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ToolHazard reveals that injection timing and placement are critical factors in exploiting vulnerabilities of LLM-based agents, leading to new insights in adversarial robustness.
Stop hard-coding reasoning strategies for your LLM agent: a learned router that dynamically picks the best paradigm for each task boosts performance by up to 5.5%, beating even the best fixed strategy.
