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BELLS-O: Evaluating the Operational Trade-offs of LLM Supervision Systems

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arXiv:2606. 20668v1 Announce Type: new Abstract: 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.

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Authors: Leonhard Waibl, Felix Michalak, Hadrien Mariaccia
Released: 2026-06-23 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because BELLS-O: Evaluating the Operational Trade-offs of LLM Supervision Systems overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)". Matching terms: phrase, under, eval, rate, compare, control. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2606.20668

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