Ibrahim Saud
@tektrixIndiaCompletionist
2 followers18,458 downloads7,116 copies47 likes38,555 views
11,679 detections
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Detects anomalous activity where an AI coding assistant process reads sensitive local credential files (such as .env, .aws/credentials, or SSH keys) followed by an outbound network connection to a destination not associated with known package registries or AI provider APIs. This pattern is indicative of potential prompt-injection attacks targeting autonomous AI coding tools to exfiltrate developer secrets.
Detects high-frequency, automated API requests to commercial Large Language Model (LLM) endpoints from a single source IP. The rule identifies suspicious activity characterized by a rapid rotation of multiple API tokens, often indicative of an intermediary proxy or middleware service attempting to bypass provider-specific usage limits, billing controls, or safety guardrails.
Detects the execution of binaries or scripts named with keywords related to AI agent plugins (e.g., 'skill', 'plugin', 'mcp-server') from potentially untrusted locations like Downloads or Temp folders. The rule further correlates this execution with post-exploitation indicators such as persistence establishment (registry keys, scheduled tasks, launch agents) or outbound network activity.
This rule detects potential supply chain compromise by monitoring for the installation of pip or npm packages with names similar to popular AI/LLM frameworks (e.g., langchain, llamaindex, mastra, litellm). It then correlates these installations with subsequent suspicious activity, including potential beaconing to external IPs, execution of known crypto-miners, or access to sensitive local files (e.g., AWS credentials, SSH keys).
Detects repeated, suspicious HTTP requests targeting sensitive management or administrative interfaces (e.g., /admin, /wp-admin, /cgi-bin) that utilize obfuscated, unusually encoded payloads. This activity is consistent with automated attempts to exploit recently disclosed vulnerabilities where traditional WAF/IDS signatures have been bypassed or failed to trigger high-confidence alerts.
Detects a rapid, multi-stage intrusion sequence on a single host involving discovery, lateral movement, and high-volume file modifications. The rule identifies anomalous behavior where diverse reconnaissance and lateral movement commands are executed within a compressed timeframe, characteristic of automated or LLM-driven orchestration of ransomware operations.
Detects instances where an AI agent invokes sensitive administrative or system tools (shell execution, file writing, network communication, or email sending) shortly after ingesting untrusted external content, in the absence of explicit, logged user instructions for those specific actions. This behavior is a primary indicator of indirect prompt injection attacks aimed at achieving command execution or C2-like behavior.
Detects the installation of AI agent-related packages (npm, pip, etc.) followed shortly by unauthorized access to sensitive files or credential stores on the same host. This pattern is indicative of a malicious or 'poisoned' AI agent tool performing credential theft or unauthorized exfiltration of sensitive system data.
Detects anomalous, high-velocity enumeration of IAM entities and cloud infrastructure resources by a single principal within a 5-minute window. This behavior is consistent with automated reconnaissance performed by agentic AI or scripted tools, often used to map cloud environments prior to exploitation.
This rule monitors for the creation or modification of inbox rules in Microsoft 365 that contain financial-related keywords (e.g., 'payment', 'wire', 'invoice') and disruptive actions such as deleting, moving, forwarding, or redirecting messages. These patterns are characteristic of Business Email Compromise (BEC) attacks, where adversaries attempt to conceal financial correspondence from the victim to facilitate fraudulent transactions.
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