Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects a specific multi-stage exfiltration pattern characterized by initial reconnaissance/fingerprinting probes against sensitive service endpoints (/health, /docs, /openapi.json) followed by unauthorized access to artifact storage endpoints (/files or /file?name=) from the same device against the same host within a short timeframe.
Detects a single Salesforce user or connected-app session performing an anomalous volume of SOQL queries against sensitive objects including Case, Account, Contact, and Opportunity. This behavior is indicative of mass data enumeration and potential exfiltration, as observed in historical incidents where attackers targeted CRM platforms to extract embedded secrets and sensitive record data.
Detects anomalous anti-forensic behavior where a Salesforce Bulk API query job is deleted shortly after its creation or completion. This sequence is often associated with efforts to obscure data exfiltration activities by removing the metadata trail of bulk data exports.
Detects anomalous Salesforce API export patterns where a user or connected application maintains a daily record export volume below typical burst thresholds, yet accumulates a high total volume over a rolling 7-day period. This pattern mimics 'low-and-slow' data exfiltration techniques used by actors like ShinyHunters to evade standard volume-based DLP alerts.
Detects anomalous large-scale data export operations in Google BigQuery (via jobs.insert or jobs.query) performed by identities (service accounts, API keys) not previously observed in the environment. This activity is indicative of credential abuse where stolen third-party analytics or monitoring service tokens are utilized for illicit data exfiltration, a behavior consistent with tactics employed by threat actors such as ShinyHunters.
Detects high-volume, anomalous data read operations performed by the Gainsight integration service account within Salesforce, which may indicate unauthorized use of the integration token for data exfiltration.
Detects anomalous, high-volume data access patterns within Salesforce, specifically monitoring for bulk API or SOQL query requests by a single user or application. This behavior is indicative of mass data exfiltration from a CRM instance, a tactic observed in extortion-themed campaigns such as those associated with the ShinyHunters group.
Detects high-volume database or CRM read activity performed by a user, followed by significant outbound network traffic to known public cloud storage, file-sharing, or messaging services. This pattern is consistent with data staging and exfiltration behaviors often attributed to the ShinyHunters threat actor group.
Detects high-volume data exports or bulk API queries against sensitive Salesforce CRM objects (Accounts, Contacts, Cases, Opportunities) performed by connected applications. This pattern is characteristic of exfiltration efforts where a newly authorized OAuth application is used to quickly extract large quantities of sensitive records.
Detects high-volume data exports or bulk API queries against sensitive Salesforce CRM objects (Accounts, Contacts, Cases, Opportunities) performed by connected applications. This pattern is characteristic of exfiltration efforts where a newly authorized OAuth application is used to quickly extract large quantities of sensitive records.
Detects high-volume, unauthenticated or anonymous requests (GetObject, ListObjects, ListObjectsV2) against AWS S3 buckets. This behavior is consistent with mass enumeration or scanning activities often associated with data exfiltration attempts against misconfigured publicly-exposed storage buckets.
Detects high-volume data transfers (exceeding 100MB) from internal sources to known external cloud storage and file-sharing platforms. This behavior is consistent with data staging and exfiltration patterns, often used by threat actors to move collected data to attacker-controlled storage infrastructure.
Detects Node.js or Python processes exhibiting suspicious behavior consistent with information-stealing malware (such as BeaverTail or InvisibleFerret). The rule monitors for these processes accessing sensitive files, including browser credentials, cookies, browser extension wallet settings, cryptocurrency wallet files, and various identification document image types, followed by the execution of archiving utilities (e.g., zip, rar, 7z) to stage the collected data for exfiltration.
Detects a specific behavioral chain associated with the CHOSEN BRICK implant. This rule identifies when a suspected malicious process writes image/screenshot files (.png, .jpg, .bmp) to disk, followed by a network connection to the Telegram Bot API within a 15-minute window, suggesting potential data exfiltration.
Detects a chained sequence of suspicious activity initiated by agentic AI development tools (e.g., Claude Code, Aider, AutoGen). The rule identifies the execution of these tools followed by local reconnaissance commands, lateral movement attempts, and outbound network connections originating from the same host, indicating potential automated exploitation or credential abuse.
Detects the use of legitimate data transfer utilities (s5cmd, rclone) configured to exfiltrate sensitive document types (.doc, .pdf, .sql, etc.) to S3-compatible cloud storage. The rule correlates this activity with recent access to files containing sensitive credentials or configuration data, a pattern observed in double-extortion campaigns.
Detects the use of legitimate data transfer utilities (s5cmd, rclone) configured to exfiltrate sensitive document types (.doc, .pdf, .sql, etc.) to S3-compatible cloud storage. The rule correlates this activity with recent access to files containing sensitive credentials or configuration data, a pattern observed in double-extortion campaigns.
Detects an suspicious progression of activities originating from identified AI assistant/agent processes. The rule monitors for a chain of behaviors occurring within a 15-minute window: execution of an AI agent/tool, followed by host discovery (network/system enumeration), access to sensitive local credential files, archival of data, and outbound network communication to common file-sharing/exfiltration platforms.
Detects an suspicious progression of activities originating from identified AI assistant/agent processes. The rule monitors for a chain of behaviors occurring within a 15-minute window: execution of an AI agent/tool, followed by host discovery (network/system enumeration), access to sensitive local credential files, archival of data, and outbound network communication to common file-sharing/exfiltration platforms.
Detects instances where AI-based coding or agent-driven tools (e.g., Cursor, Claude Code, AutoGPT) perform suspicious file archiving/packaging activities followed by network egress to unauthorized or known exfiltration-prone services. This helps identify potential data exfiltration by AI agents or compromised development environments.
Detects HTTP POST requests containing potential credential exfiltration indicators, specifically targeting the '/google.php' endpoint with 'do=login&', 'user=', and 'pass=' parameters. This pattern is associated with FlowerStorm (Storm-1167) AiTM phishing kits.
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