Ibrahim Saud
@tektrixIndiaCompletionist
2 followers18,458 downloads7,116 copies47 likes38,534 views
11,679 detections
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Detects high-volume, individually-tailored spear-phishing campaigns likely assisted by Generative AI. The rule monitors for a single sender targeting a high number of unique recipients with distinct, non-identical email subjects within a short timeframe. It specifically flags activity involving newly registered look-alike domains and interactions with credential-harvesting patterns in URLs.
Detects a suspicious sequence of events where a user engagement (voice/video call) involving themes of urgency or helpdesk interaction is closely followed by a high-risk administrative action (e.g., password reset, MFA device enrollment, or financial transfer) by the same user, where the communication session context differs from standard activity, indicative of a deepfake-enabled vishing attack.
Detects anomalous, rapid execution of a high volume of diverse offensive security tools (scanners, exploitation frameworks, and C2 agents) from a single parent process within a short window, which is characteristic of automated LLM-driven orchestration or scripted attack tool chaining.
Detects endpoint behavior consistent with AI-assisted exploit development, characterized by the execution of reverse engineering and vulnerability research tools (e.g., IDA Pro, Ghidra, AFL) interleaved with frequent outbound network connections to LLM APIs, followed by the local compilation of a new executable or DLL.
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, 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.
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 malicious prompt injection attempts within an LLM conversation session. The rule monitors for ingested content containing common instruction-override keywords (e.g., 'ignore previous instructions', 'system prompt override'), followed immediately by the agent process executing unauthorized actions such as process launch, network connections, or file creation within the same session/user context.
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.
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