Dense-Armor
A Python runtime safety protection library for robots and AI systems, integrating four core capabilities: online learning, anomaly and drift detection, uncertainty estimation, and runtime protection. It can be installed via pip.
Timeline (last 90 days)
Dense-Armor is a Python library that provides runtime safety protection for robots and AI systems, integrating four core capabilities: online learning, anomaly and drift detection, uncertainty estimation, and runtime protection against unsafe inputs and outputs.
Dense-Armor can be installed via pip install dense-armor, and its source code is hosted on GitHub.
Dense-Armor's runtime protection can actively block dangerous behaviors, such as intercepting the output of LLM prompt injection attacks, or executing an emergency stop by directly overriding control commands for robotic systems.
Dense-Armor is currently still at an early stage, and the author has not yet published detailed performance benchmarks, the types of models supported, or comparison data with other safety frameworks.
Dense-Armor's potential application scenarios include industrial automation (as a safety layer between PLCs and upper-level AI systems), service robots (monitoring anomalies in human-robot interaction), and AI content generation (output filtering and risk control).
All Facts (4)
Dense-Armor is a Python library that provides runtime safety protection for robots and AI systems, integrating four core capabilities: online learning, anomaly and drift detection, uncertainty estimation, and runtime protection against unsafe inputs and outputs.
50%UnverifiedDense-Armor can be installed via pip install dense-armor, and its source code is hosted on GitHub.
50%UnverifiedDense-Armor's runtime protection can actively block dangerous behaviors, such as intercepting the output of LLM prompt injection attacks, or executing an emergency stop by directly overriding control commands for robotic systems.
50%UnverifiedDense-Armor's potential application scenarios include industrial automation (as a safety layer between PLCs and upper-level AI systems), service robots (monitoring anomalies in human-robot interaction), and AI content generation (output filtering and risk control).
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