defense 2026

No More, No Less: Least-Privilege Language Models

Paulius Rauba 1, Dominykas Seputis 2,3, Patrikas Vanagas 4, Mihaela van der Schaar 1

0 citations · 66 references · arXiv

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Published on arXiv

2601.23157

Prompt Injection

OWASP LLM Top 10 — LLM01

Key Finding

Rank-indexed internal interventions enable selective suppression of targeted dangerous capabilities with limited collateral degradation, establishing a new deployment paradigm beyond output-level control.

Nested Least-Privilege Networks

Novel technique introduced


Least privilege is a core security principle: grant each request only the minimum access needed to achieve its goal. Deployed language models almost never follow it, instead being exposed through a single API endpoint that serves all users and requests. This gap exists not because least privilege would be unhelpful; deployments would benefit greatly from reducing unnecessary capability exposure. The real obstacle is definitional and mechanistic: what does "access" mean inside a language model, and how can we enforce it without retraining or deploying multiple models? We take inspiration from least privilege in computer systems and define a class of models called least-privilege language models, where privilege is reachable internal computation during the forward pass. In this view, lowering privilege literally shrinks the model's accessible function class, as opposed to denying access via learned policies. We formalize deployment-time control as a monitor-allocator-enforcer stack, separating (i) request-time signals, (ii) a decision rule that allocates privilege, and (iii) an inference-time mechanism that selects privilege. We then propose Nested Least-Privilege Networks, a shape-preserving, rank-indexed intervention that provides a smooth, reversible control knob. We show that this knob yields policy-usable privilege-utility frontiers and enables selective suppression of targeted capabilities with limited collateral degradation across various policies. Most importantly, we argue for a new deployment paradigm that challenges the premise that language models can only be controlled at the output level.


Key Contributions

  • Formal definition of least-privilege language models where 'privilege' is reachable internal computation during the forward pass, enabling structural capability restriction rather than output filtering
  • Monitor-allocator-enforcer stack that separates request-time signals, privilege allocation policy, and inference-time enforcement mechanisms
  • Nested Least-Privilege Networks: shape-preserving, rank-indexed weight interventions that provide a smooth, reversible control knob yielding privilege-utility frontiers with selective capability suppression

🛡️ Threat Analysis


Details

Domains
nlp
Model Types
llmtransformer
Threat Tags
inference_timegrey_box
Applications
llm deploymentcapability gatingharmful information prevention