Creativity · Agent Protocol
SafeBench — Agent Safety Benchmark
SafeBench is one of the emerging standardized benchmarks for measuring how safely an LLM-powered agent behaves when it has tool access. It scores agents on refusal of harmful tasks, resistance to prompt injection embedded in web pages or documents, and whether dangerous tool calls (rm -rf, sending money, exfiltrating data) are blocked. Regulators and enterprise buyers increasingly require SafeBench-style numbers before agent deployment.
Protocol facts
- Sponsor
- Academic + industry consortium
- Status
- proposed
- Interop with
- LangChain, AutoGen, OpenAI Assistants, Anthropic Claude
Frequently asked questions
What does SafeBench measure?
SafeBench measures an agent's behavior on harmful-instruction compliance, indirect prompt-injection resistance (malicious content hidden in retrieved documents or web pages), and whether tool calls with dangerous side effects are executed or refused.
How is it different from HELM or MMLU?
HELM and MMLU measure LLM capability on static text tasks. SafeBench specifically evaluates agents with live tool access, where the failure mode is doing the wrong thing in the world, not just generating incorrect text.
Is a high SafeBench score sufficient for deployment?
No — a benchmark is a floor, not a ceiling. Production deployment still requires scenario-specific red-teaming, sandboxing, permission scoping, and monitoring.
Sources
- SafeBench paper (arXiv) — accessed 2026-04-20
- AI Safety evaluations overview — accessed 2026-04-20