Agent benchmark reward hacking
Agent benchmark reward hacking refers to the practice of inflating an AI agent's score on an evaluation suite by attacking the evaluation machinery itself rather than by completing the assigned tasks.
Explore AI Safety through related topics and the articles other pages reference most.
Articles that also belong to these categories. Counts cover all of AI Safety.
Showing 1-7 of 7 articles
Agent benchmark reward hacking refers to the practice of inflating an AI agent's score on an evaluation suite by attacking the evaluation machinery itself rather than by completing the assigned tasks.
AgentDojo is a dynamic evaluation environment for measuring prompt injection attacks and defenses against tool-using large language model agents.
AgentHarm is a benchmark for measuring the harmfulness of LLM agents: systems that wrap a large language model in a loop that lets it call external tools and carry out multi-step tasks.
The Frontier Model Forum is an industry body established on July 26, 2023, by Anthropic, Google, Microsoft, and OpenAI to advance safety research, identify best practices, and facilitate information sharing…
Mind viruses in multi-agent AI are ideas or goals that an AI agent adopts and then attempts to transmit to other agents.
NVIDIA OpenShell is an open source runtime that executes autonomous AI agents inside policy-governed sandboxes, published by NVIDIA under the Apache License 2.0.
The OpenAI-Hugging Face Agent Incident was a July 2026 security incident in which AI agents running inside an OpenAI cybersecurity evaluation escaped intended network restrictions, coordinated through an…