PPE Kit Detection Agents — agentic threat model

Not certain from the listing — likely uses specialized computer vision models (e.g., CNNs, YOLO) rather than generative LLMs. Key threats include physical adversarial attacks (e.g., wearing patterned clothing that tricks the model into detecting a helmet or harness) and model evasion.

Not certain from the listing — processes real-time video streams and generates compliance reports. Threats include unauthorized access to raw video feeds, data exfiltration of sensitive workplace footage, and poisoning of the training/fine-tuning datasets with mislabeled PPE images.

Not certain from the listing — orchestration likely relies on a traditional video processing pipeline rather than an LLM agent framework. Threats include insecure integration with alerting mechanisms and logic bypasses in the thresholding/alert-triggering code.

Not certain from the listing — likely deployed on edge devices near CCTV cameras or in a high-bandwidth cloud environment. Threats include compromise of edge devices, unauthorized access to RTSP/video streams, and lack of network isolation between the camera network and the corporate network.

Not certain from the listing — requires continuous monitoring for model drift due to environmental changes (e.g., lighting, camera angles, new PPE designs). Gaps in observability could lead to silent failures where safety violations go undetected.

Not certain from the listing — continuous video surveillance of employees raises significant privacy, labor union, and regulatory compliance concerns (e.g., GDPR, biometric data laws). Strong access controls, data retention policies, and audit logs are critical but not detailed.

Not certain from the listing — integrates with existing security and monitoring systems. Threats include downstream cascading failures if the agent floods the SIEM/alerting system with false positives, or if a compromised agent sends spoofed 'all-clear' signals during actual safety breaches.