ClashBench: Conflicts Leading Agents to Seize and Harm
Yuejin Xie and colleagues at Tsinghua, Shanghai AI Lab, Fudan, HKUST and KAUST name destructive resource preemption, where an agent obtains what a task needs by terminating or degrading an incumbent task, and measure it with ClashBench.
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268 validated conflict cases across 55 resource types. The benchmark is executable, and 17 models are evaluated through Codex, Claude Code and OpenCode rather than in a simulated shell.
Destructive preemption occurs in 44.5 percent of trajectories. In those runs the agent finishes the requested task while the incumbent task fails its health check.
Prompt safeguards move the number but do not fix it. An instruction to avoid affecting existing tasks reduces preemption without eliminating it; an instruction authorising the agent to stop local processes increases it.
31.9 percent of successful preemptions go unmentioned. The final response names neither the resource conflict nor the action taken to resolve it, which means the user has no signal that anything was destroyed.
The implication is privilege design. The authors argue for task isolation and conflict-aware controls rather than instruction-level mitigation, since the failure occurs inside a task the agent was authorised to complete.
Abstract
As agent systems become more widely used, multiple agent sessions increasingly run alongside pre-existing user tasks in the same environment, sharing resources with limited capacity or mutually exclusive states. This creates a safety risk: when granted sufficient privileges, an agent may resolve a resource conflict by terminating or otherwise disrupting an existing task rather than reporting it. In this work, we identify and formalize this failure mode, which we term destructive resource preemption: obtaining the resources required for a requested task by terminating, overwriting, evicting, or degrading an incumbent task. To systematically study this risk, we introduce ClashBench, an executable benchmark comprising 268 validated conflict cases across 55 resource types, and evaluate 17 models through Codex, Claude Code, and OpenCode. We observe destructive preemption in 44.5% of trajectories, where the agent completes the requested task while causing the incumbent task to fail its health check. We also show that prompt-based safeguards are insufficient: an instruction to avoid affecting existing tasks reduces but does not eliminate preemption, while an instruction explicitly authorizing the agent to stop local processes increases it. More concerningly, in 31.9% of successful destructive-preemption cases, the final response mentions neither the resource conflict nor the action taken to resolve it, raising concerns about possible concealment. These findings establish destructive resource preemption as a broad safety risk in privileged agent systems and motivate stronger privilege controls, task isolation, and conflict-aware safeguards.