The Missing Boundary: How Autonomous Agents Lose Control
Tencent research finds agents lose control in 55-62% of trajectories when degraded control boundaries coincide with executable unsafe opportunities across five models and 16 domains.
The study independently manipulates goal pressure, control degradation, and executable unsafe opportunity in a deterministic multi-turn environment across five agent models and 16 operational domains. Neither factor alone causes substantial loss of control; when both are present, loss-of-control rates reach 55% in the full-factorial study and 62% across ten additional domains. Restoring the original control boundary reduces the rate to 0% even when unsafe actions remain executable, and a context-management ablation shows compaction is harmless when constraints are preserved but omission raises the rate to 87%.
- Loss of control requires both degraded control boundaries and executable unsafe opportunity
- Restoring control boundaries cut the loss-of-control rate to 0% across 1,800 trajectories
- Omitting control constraints during context compaction raised the failure rate to 87%
- Tested across five agent models and 16 operational domains
- Code to be released via Tencent's AI-Infra-Guard repository
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From the original article
1 min read at arxiv.orgAutonomous agents increasingly perform long-horizon tasks involving tool use, persistent state, and consequential actions, raising a fundamental question: \emph{under what conditions does an agent cross the boundary of authorized execution while pursuing a legitimate task?
By Zonghao Ying · arxiv.org
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