | ||
| - Splunk Vulnerabilities | ||
| asset_type: Splunk Server | ||
| confidence: 100 | ||
| impact: 100 | ||
| message: Vulnerable $version$ of Splunk Add-on Builder found - Upgrade Immediately. | ||
| mitre_attack_id: | ||
| - T1082 | ||
| observable: | ||
| - name: version | ||
| type: Other | ||
| role: | ||
| - Other | ||
| product: | ||
| - Splunk Enterprise | ||
| risk_score: 100 | ||
| required_fields: | ||
| - disabled | ||
| - core | ||
| - version | ||
| - label | ||
| security_domain: endpoint | ||
| manual_test: This search uses a REST call against a running Splunk instance to fetch the versions of installed apps. | ||
| It cannot be replicated with a normal test or attack data. | ||
|
|
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82
detections/cloud/kubernetes_anomalous_inbound_network_activity_from_process.yml
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| name: Kubernetes Anomalous Inbound Network Activity from Process | ||
| id: 10442d8b-0701-4c25-911d-d67b906e713c | ||
| version: 1 | ||
| date: '2024-01-10' | ||
| author: Matthew Moore, Splunk | ||
| status: experimental | ||
| type: Anomaly | ||
| description: 'This detection detects inbound network traffic volume anomalies from processes running within containerised workloads. | ||
| Anomalies are provided with context identifying the Kubernetes cluster, the workload name, and the type of anomaly.This detection | ||
| leverages Network performance Monitoring metrics harvested using an OTEL collector, and is pulled from Splunk Observability cloud | ||
| using the Splunk Infrastructure Monitoring Add-on. (https://splunkbase.splunk.com/app/5247). This detection compares the tcp.bytes, | ||
| tcp.new_sockets, tcp.packets, udp.bytes, udp.packets metrics for destination (receiving) workload process pairs over the last 1 hour, | ||
| with the average of those metrics for those pairs over the last 30 days in order to detect any anonymously high inbound network activity. | ||
| Anomalies in inbound network traffic may suggest that the container is receiving unexpected or unauthorized data, potentially indicative of a breach, | ||
| a vulnerability exploitation attempt, an attempt to overload the service, or propagation of malware. Successful compromise of a containerised | ||
| application resulting in the ability to upload data, can result in installation of command and control software or other malware, | ||
| data integrity damage, container escape, and further compromise of the environment. Additionally this kind of activity may result in | ||
| resource contention, performance degradation and disruption to the normal operation of the environment.' | ||
| data_source: [] | ||
| search: '| mstats avg(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name dest.workload.name dest.process.name span=10s | ||
| | eval key=''dest.workload.name'' + ":" + ''dest.process.name'' | ||
| | join type=left key | ||
| [ mstats avg(tcp.*) as avg_tcp.* avg(udp.*) as avg_udp.* stdev(tcp.*) as stdev_tcp.* avg(udp.*) as stdev_udp.* where `kubernetes_metrics` AND earliest=-30d latest=-1h by dest.workload.name dest.process.name | ||
| | eval key=''dest.workload.name'' + ":" + ''dest.process.name'' | ||
| ] | ||
| | eval anomalies = "" | ||
| | foreach stdev_* | ||
| [ eval anomalies =if( ''<<MATCHSTR>>'' > (''avg_<<MATCHSTR>>'' + 3 * ''stdev_<<MATCHSTR>>''), anomalies + "<<MATCHSTR>> higher than average by " + | ||
| tostring(round((''<<MATCHSTR>>'' - ''avg_<<MATCHSTR>>'')/''stdev_<<MATCHSTR>>'' ,2)) + " Standard Deviations. <<MATCHSTR>>=" + tostring(''<<MATCHSTR>>'') + " avg_<<MATCHSTR>>=" | ||
| + tostring(''avg_<<MATCHSTR>>'') + " ''stdev_<<MATCHSTR>>''=" + tostring(''stdev_<<MATCHSTR>>'') + ", " | ||
| , anomalies) | ||
| ] | ||
| | fillnull | ||
| | eval anomalies = split(replace(anomalies, ",\s$$$$", "") ,", ") | ||
| | where anomalies!="" | ||
| | stats count(anomalies) as count values(anomalies) as anomalies by k8s.cluster.name dest.workload.name dest.process.name | ||
| | where count > 5 | ||
| | rename k8s.cluster.name as host | ||
| | `kubernetes_anomalous_inbound_network_activity_from_process_filter` ' | ||
| how_to_implement: 'To gather NPM metrics the Open Telemetry to the Kubernetes Cluster and | ||
| enable Network Performance Monitoring according to instructions found in Splunk Docs | ||
| https://docs.splunk.com/observability/en/infrastructure/network-explorer/network-explorer-setup.html#network-explorer-setup | ||
| In order to access those metrics from within Splunk Enterprise and ES, the Splunk Infrastructure Monitoring add-on must be installed and | ||
| configured on a Splunk Search Head. Once installed, first configure the add-on with your O11y Cloud Org ID and Access Token. | ||
| Lastly set up the add-on to ingest metrics from O11y cloud using the following settings, and any other settings left at default:\ | ||
| * Name sim_npm_metrics_to_metrics_index \ | ||
| * Org ID <Your O11y Cloud Org Id> \ | ||
| * Signal Flow Program data(''tcp.packets'').publish(label=''A''); data(''tcp.bytes'').publish(label=''B''); data(''tcp.new_sockets'').publish(label=''C''); data(''udp.packets'').publish(label=''D''); data(''udp.bytes'').publish(label=''E'') \ | ||
| * Metric Resolution 10000' | ||
| known_false_positives: unknown | ||
| references: | ||
| - https://github.com/signalfx/splunk-otel-collector-chart | ||
| tags: | ||
| analytic_story: | ||
| - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring | ||
| asset_type: Kubernetes | ||
| confidence: 50 | ||
| impact: 50 | ||
| message: Kubernetes Anomalous Inbound Network Activity from Process in kubernetes cluster $host$ | ||
| mitre_attack_id: | ||
| - T1204 | ||
| observable: | ||
| - name: host | ||
| type: Hostname | ||
| role: | ||
| - Attacker | ||
| product: | ||
| - Splunk Enterprise | ||
| - Splunk Enterprise Security | ||
| - Splunk Cloud | ||
| required_fields: | ||
| - tcp.* | ||
| - udp.* | ||
| - k8s.cluster.name | ||
| - dest.process.name | ||
| - dest.workload.name | ||
| risk_score: 25 | ||
| security_domain: network |
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82
detections/cloud/kubernetes_anomalous_outbound_network_activity_from_process.yml
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,82 @@ | ||
| name: Kubernetes Anomalous Outbound Network Activity from Process | ||
| id: dd6afee6-e0a3-4028-a089-f47dd2842c22 | ||
| version: 1 | ||
| date: '2024-01-10' | ||
| author: Matthew Moore, Splunk | ||
| status: experimental | ||
| type: Anomaly | ||
| description: 'This detection detects outbound network traffic volume anomalies from processes running within containerised workloads. | ||
| Anomalies are provided with context identifying the Kubernetes cluster, the workload name, and the type of anomaly. This detection | ||
| leverages Network performance Monitoring metrics harvested using an OTEL collector, and is pulled from Splunk Observability cloud | ||
| using the Splunk Infrastructure Monitoring Add-on. (https://splunkbase.splunk.com/app/5247). This detection compares the tcp.bytes, | ||
| tcp.new_sockets, tcp.packets, udp.bytes, udp.packets metrics for source (transmitting) workload process pairs over the last 1 hout, | ||
| with the average of those metrics for those pairs over the last 30 days in order to detect any anonymously high outbound network activity. | ||
| Anonymously high outbound network traffic from a process running in a container is a potential indication of data exfiltration, or an indication that the process has been modified. | ||
| Anomalously high outbound network activity from a process running within a container suggests the potential compromise, which may lead to unauthorized data exfiltration, | ||
| communication with malicious entities, or the propagation of malware to external systems. The compromised container could also serve as a pivot point | ||
| for further attacks within the containerized environment.' | ||
| data_source: [] | ||
| search: '| mstats avg(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name source.workload.name source.process.name span=10s | ||
| | eval key=''source.workload.name'' + ":" + ''source.process.name'' | ||
| | join type=left key | ||
| [ mstats avg(tcp.*) as avg_tcp.* avg(udp.*) as avg_udp.* stdev(tcp.*) as stdev_tcp.* avg(udp.*) as stdev_udp.* where `kubernetes_metrics` AND earliest=-30d latest=-1h by source.workload.name source.process.name | ||
| | eval key=''source.workload.name'' + ":" + ''source.process.name'' | ||
| ] | ||
| | eval anomalies = "" | ||
| | foreach stdev_* | ||
| [ eval anomalies =if( ''<<MATCHSTR>>'' > (''avg_<<MATCHSTR>>'' + 3 * ''stdev_<<MATCHSTR>>''), anomalies + "<<MATCHSTR>> higher than average by " + | ||
| tostring(round((''<<MATCHSTR>>'' - ''avg_<<MATCHSTR>>'')/''stdev_<<MATCHSTR>>'' ,2)) + " Standard Deviations. <<MATCHSTR>>=" + tostring(''<<MATCHSTR>>'') + " avg_<<MATCHSTR>>=" | ||
| + tostring(''avg_<<MATCHSTR>>'') + " ''stdev_<<MATCHSTR>>''=" + tostring(''stdev_<<MATCHSTR>>'') + ", " | ||
| , anomalies) | ||
| ] | ||
| | fillnull | ||
| | eval anomalies = split(replace(anomalies, ",\s$$$$", "") ,", ") | ||
| | where anomalies!="" | ||
| | stats count(anomalies) as count values(anomalies) as anomalies by k8s.cluster.name source.workload.name source.process.name | ||
| | where count > 5 | ||
| | rename k8s.cluster.name as host | ||
| | `kubernetes_anomalous_outbound_network_activity_from_process_filter` ' | ||
| how_to_implement: 'To gather NPM metrics the Open Telemetry to the Kubernetes Cluster and | ||
| enable Network Performance Monitoring according to instructions found in Splunk Docs | ||
| https://docs.splunk.com/observability/en/infrastructure/network-explorer/network-explorer-setup.html#network-explorer-setup | ||
| In order to access those metrics from within Splunk Enterprise and ES, the Splunk Infrastructure Monitoring add-on must be installed and | ||
| configured on a Splunk Search Head. Once installed, first configure the add-on with your O11y Cloud Org ID and Access Token. | ||
| Lastly set up the add-on to ingest metrics from O11y cloud using the following settings, and any other settings left at default:\ | ||
| * Name sim_npm_metrics_to_metrics_index \ | ||
| * Org ID <Your O11y Cloud Org Id> \ | ||
| * Signal Flow Program data(''tcp.packets'').publish(label=''A''); data(''tcp.bytes'').publish(label=''B''); data(''tcp.new_sockets'').publish(label=''C''); data(''udp.packets'').publish(label=''D''); data(''udp.bytes'').publish(label=''E'') \ | ||
| * Metric Resolution 10000' | ||
| known_false_positives: unknown | ||
| references: | ||
| - https://github.com/signalfx/splunk-otel-collector-chart | ||
| tags: | ||
| analytic_story: | ||
| - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring | ||
| asset_type: Kubernetes | ||
| confidence: 50 | ||
| impact: 50 | ||
| message: Kubernetes Anomalous Outbound Network Activity from Process in kubernetes cluster $host$ | ||
| mitre_attack_id: | ||
| - T1204 | ||
| observable: | ||
| - name: host | ||
| type: Hostname | ||
| role: | ||
| - Attacker | ||
| product: | ||
| - Splunk Enterprise | ||
| - Splunk Enterprise Security | ||
| - Splunk Cloud | ||
| required_fields: | ||
| - tcp.* | ||
| - udp.* | ||
| - k8s.cluster.name | ||
| - source.workload.name | ||
| - dest.workload.name | ||
| - udp.packets | ||
| risk_score: 25 | ||
| security_domain: network |
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