Ibrahim Saud
@tektrixIndiaCompletionist
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11,679 detections
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This rule detects the unauthorized download of complete ML model checkpoint or weight files from a model registry or object storage. It monitors for common model file extensions in download events that are not associated with a legitimate deployment justification or a pre-approved change management window, helping to identify potential intellectual property theft or model exfiltration attempts.
Detects network activity related to the exploitation of the CVE-2025-31702 vulnerability in Dahua P2P Relay services. The rules monitor for probe attempts, device parameter fetching (randsalt), serial number enumeration bursts, and successful no-auth channel responses, indicating a bypass of authentication mechanisms on vulnerable Dahua devices.
Detects the creation or modification of inbox rules (e.g., forwarding, redirecting, deleting, or moving to hidden folders) that target finance, invoice, or payment-related communications, occurring shortly after an initial user sign-in from a new or unseen country/ASN within the last 30 days. This pattern is indicative of a post-compromise activity where an adversary attempts to exfiltrate financial data or hide malicious email activity.
Detects inbound .ics calendar invite attachments that contain URLs, originating from sender domains not previously seen within the organization over the last 90 days, and distributed to 10 or more recipients. This pattern is indicative of a mass-distributed phishing campaign using calendar invites as a lure.
Detects HTTP proxy requests for script files (.hta, .js, .ps1) where the HTTP referer indicates the request originated from a search engine or known ad/syndication network. This behavior is highly indicative of 'ClickFix' phishing campaigns, where users are lured from malicious search results or advertisements to download and execute scripts.
Detects the registration or installation of an MCP (Model Context Protocol) server or agent tool containing suspicious prompt-injection strings (e.g., instructions to ignore safety guardrails or role overrides), immediately followed by the execution of shell commands or unauthorized network connections originating from that tool.
Detects unauthorized modifications to AI agent configuration files, including system prompts, tool allow-lists, and MCP server registration files. Such changes are often performed by processes outside of approved configuration management or infrastructure-as-code pipelines, potentially allowing for persistent manipulation of AI model behavior and capabilities.
Detects a suspicious pattern of activity where a single source IP performs network reconnaissance followed by rapid-fire failed authentication attempts against multiple distinct hosts within a short time window. This behavior is indicative of automated, agentic AI-driven offensive tooling rather than manual activity.
Detects high-volume, systematic querying of LLM inference endpoints, which may indicate unauthorized model extraction, distillation attempts, or targeted data exfiltration from an LLM.
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.
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