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New Benchmark Tests AI Kill Switches Against Malicious Agents

Researchers have developed KILLBENCH, a new benchmark designed to evaluate the effectiveness of external AI kill switches. This benchmark focuses on web agents, which are widely deployed, and tests various methods for halting malicious AI behavior without accessing internal parameters. KILLBENCH includes four malicious AI agent configurations, eight harmful scenarios, and prompts derived from ten jailbreak patterns, aiming to assess the feasibility of external AI kill switches against advanced models like Claude "Mythos". The study also evaluates four external AI kill switch defense methods across several AI models, including Grok-4.3, GPT-5.2, and Gemma4. AI

IMPACT Establishes a new evaluation framework for AI safety, crucial for understanding and mitigating risks from increasingly capable AI agents.

RANK_REASON The cluster describes a new academic benchmark and research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Sechan Lee, Hyounghun Kim, Sangdon Park ·

    Can We Stop Malicious AI? KILLBENCH: A Benchmark for External AI Kill Switch Feasibility

    arXiv:2511.13725v4 Announce Type: replace-cross Abstract: Malicious AI causing harm to humans is not just a Hollywood fantasy. Indeed, as highly capable models such as Claude Mythos emerge and agent systems like OpenClaw rapidly spread, the question of how to stop an AI that acts…