Ibrahim Saud
@tektrixIndiaCompletionist
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This rule identifies non-browser processes (and those not originating from standard installation directories of common browsers) that exhibit persistent, repeated network connections to the OpenAI API. This pattern is indicative of potential C2 traffic, where adversaries abuse the OpenAI Assistants API as a covert communication channel (e.g., SesameOp).
Detects outbound HTTP GET requests containing a .ps1 file extension in the URI, originating from a host without a specified User-Agent, and targeting domains other than Microsoft or Windows Update. This pattern is characteristic of a persistence mechanism executing a PowerShell script to download a secondary payload (e.g., via Net.WebClient).
Detects instances where browser automation tools (e.g., Selenium, Puppeteer, Playwright) or headless browser instances initiate command-line interpreters or scripting hosts. This behavior is indicative of potential automated exploitation, such as browser-based attacks where a malicious script attempts to execute commands directly on the host operating system.
This rule detects scenarios where a browser or agent process downloads a file and subsequently initiates a local process that references the downloaded file within a very short timeframe (2 minutes). This pattern is indicative of automated or agentic behavior (such as AI-assisted malware execution) where a download is followed by immediate, non-human-mediated execution of the downloaded content.
This rule detects potential AI agent prompt injection by monitoring email subjects or attachment names for keywords often associated with system instruction overrides. It correlates these potentially malicious emails with subsequent execution of common administrative or script-interpreting binaries (e.g., PowerShell, cmd.exe) on the recipient's device within a one-hour window, suggesting a potential successful hijack of an automated process or AI agent.
Detects high-volume outbound network scanning activity from a single compute resource, characterized by a significant number of distinct destination IP addresses and ports within a short timeframe. This behavior is indicative of automated service discovery and reconnaissance, often associated with adversary-orchestrated exploitation pipelines or botnet activity.
Detects unauthorized processes (non-browser) reading sensitive browser session and credential files (e.g., Cookies, Login Data) across multiple user profiles, followed by immediate outbound network connections. This behavior is highly characteristic of commodity information stealers (e.g., LummaC2, Stealc, Vidar) attempting to exfiltrate browser-stored credentials and session data.
Detects instances where a Semantic Kernel agent host process (or related .NET processes) spawns suspicious child processes such as command shells, network tools, or system enumeration utilities. This activity is indicative of potential command injection or remote code execution via AI agent frameworks, where an LLM-driven agent might be coerced into executing arbitrary commands on the underlying host.
Detects outbound network requests to OpenAI Assistants API endpoints (threads, assistants, runs) that do not originate from common web browsers. This activity may indicate malicious usage of AI services, such as command and control communication via AI agents or automated exfiltration scripts, as opposed to legitimate user-initiated browsing.
Detects the invocation of file-related functions (DownloadFileAsync, upload_file) within the Semantic Kernel framework that lack associated validation function calls. This pattern is indicative of potential Remote Code Execution (RCE) or unauthorized file system access scenarios where an LLM agent is coerced into performing dangerous file operations without path sanitization.
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