The Spear Phishing Detection pipeline container image contains a compiled Morpheus pipeline that is designed to act as a Postfix SMTP e-mail content filter and mock e-mail generator. This container image is part of the Spear Phishing Detection AI Workflow.

This image can be run in one of two modes: Pipeline or Mock E-mail Generator.

Inference

In Inference mode, the pipeline will listen for SMTP e-mail from a separate Postfix container, enrich the e-mail with results from intention classification & per-sender sketch history, calculate a phishing-likliness score, and then return the mail to Postfix for delivery to the next-hop SMTP server. This is typically started with the following arguments:

python phishing_inference_smtp.py --intent money --intent banking --intent crypto --model_name $PHISHING_MODEL_NAME --sender_sketch_db_driver_name postgresql+psycopg2 --sender_sketch_db_host $PHISHING_SENDER_SKETCH_DB_HOST --sender_sketch_db_password $PHISHING_SENDER_SKETCH_DB_PASSWORD --sender_sketch_db_port $PHISHING_SENDER_SKETCH_DB_PORT --sender_sketch_db_username $PHISHING_SENDER_SKETCH_DB_USERNAME --sender_sketch_db_database_name $PHISHING_SENDER_SKETCH_DB_DATABASE_NAME --smtp_filter_port 10025 --smtp_sub_port 10026 --smtp_sub_host $PHISHING_SMTP_SUB_HOST --tracking_uri $PHISHING_TRACKING_URI --smtp_source_queue_retry_delay $PHISHING_SMTP_SOURCE_QUEUE_RETRY_DELAY --smtp_source_queue_max_retries $PHISHING_SMTP_SOURCE_QUEUE_MAX_RETRIES --smtp_source_queue_max_size $PHISHING_SMTP_SOURCE_QUEUE_MAX_SIZE

Mock E-mail Generator

The mock e-mail generator mode will read from a provided CSV file of e-mails (body,arrival_time,sender,intents,label,source,extra_info,subject,gen_ai_subject). The generator is controlled using environment variables:

python ./scripts/gen_emails.py

DESTINATION_ADDRESS: the e-mail address to send the mails to
EMAIL_SET: the path to the e-mail dataset CSV file
MAX_RATE: the rate (in e-mails per second) to send mails (can be float, e.g. 0.1 for 1 e-mail per 10 seconds)
SMTP_HOST: the mail server to receive the mail

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This container image is part of the Spear Phishing Detection AI Workflow.\n\nThis image can be run in one of two modes: Pipeline or Mock E-mail Generator.\n\nInference\n\nIn Inference mode, the pipeline will listen for SMTP e-mail from a separate Postfix container, enrich the e-mail with results from intention classification & per-sender sketch history, calculate a phishing-likliness score, and then return the mail to Postfix for delivery to the next-hop SMTP server. This is typically started with the following arguments:\n\npython phishing_inference_smtp.py --intent money --intent banking --intent crypto --model_name $PHISHING_MODEL_NAME --sender_sketch_db_driver_name postgresql+psycopg2 --sender_sketch_db_host $PHISHING_SENDER_SKETCH_DB_HOST --sender_sketch_db_password $PHISHING_SENDER_SKETCH_DB_PASSWORD --sender_sketch_db_port $PHISHING_SENDER_SKETCH_DB_PORT --sender_sketch_db_username $PHISHING_SENDER_SKETCH_DB_USERNAME --sender_sketch_db_database_name $PHISHING_SENDER_SKETCH_DB_DATABASE_NAME --smtp_filter_port 10025 --smtp_sub_port 10026 --smtp_sub_host $PHISHING_SMTP_SUB_HOST --tracking_uri $PHISHING_TRACKING_URI --smtp_source_queue_retry_delay $PHISHING_SMTP_SOURCE_QUEUE_RETRY_DELAY --smtp_source_queue_max_retries $PHISHING_SMTP_SOURCE_QUEUE_MAX_RETRIES --smtp_source_queue_max_size $PHISHING_SMTP_SOURCE_QUEUE_MAX_SIZE\n\nMock E-mail Generator\n\nThe mock e-mail generator mode will read from a provided CSV file of e-mails (body,arrival_time,sender,intents,label,source,extra_info,subject,gen_ai_subject). The generator is controlled using environment variables:\n\npython ./scripts/gen_emails.py\n\n- DESTINATION_ADDRESS: the e-mail address to send the mails to\n- EMAIL_SET: the path to the e-mail dataset CSV file\n- MAX_RATE: the rate (in e-mails per second) to send mails (can be float, e.g. 0.1 for 1 e-mail per 10 seconds)\n- SMTP_HOST: the mail server to receive the mail","Spear Phishing Pipeline",["D",1706657221456],[188],"/api/image/nvaie/-/containers/cybersecurity-sp-pipeline",12801576828,"hasSignedTag",[],[],[],[258],[],{"_92":259,"_124":259,"_125":260,"_127":261},"signed images",{"_129":261,"_124":259},"system",{"_156":1241,"_158":1242},"ngcApps",[1194],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1175":1176,"_1177":1178,"_1179":1180,"_1181":1182,"_1183":273,"_187":297,"_1184":1185,"_1186":51,"_1187":135,"_1188":135,"_1189":1190,"_1191":135,"_1192":1193},"amplitude",{"_1172":1173,"_812":-7,"_1174":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1166":1167,"_1168":1169,"_1170":1171},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1052":379,"_1053":1054,"_1055":1056,"_1057":1058},"announcements",[],"consentState",{"_336":1042,"_1043":26,"_1044":26,"_1045":135,"_1046":1047},"consentConfig",{"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337},"showContactAdmin",1785277551303,[],[],[311,312,313,314,315,316,317],{"_40":62,"_42":67,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":62,"_42":65,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":62,"_42":63,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":54,"_50":51},{"_40":41,"_42":43,"_44":64,"_46":-5,"_48":-5,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. 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