arXiv Artificial Intelligence

SHE: Trajectory-driven Safety Harness Evolution for LLM Agents

2 hours ago 11.08.2026 07:00 👁 1 views
SHE: Trajectory-driven Safety Harness Evolution for LLM Agents

Quick summary

arXiv:2608.09885v1 Announce Type: new Abstract: The safety of large language model (LLM) agents depends not only on model weights but also on the agent harness that manages context, memory, tools, permissions, and runtime control. Existing safety mechanisms often treat the harness as a fixed deployment artifact, limiting their ability to evolve with emerging risks. Moreover, coupled functions across harness components obscure safety responsibility attribution, making localized evolution difficult. We propose Safety Harness Evolution (SHE), a framework that learns evolving safe boundaries from

Key takeaways

  • arXiv:2608.09885v1 Announce Type: new Abstract: The safety of large language model (LLM) agents depends not only on model weights but also on the agent harness that manages context, memory, tools, permissions, and runtime control.
  • Existing safety mechanisms often treat the harness as a fixed deployment artifact, limiting their ability to evolve with emerging risks.
  • Moreover, coupled functions across harness components obscure safety responsibility attribution, making localized evolution difficult.

Why it matters

This development is a reminder to test misuse and data-leak scenarios alongside speed and quality. Trust should come from testable controls and clear failure reporting, not protection claims alone.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗