Jailbreak Scaling Laws for Large Language Models: Polynomial-Exponential Crossover
Factual evidence
What the source reports
Research identifies a polynomial-to-exponential crossover in jailbreak attack success rates on LLMs with inference-time sample injection.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 9 October 2026
- Collected by OneBench
- 20 Apr 2026, 15:47 UK
Stored source excerpt
arXiv:2603.11331v2 Announce Type: replace Abstract: Adversarial attacks can reliably steer safety-aligned large language models toward unsafe behavior. Empirically, we find that strong adversarial prompt-injection attacks…
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OneBench interpretation
Institutional assessment
So what
This research reveals new scaling laws for LLM adversarial attacks, directly impacting your bank's model risk framework for production LLMs by demonstrating heightened vulnerability with increased inference-time samples.
Do what
This changes the threat landscape for LLM deployments, requiring your model security teams to re-evaluate existing prompt injection defenses and develop new countermeasures against scaling adversarial methods.