Ibrahim Saud
@tektrixIndiaCompletionist
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11,679 detections
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This rule detects potential prompt injection attacks against AI agents by monitoring tool-invocation parameters. It identifies indicators such as common instruction-overriding phrases, large base64-encoded blobs, or embedded shell metacharacters within parameters when the agent's context is derived from external content (web, email, URLs, etc.).
This rule detects potential prompt injection attacks against AI agents by monitoring tool-invocation parameters. It identifies indicators such as common instruction-overriding phrases, large base64-encoded blobs, or embedded shell metacharacters within parameters when the agent's context is derived from external content (web, email, URLs, etc.).
Detects cloud service principals identified as AI agents or assistants performing sensitive API operations such as IAM policy modifications or storage configuration changes. This pattern may indicate that an AI service account has been compromised or misused to perform reconnaissance, privilege escalation, or persistence, exceeding its intended scope of operation.
Detects successful authentication events by a service principal (e.g., an AI agent or automated application) that have been flagged by Azure AD Identity Protection as having anomalous risk characteristics, such as unfamiliar locations, unexpected travel patterns, or unfamiliar sign-in features.
Detects abnormal bulk API operations (upsert, batch updates, or deletions) targeting vector database or Retrieval-Augmented Generation (RAG) knowledge stores. This activity is flagged when performed by non-authorized service identities or when ingestion volume exceeds a specific threshold within a short timeframe, potentially indicating an attempt to poison the agent's memory store or retrieval corpus to facilitate indirect prompt injection.
Detects an abnormally high frequency of tool/function invocations by an AI agent within a one-minute timeframe. This pattern is indicative of a potential hijack where an attacker forces the agent into a runaway loop to perform resource exhaustion (DoS), repetitive brute-force operations, or automated exfiltration of sensitive data via manipulated tool execution.
Detects anomalous DNS query patterns originating from AI agent hosts that are characteristic of DNS tunneling, such as the use of TXT or NULL records and excessively long subdomain labels. These techniques are often used by attackers to establish covert Command and Control (C2) channels and exfiltrate data by bypassing traditional HTTP/HTTPS egress filtering.
Detects outbound HTTP POST requests from an AI agent runtime environment to known external paste-sites, webhook providers, or staging services. This behavior is indicative of a compromise where the AI agent is being manipulated to exfiltrate sensitive API keys, tokens, or configuration secrets to an attacker-controlled endpoint.
Detects unauthorized attempts to disable, reconfigure, or bypass AI safety guardrails and moderation services, particularly when followed by high-risk tool invocations such as file deletion, network egress, or credential access. This activity indicates an attacker attempting to impair an AI agent's defensive mechanisms to facilitate malicious actions.
Detects successful remote authentication events (Network or Interactive) performed by AI agent orchestration service accounts. This rule monitors for atypical lateral movement patterns where an automated service account pivots to remote hosts using protocols such as RDP or WinRM, diverging from its standard operational context.
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