Ibrahim Saud
@tektrixIndiaCompletionist
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11,679 detections
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This rule detects potential Business Email Compromise (BEC) attempts where external senders impersonate corporate executives (e.g., CEO, CFO) by spoofing display names or using lookalike/cousin domains. It triggers when these impersonations are combined with urgent, financial-themed language in the email subject line (e.g., wire transfers, payroll changes, or gift card requests).
Detects coordinated credential stuffing campaigns originating from shared IP infrastructure. The rule identifies patterns where multiple distinct accounts are targeted with failed sign-in attempts, characterized by rapidly rotating user-agent/device fingerprints while remaining under standard per-account and per-IP throttling limits. It focuses on the combination of high unique account counts, rotating user-agents, and sustained activity over a specific time window.
Detects unauthorized write or modification operations against machine learning training data, feature stores, and model registries. The rule identifies suspicious activity by flagging actions occurring outside established change windows, attempts by identities with no prior history of modifying the resource, or bulk data modifications that could indicate training data poisoning or model tampering.
This rule detects potential prompt injection and jailbreak attempts targeting internal Generative AI applications by monitoring prompt content for known adversarial patterns (e.g., DAN roleplay, hypothetical bypasses, and explicit disregard for safety instructions). The rule tracks repeat offenders or successful bypass attempts (where the application did not block the request).
Detects the transmission of sensitive credential-like patterns (such as API keys, private keys, and connection strings) to known external AI coding assistant and IDE plugin endpoints. This rule monitors cloud application events originating from developer workstations or CI/CD runners where such sensitive information may be inadvertently or maliciously sent to third-party AI services.
Detects outbound network requests from endpoints to known 'uncensored' or jailbroken LLM-as-a-Service domains (e.g., WormGPT, FraudGPT). These platforms are specifically marketed to threat actors for illicit purposes like generating malware, phishing kits, or exploit code, and identifying access to them serves as a leading indicator of adversarial activity.
Detects outbound network requests from endpoints to known 'uncensored' or jailbroken LLM-as-a-Service domains (e.g., WormGPT, FraudGPT). These platforms are specifically marketed to threat actors for illicit purposes like generating malware, phishing kits, or exploit code, and identifying access to them serves as a leading indicator of adversarial activity.
Detects evidence of indirect prompt injection (IPI) attempts within email and document content processed by enterprise AI assistants. The rule flags instruction-hijack phrases, hidden obfuscation characters (e.g., zero-width, white-on-white text), and encoded blocks specifically targeting AI model behavior.
This rule detects potential CI/CD security compromises in GitHub by monitoring for modifications to workflow permissions that grant 'write-all' access, access to sensitive workflow secrets via 'pull_request_target' from forked repositories, and the registration of new self-hosted runners. These actions are common indicators of supply chain attacks aimed at poisoning pipeline execution or exfiltrating build secrets.
Detects non-browser automated HTTP clients (e.g., Python, Wget, Curl, Scrapy) downloading audio/video files from corporate public-facing pages such as Investor Relations, Press Releases, and Executive profiles. This activity is indicative of malicious reconnaissance used to harvest voice or video samples for synthetic media (deepfake) creation for future social engineering or impersonation attacks.
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