BELLS-O: Evaluating the Operational Trade-offs of LLM Supervision Systems
LLM supervision systems, namely input/output moderation filters and jailbreak detectors, are the primary safeguard against misuse in deployed AI applications, yet existing benchmarks are often vendor-biased, omit cost and latency, and rarely compare specialized guardrails against repurposed generalist LLMs. We present BELLS-O (Benchmark for the Evaluation of LLM Supervision Systems, Operational), the first independent operational benchmark of LLM supervision systems. BELLS-O evaluates 28 systems from 17 providers: every major specialized guardrail (e.g., LlamaGuard-4, ShieldGemma-2, Lakera Guard) and frontier generalists repurposed as supervisors (e.g., GPT-5.4, Claude Sonnet 4.6, Grok-4....
| Authors | Leonhard Waibl, Felix Michalak, Hadrien Mariaccia |
|---|---|
| Primary category | cs.CR |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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