Ibrahim Saud
@tektrixIndiaCompletionist
2 followers18,458 downloads7,077 copies47 likes38,463 views
11,679 detections
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10
Detects outbound HTTPS connections to major generative AI inference endpoints originating from non-browser, non-development tool processes at regular intervals. This activity is indicative of beaconing behavior, where malware (specifically families like LAMEHUG) leverages LLM APIs for dynamic command generation, payload obfuscation, or C2 instruction retrieval, bypassing traditional security controls.
Detects anomalous remote access patterns on a single endpoint, specifically involving AnyDesk or TeamViewer sessions originating from multiple distinct geographical locations combined with usage by multiple distinct user accounts. This pattern is consistent with DPRK IT worker fraud (Wagemole) involving shared laptop farms.
Detects coordinated activity patterns across multiple distinct tenant environments within a one-hour window. The rule looks for shared infrastructure (C2 domains), malicious tool hashes, or specific agentic-CLI command line signatures (e.g., related to orchestration, reconnaissance, or exfiltration) that suggest an AI-orchestrated actor or automated attack campaign affecting multiple organizations simultaneously.
This rule detects potential prompt injection attacks against internal AI-powered assistants, agents, or LLM gateways. It monitors network HTTP requests for common patterns used to override system instructions, bypass safety filters, or force the model to adopt a privileged, unauthorized persona (e.g., administrator or debug mode). These techniques are often used in social engineering to manipulate the AI's output or security posture.
Detects anomalous child process spawning from common web browsers or productivity applications (e.g., Office, Acrobat) on hosts that have recently communicated with known LLM or code-generation APIs. This behavioral correlation is intended to identify the potential execution of AI-generated exploit code.
Detects HTTP requests to popular LLM API services (OpenAI, Anthropic, Google, Cohere) containing common jailbreak or role-play patterns. These patterns attempt to bypass safety guardrails by using techniques such as persona-switching (e.g., 'DAN'), hypothetical scenario framing, or unauthorized security testing pretexts.
Detects anomalous account recovery or MFA reset activity performed by a single help-desk user account. A high volume of reset actions across numerous distinct users within a short duration is a key behavioral indicator of potential help-desk account compromise or insider abuse involving unauthorized account takeovers.
Detects anomalous large-volume file download or export activity from cloud repositories (SharePoint, OneDrive, or Salesforce) occurring shortly after a help-desk-mediated credential or MFA reset for the same user. This behavior is indicative of account takeover, specifically vishing-to-exfiltration patterns used by threat actors.
Detects suspicious account management activities, such as password resets or MFA re-enrollment, performed by an operator on a high-privileged or administrator account outside of typical business hours (06:00-20:00). This behavior is often indicative of help-desk impersonation or credential takeover attempts targeting Identity Provider (IdP) administrative roles.
Detects MFA or credential reset activities on high-value or privileged accounts that lack a corresponding out-of-band verification signal, such as manager approval or identity proofing, within a one-hour window. This behavior is indicative of social engineering attempts where attackers manipulate help-desk procedures to bypass identity protections.
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